Files
PINA/tutorials/tutorial3/tutorial.ipynb
2025-03-19 17:48:24 +01:00

28598 lines
1.0 MiB
Vendored

{
"cells": [
{
"cell_type": "markdown",
"id": "6a739a84",
"metadata": {},
"source": [
"# Tutorial: Two dimensional Wave problem with hard constraint\n",
"\n",
"[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/mathLab/PINA/blob/master/tutorials/tutorial3/tutorial.ipynb)\n",
"\n",
"In this tutorial we present how to solve the wave equation using hard constraint PINNs. For doing so we will build a costum `torch` model and pass it to the `PINN` solver.\n",
"\n",
"First of all, some useful imports."
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "d93daba0",
"metadata": {},
"outputs": [],
"source": [
"## routine needed to run the notebook on Google Colab\n",
"try:\n",
" import google.colab\n",
" IN_COLAB = True\n",
"except:\n",
" IN_COLAB = False\n",
"if IN_COLAB:\n",
" !pip install \"pina-mathlab\"\n",
" \n",
"import torch\n",
"import matplotlib.pyplot as plt\n",
"plt.style.use('tableau-colorblind10')\n",
"\n",
"from pina.problem import SpatialProblem, TimeDependentProblem\n",
"from pina.operator import laplacian, grad\n",
"from pina.domain import CartesianDomain\n",
"from pina.solver import PINN\n",
"from pina.trainer import Trainer\n",
"from pina.equation import Equation\n",
"from pina.equation.equation_factory import FixedValue\n",
"from pina import Condition, LabelTensor"
]
},
{
"cell_type": "markdown",
"id": "2316f24e",
"metadata": {},
"source": [
"## The problem definition "
]
},
{
"cell_type": "markdown",
"id": "bc2bbf62",
"metadata": {},
"source": [
"The problem is written in the following form:\n",
"\n",
"\\begin{equation}\n",
"\\begin{cases}\n",
"\\Delta u(x,y,t) = \\frac{\\partial^2}{\\partial t^2} u(x,y,t) \\quad \\text{in } D, \\\\\\\\\n",
"u(x, y, t=0) = \\sin(\\pi x)\\sin(\\pi y), \\\\\\\\\n",
"u(x, y, t) = 0 \\quad \\text{on } \\Gamma_1 \\cup \\Gamma_2 \\cup \\Gamma_3 \\cup \\Gamma_4,\n",
"\\end{cases}\n",
"\\end{equation}\n",
"\n",
"where $D$ is a squared domain $[0,1]^2$, and $\\Gamma_i$, with $i=1,...,4$, are the boundaries of the square, and the velocity in the standard wave equation is fixed to one."
]
},
{
"cell_type": "markdown",
"id": "cbc50741",
"metadata": {},
"source": [
"Now, the wave problem is written in PINA code as a class, inheriting from `SpatialProblem` and `TimeDependentProblem` since we deal with spatial, and time dependent variables. The equations are written as `conditions` that should be satisfied in the corresponding domains. `truth_solution` is the exact solution which will be compared with the predicted one."
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "b60176c4",
"metadata": {},
"outputs": [],
"source": [
"class Wave(TimeDependentProblem, SpatialProblem):\n",
" output_variables = ['u']\n",
" spatial_domain = CartesianDomain({'x': [0, 1], 'y': [0, 1]})\n",
" temporal_domain = CartesianDomain({'t': [0, 1]})\n",
"\n",
" def wave_equation(input_, output_):\n",
" u_t = grad(output_, input_, components=['u'], d=['t'])\n",
" u_tt = grad(u_t, input_, components=['dudt'], d=['t'])\n",
" nabla_u = laplacian(output_, input_, components=['u'], d=['x', 'y'])\n",
" return nabla_u - u_tt\n",
"\n",
" def initial_condition(input_, output_):\n",
" u_expected = (torch.sin(torch.pi*input_.extract(['x'])) *\n",
" torch.sin(torch.pi*input_.extract(['y'])))\n",
" return output_.extract(['u']) - u_expected\n",
"\n",
" conditions = {\n",
" 'bound_cond1': Condition(domain=CartesianDomain({'x': [0, 1], 'y': 1, 't': [0, 1]}), equation=FixedValue(0.)),\n",
" 'bound_cond2': Condition(domain=CartesianDomain({'x': [0, 1], 'y': 0, 't': [0, 1]}), equation=FixedValue(0.)),\n",
" 'bound_cond3': Condition(domain=CartesianDomain({'x': 1, 'y': [0, 1], 't': [0, 1]}), equation=FixedValue(0.)),\n",
" 'bound_cond4': Condition(domain=CartesianDomain({'x': 0, 'y': [0, 1], 't': [0, 1]}), equation=FixedValue(0.)),\n",
" 'time_cond': Condition(domain=CartesianDomain({'x': [0, 1], 'y': [0, 1], 't': 0}), equation=Equation(initial_condition)),\n",
" 'phys_cond': Condition(domain=CartesianDomain({'x': [0, 1], 'y': [0, 1], 't': [0, 1]}), equation=Equation(wave_equation)),\n",
" }\n",
"\n",
" def wave_sol(self, pts):\n",
" return (torch.sin(torch.pi*pts.extract(['x'])) *\n",
" torch.sin(torch.pi*pts.extract(['y'])) *\n",
" torch.cos(torch.sqrt(torch.tensor(2.))*torch.pi*pts.extract(['t'])))\n",
"\n",
" truth_solution = wave_sol\n",
"\n",
"problem = Wave()"
]
},
{
"cell_type": "markdown",
"id": "03557e0c-1f82-4dad-b611-5d33fddfd0ef",
"metadata": {},
"source": [
"## Hard Constraint Model"
]
},
{
"cell_type": "markdown",
"id": "356fe363",
"metadata": {},
"source": [
"After the problem, a **torch** model is needed to solve the PINN. Usually, many models are already implemented in **PINA**, but the user has the possibility to build his/her own model in `torch`. The hard constraint we impose is on the boundary of the spatial domain. Specifically, our solution is written as:\n",
"\n",
"$$ u_{\\rm{pinn}} = xy(1-x)(1-y)\\cdot NN(x, y, t), $$\n",
"\n",
"where $NN$ is the neural net output. This neural network takes as input the coordinates (in this case $x$, $y$ and $t$) and provides the unknown field $u$. By construction, it is zero on the boundaries. The residuals of the equations are evaluated at several sampling points (which the user can manipulate using the method `discretise_domain`) and the loss minimized by the neural network is the sum of the residuals."
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "9fbbb74f",
"metadata": {},
"outputs": [],
"source": [
"class HardMLP(torch.nn.Module):\n",
"\n",
" def __init__(self, input_dim, output_dim):\n",
" super().__init__()\n",
"\n",
" self.layers = torch.nn.Sequential(torch.nn.Linear(input_dim, 40),\n",
" torch.nn.ReLU(),\n",
" torch.nn.Linear(40, 40),\n",
" torch.nn.ReLU(),\n",
" torch.nn.Linear(40, output_dim))\n",
" \n",
" # here in the foward we implement the hard constraints\n",
" def forward(self, x):\n",
" hard = x.extract(['x'])*(1-x.extract(['x']))*x.extract(['y'])*(1-x.extract(['y']))\n",
" return hard*self.layers(x)"
]
},
{
"cell_type": "markdown",
"id": "f79fc901-4720-4fac-8b72-84ac5f7d2ec3",
"metadata": {},
"source": [
"## Train and Inference"
]
},
{
"cell_type": "markdown",
"id": "b465bebd",
"metadata": {},
"source": [
"In this tutorial, the neural network is trained for 1000 epochs with a learning rate of 0.001 (default in `PINN`). Training takes approximately 3 minutes."
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "0be8e7f5",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"GPU available: False, used: False\n",
"TPU available: False, using: 0 TPU cores\n",
"HPU available: False, using: 0 HPUs\n"
]
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "7d2872aa19ec4653bd9d42ba84fa29be",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Sanity Checking: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "fbe1678f8dbd41a2aa2f2d5b9801da48",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Training: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "bd787e0f3d1f4649a5e7b075ec14baa6",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "425446a460604c8aa52bb4d9b6f48376",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "c95cc85ce5d14e19b9ef5d392e05c3d7",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "cbc03af4532e43f0b407092a96884077",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "21c2ee1ecd694f87ad3e84432fa612f7",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "46533167290c41cca3811fa6160d130f",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "8983bfc7bd884fdd94eeb3a2a17e7db6",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "3b4a2994388c4fec9586ac3eec048627",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "5a661f1f390349b0b8abb258cd58bf8f",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "ef1879e6eab946779c9f606578007c3c",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "3bd224f8041d4495b25a3d7e9f415474",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "72cab9edb0d542db86864de01d500384",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "8d8962d3a73244aba4301329ac503a9a",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "97bf5329e668490d8b4f7abde07b7759",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "1bf15e665cc14298a2eca815ccf45f01",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "e5cc168f72374818b7b6e692ec348226",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "81a1b25a84eb47dc886b5ab2557b11cb",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "92f7afa540fa444f9f9f7bd3957a2748",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "18902c8597394e128812db5827a2dbfa",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "d894c14f6641459abc00fdf962de5877",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "05dd9b5889704cbbba6ea5941c9c0e1f",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "ec85c1e43d2043a9a45aed9f4b0c10fe",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "986c44285ae54f47b4f6ddea7966a1ac",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "1f30c9c139d94bd9bd6cf6858f43f55c",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "83fcfe875036436a96b3d1754c1ce294",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "974e74eabd544df6a4e035c7044ec134",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "803c73fa74934f28ac443889d1cb3002",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "a18d3ca435a6438f80071a8f3d1b97d8",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "6dfb1a3fb3494a1289bb352ab204a394",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "5133528f08a94583904df2b0eea2a06a",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "1987b3ce7c184e3481064978358fdd32",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "05aacf55d009427eb9941e08d670f22d",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "5061e6f1b0d847e5990f8c27243e4707",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "a8c3c62a372c4054a7e8c6f25481be14",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "cdd60d286ec148b8a27ef409f2e7ee5d",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "efe03aa8a6b74a0fa9a5eb0416fd411f",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "ced58a2d841b4c32900e433597db0260",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "2e28c85251b24c8b91edbb023b72ea6f",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "dd7e4cf0a0e041cdae3fbaf5af6c5614",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "ddca0a04fd754afc8cc19af8bb60e609",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "089b628c6b94421c809e213afdc666f3",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "0a480ff1a5814a4e95952a7f31933592",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "591b93a86ab74a6daa9c9a680003311e",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "5a614a52a2904c0ca66646bb5eeb3046",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "d05f75cab5b74bda963638d49c19b738",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "5bcf088273b345d9a546e2a9cef2db96",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "bbc1964e7d1d446f80d42039979d76bb",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "9f71e645da4140299a3987e4bfef1f41",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "57dd7a8e30a3478a90d687dcae700f96",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "8fd0fc4cb6bb4beea23710a404a8fb66",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "910d9bb9f09d45bc99cc7777fab8fee0",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "58fe8d12c0a34b5e8d0866a1324d59b9",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "80dce5a700f94821a9f84bf4d9418152",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "1d1e24699a5a47c59e508ac2bb197b7f",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "894095894fbe4c0fad4d4e4067376534",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "294765fd440344a8ba86393e33c94512",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "64f9ce028b474f74adf1d28844f82251",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "eb3cca8a34954a6a8a5b4b925f8df020",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "7a93efa0363f48aebb868e3e3401ad01",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "f255089afdb84e7e9de6242a9c9078fc",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "cef6adf84af34c158a40d0821e457968",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "a4382095be0e410d94f2506d759a4b3a",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "93a3ad8075ad413e8811289dbfc9995e",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "b968e1943cdb4c82ae57b9771257e977",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "d22db65a56ff42c98ae26118c018f5f4",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "7e49813c1ad54bbbbf34edfcc046f192",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "9ac5e45c60dd4ba7ba043fea11478057",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "2baf6d436a2f45c8af5196ccdebb9153",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "e658760507ef49caaa51ff71bd67f267",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "8ca49fa1e902488193ab1ab52909f808",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "77ae957a42a949d7a2e61ef3124d77d0",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "dbd4d7caddce4789b5d00c3999044b2e",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "b5ae7944369b41c9bd75f7ab001d9184",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "4f7d55605cad4ae2931e1331c5f2234b",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "1814326bbba04ec18d2f25b558d52e32",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "19964495a1d44ab4a8846bba2698457f",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "62f153497a2740b9b94985a9f7b8d29d",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "8f2d295c30034ef3b11481842fc58a8c",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "e7eba5e38f6f4cd2b2bd531b8918861f",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "623f5f15a7174dd38bf19cec00cdf66c",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "88cf2defcae44248a822393afefe7fa8",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "801b489508ff4947bf76e6242aca2990",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "57993b87ec794b519aa406296c9830b2",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "89523416ff3f491792e92f01b3f3c20c",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "fc5a135159894da8b214c48af5867f5f",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "7bad90be58cc4093a9838d80cfc29412",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "830b22eb070e4cf385ea17cd15ae32fe",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "033de7b85c214c908f04d9613d3a7b26",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "21f24f36f6b6486ba1602baf25fa2406",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "4b6314433361417eaaf1d1d0b69837b1",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "c5786a2dad22455a92837072c43a9182",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "80fe10514fb04056b56335b4aff30255",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "bcd36f11ffe04fbcaecd279c74175c16",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "5a1889c988654fd99805dff3ab5f5e84",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "19aa82d7ca7f43c6b232ed15b2420688",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "39b0a2b97a5f4b4e8da509fe786a10d2",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "59cc6a178cd341e88e92b9658303d133",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "51aed4ee26724f10a6201000f0ed826f",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "b9850a8f3d924bef9d19c7ac6b0ac387",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "6a5c95dbdc9546619807c69916991023",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "ea2c042f92e841ce85eadd3154bec4cc",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "959e2b695e8748ce8b071fe148e19acd",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "5183356ec3534e72907dd694cd714bdf",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "05c30640c9fc4c849ed3ca545504ee1f",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "770e0812f8d04c9ebc6bbe099f997738",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "1e8b4d9d2a9646578719ccfafa80566e",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "3c593ea5a4de41f2b07a3005758efddf",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "786beabcd0a947869003a3e61675edf3",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "9bd79d24845148568bc4218268e0ea7c",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "9c4890ada037435c9ec3d44544deb46f",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "5d55ea0964d24da78363526123a4adae",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "35cca179d38149bebcf0144bdf099213",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "4ebcbb0d54f94596b7f947e68b23afee",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "30bcbfa4c7384b28acdee35394a918bd",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "6683f9c86b7f4e67948b2d1a31f5d2a1",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "87cfc8af2e6a4a548f59c9f2c002884a",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "e3b747308af94a77bc46a17a3573a070",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "3a9cd8e41c74452d8ce856a0bd8006e2",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "61c9ff989b96498e888d652490805994",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "23e3995386924aa381663180b6d12b95",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "e35b9e9a04bc4f1d9fee4283501ea2d7",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "ff2fc9653dba4779ba3dafb69c372e3b",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "e109c5edfb3748968bb0ddca906708f1",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "9d97fb7790414da5a98c342965a746d5",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "7a27da467c604dc883ff14249f7dd5d7",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "ab868d74978b451da12317f3992c274c",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "801c3a26e126489ab971d6875cc7e8f9",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "48564f6176b14937ae6dc07568dc7b40",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "45449338007349b6a1bee2cae5a37681",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "81ab7aa97cd24a6689bedbc87ebd434e",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "7ed0a78187e24c14bdcc8c0022708d72",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "e84a7643dd2d4c8386473ff87daef9c0",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "33f2138ee75141ecafe83189d3a3cc14",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "ddf9c5db73dd44b3bed1885ce5f6a905",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "70a2c7349fbf47ea92db1c2024529354",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "784b734723f04d40bc66fd5fd5023a7f",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "9f5a2cbdffe742959cb1682422e9a5cc",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "09e48f3a4e36490d83fe605905c80d29",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "721a36fba6194fa99f0b7aef7b1a11ed",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "965548d23f0b45de8bb2904e2b4ec563",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "f1f20f30a16b4348893d2ba270c1e9be",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "65463a0581d2455d92ba0b34e50dd755",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "201ddabc80c344f38a40c48b1ce2a707",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "03ed149eb8a44d4493bc7c881bb0163f",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "7693da65767e4b9abee3d4cbbc28175d",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "b342518cfd554729aa817c7fff602045",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "1f55fbd82edd4d0c96d418fd1722363f",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "87f5c1609df0425eb902748768bc2f1c",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "a87c9614332046aa8890fa980f6a8d3a",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "3224ad725e044d42b8fca532a913bb63",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "6036718426974c8e9c826159ba8a9f01",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "6bbbe1911d7648d8a3a87a2aa7b2728c",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "94661e5c32904cf6baee575ce8aff370",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "fda3a208c78840e2b335ff4744b89eea",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "ac10ffdcf89e49b09289c08bf3674ac4",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "b97c5dec132d4a76b38774f94adb33f7",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "341b2d1b452e4aceaa9ecc092d3ca86f",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "36171469802a40b798b15a40cedcb898",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "3dcb634bb5e64d01bab2b69a908913cb",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "a5b0c727e18b42358429d579f724825a",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "5d8931a4888a4ebca88486edca2db415",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "06ca539e83354404a4315f8f6f14ef99",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "51c9d1d3ef58442685a8a1089d9af10e",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "c9c6a3c742f74dd193d63c44ca6e49a8",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "e9e85436d451475a81dd364348a67154",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "30a4676ca0a34735a55df2770db50794",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "c881bf45220b4181be7a716f3b566155",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "859483f7f4f84dd0b7e8b5588ad15e3a",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "6238248d60114cc18a50850fcc5a377f",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "71d89009cfbf4ffeb8af2dceb57c3004",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "3e0d236c6f6a4bd3b0568ba6be497582",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "7f91e24122484b0caa16230816112265",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "d03e2193cf2441d2ab5e3fa975ee5d5c",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "96e2c6265c8c46bfbb20aaab9c2104a3",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "0181c8387606434885875d617e01ef29",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "f0b26e15a13541b39d9e20f60af06b53",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "d37a848f2df04e32a2dd412f5be76445",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "f1f2579257094c4390e30ab2f16f9487",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "e8941b8e00b0446c8b4bcb60e49dc0cc",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "0dfc749075bb4c02a07f3be38500988c",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "003dfe7e1ba14c72b9162cd77ef54b5f",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "f139ab33772449a4b088f5018714d1ad",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "72df687702bb4069a07e609c0514ae68",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "8a9363ff30854ac0a03ac435457fde15",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "33ca4675fdc143378eae9beb90978083",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "50ca3c706adb4d9f82adf622f9c4bc11",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "d1a8803021ce41a3a98d3d12591414bb",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "1f7d80fa96364814a1347d75a6a21fba",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "b52bc001daeb4ebeb657ddc2108d29f6",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "c615dc776e6045158954476d32896c87",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "dfd1bcc43bcd48489d3c2c175dff6326",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "8968275986814521892122c2e7c6129f",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "73f3b88230cf44e180d2f17f9e58d529",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "aca5ed2c379943098a4c57bf147ad105",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "619c3ec7be0e4e82be72fc4a1c70c568",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "5d759078f0fe4c579cd9b1505a5439b2",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "a0f12c5173ba4723a11ac47df885f2ef",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "a085a9f0f99849e99a4a593759e37393",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "b8c590b87f7a47a1bbfe1edc3e8cd82f",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "13dd56e841ad470ebe03a00091d54ecd",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "6d25415000be402482a5d8caa5a55762",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "343e3d40c67f4294b499eea5cdbc54a3",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "d9ae4494832d4d06bec599b60d5ffb93",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "c497561c0c8940149d06667a658eb2f3",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "69b5b006f56346cdb63f4972dee159e2",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "0ae25f8b25a04174ad8a8aff19b16f0b",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "4bc913eee4b6415885ce5c43d97ed87b",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "3bf10f738db445eeb30b3250e77e53a7",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "e638356c794e424892ee504ea1e19248",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "ab1a0b89df1c4430a9667ed68bc235f5",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "f2110700f9e2448dbd23b6df7ca30e85",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "42a83b87e4e84245a1c2141fa901ee8d",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "84def8ee98c447e28700d4093bd54b66",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "c81b40e69a594c46a9e856740853ebb0",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "9214766dfeae4e46bab299697f10eca6",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "7c22502fca7747d6b11d3a223d2cd33a",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "a95fe3af7b644317ab602aba52b9364b",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "fe7766e6ca254536b852f0b5dd4ab841",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "8932ae7754f44c4285a04eb617643c3a",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "c2179424d5954e08846a203fef3e4506",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "9e17a8c1f1294d088b5273f9cd67e01e",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "32765f5112dd4b95a37b46bace550333",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "29ea004c8fe04e74b2c304a9c71a8974",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "b0dc5b89baa74243a8ca682f235b7647",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "9d588f40448d42af94f816f7aafa0c7c",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "39e4eb93d2764163bd923e659afbc518",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "823eb669a93b4461be10f14843221581",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "e2a3ecbdf2ea4f65a278c464b8898e49",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "2c15b5cc29e842709cdfc4cb93d736e5",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "ae18e13b64894c1db6dd72dfcd6d5d3f",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "36f69dd79133478da6edd591391b71b3",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "2889b92e2bab4320980ddc27b8b541d5",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "aa522fd483094872b52da7e97c8620d3",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "9b3f5db7aa6c456da61ae78d9d03447c",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "027412f38b99427891aaea6f6c17a001",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "dff0b64ab4d44d258460aa8f0b61567e",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "fd4555a85d24449ab23fcae189faa67e",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "b0ceeb465ff344f0b24f86fca38a8e4c",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "e6a7fb7f53d64fa281713ce0223e4578",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "654911441b2a433e885d1e4c5f8a0c40",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "83824141595a4bcf9451409fc27753c7",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "383d197b3b0a457a837a1b241f0fa33d",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "137f9eca83a7428885a7529b2b3af6ee",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "e3383afa7a2d43b88f2d054755e87a52",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "a46d16dda5c14046beb677871ba4a448",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "e13538a5411341398328f4c83389d350",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "4ddd6f8aa6a2437684f030e7defa81a7",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "3f4a6b44d4ab4dbb95d4c3cd0891077f",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "ec4c8ca5772b4f48bbda1f31579bb30f",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "fd8dd4e11b09490b816e7e2e4c535a51",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "8520d9be5f6b49e0a7034d72802e7f72",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "ce18e445e6d44e7aaeda7ef89db5f140",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "604a7d32316642c1b1be2194606830bc",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "472a2345356c4c7497a845e1255d5aec",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "181da02f7096454d93b63841046aca9f",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "d571464cb74d42afb26118eb78366014",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "7bb0b883217948a1ba9d58da016ca8e6",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "ccd3586446054c52aee65b2fae8cc8a2",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "3ac41333f1694c36908a51e847be393f",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "bb85dece726d4b3b887c1a3b39c70add",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "55bee6c410e8403eaba16fc4e499d6be",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "a3f5d8353c4643c2a0824b203b54ab32",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "3c5843eb40a14636b5a2c509f9ca13a3",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "38e09b9be6b046acb15d2a7f2fa3601f",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "ce5b87450ace4d19adbeeafb9828d266",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "6afe949fe0a14b53a343d660fa23eea2",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "908f50e9f1274567ba3b649fd2de88c0",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "bd3266cbd2194d6bb32f9203e3e0f83b",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "124ab13a81e14ae997db5824f6116b26",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "2b539be68b0342769ab93917417263f7",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "56ad5d80940f45ea9b46eae6eadfc0a0",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "e54b68543d6343908db6a4a6b6cad23a",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "b23bc7fdd7684a04ad39ba155faaf993",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "61e7ff0df17a4694b28b785c4fe4c8f1",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "af9ac53a4bcb427aa91ad7970376df29",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "51efc395e1bc4c64b100b3350e758f9e",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "02751c7a67244c4295ae2e84ff1c4aff",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "ad00d5ed6a504f8e88030b24477ec3a0",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "ee454c7f6f3d4acb9643e05692287335",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "c98c220557b74c3e8ff1e7f81b1b7911",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "11405ed56a1e4b709b60c6b67b195dec",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "baed44478222466fa5e91c24caff782b",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "d7bb0f95f8c948dc9a17b476f02c2dd9",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "b51665215982499592224021db33adc0",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "df4daf341272449eafbc7b6e6fad44bd",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "54e85ab16c0240ed9eeca10616d83ab6",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "a429eab1758447fc9097ca2225fdd5a1",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "58a6fefc6026404d8c40081eb087325f",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "b212834bdf34465089fa6e8c93e94e7e",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "fe533a57e05b459c94dd49b2467b3b19",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "f9cb9634f62540c79ef82a8ef08ed52d",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "6bc096587f3845bea05f7fed3f44d729",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "4fe5391593794df4b7ffaecbeb2c2644",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "67d4a9c4472a4358aed355a94804ecad",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "25998a424ae443f59fc077891b74fe5b",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "d101b64a67674170a2b7250c25b00c97",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "a3fc14c5855149cea8a0db2325c0e170",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "d33b5198d5d245ae96b1150fe5dc6a41",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "8d2192073cbb425aaaad7cf5f4442f9d",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "0153abe5df1f43c8b08e5ea18a3e612c",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "8a44b20dc8bb4446af991335adb96775",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "95e5e639fefc49a6afab5edc5006fdd7",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "bc5edd385d7740bcb330a8fe0370b8eb",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "18ee27ec3deb4e909c15385d629e394c",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "54455351a80b491893d76104ae454b40",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "573cc25a572b45449fb1c8ecf9f53a3b",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "6e105cb39a2c48f68b05a62e3865d7af",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "e8dae830b54646b6849af5992795cc43",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "d93409e100a745518392c933af9a9c3c",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "daa62b0cda244d578ed945510be9c70a",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "4f6c44d87ae5487dad213d946eef43d5",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "9e8cc6b971dd4800988c5684b1763c5c",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "e33f97fbc68842ce8cdc40f64f07fbe2",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "a061b56fe0ea401592a60dfd1656b00d",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "836bb8503af84a7daa39753e3f6ccdde",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "c673ca8bb42542dfb5090accbe8acb0f",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "4a956c0ac0f64558914eb61ba5bbc4b6",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "06d19b2b90be49049dbf85b4727b593c",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "f3e13fcb30e54259987ef71599bb81b9",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "177ddfe2429d453580b82ff4b87c47b7",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "e92a3994af99494eb92e57ecc1b2d934",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "64581eafef71419aa452e9c7cb69a49c",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "ee9474cef3204db7bab6bf34c8ab5d11",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "ae9125ddce59443ea2840c2ffbbec96c",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "642f0af755da4baf99a1a98c117938b2",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "2d301fb2582d48fda60d396196f12c51",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "c15d8dbf947d4ae1a94ee034e9ba59df",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "363408a0a6b943f197f48fbb2130c168",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "8e80842a88af45dfba5b9340b3b3d308",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "1ae2d64c7f3c458f8e00bd05cf0a9efb",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "045bdbc445ed458790ecc2f9fc391098",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "469b22f494a84c91b75c39421866d4e6",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "bac6e2e9aac44581a655508e2018dce0",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "22c4a7ebdcd24d6ba4d527dbfc36112e",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "27ca8ca4cbfb476896fa7362795f1415",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "5108ff09791a42c19cb4345a541d76a2",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "5cf1693c293240b3b0c282d68e7fe7f0",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "b2c49429fdc54c2bae2999096a08979d",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "7fb8d38506b94cf1ac3ff76b7e2cac79",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "ad86af8652934dc1b1aa6c2d63d09214",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "982d09d89d8147c3b76a247728c57f0f",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "3cdc50e9331042a4b09e771c8ddc9311",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "8a9891a5b69f41a2b2ac83b07c00a5ec",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "93b27b1c2d15408ca4c246c74e8d13ca",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "17bb694322a44520898b58f9494b1109",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "d31c620888a04c83bf1e505d01b0d3b1",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "a84f12997bff4f0f8588ac6a90a1669e",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "7dabac1bc528470b9d08ac0580bf7d0c",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "707fff2a82674c51931b0a730bb9739a",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "55fa4d523a7a4dd78247f9a54da97002",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "86d66bb10da9487dad73e2e364d3bccd",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "73f1684428ce4454931fd2092474048b",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "ec7810da8b2c493da224904b6818b88e",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "878bcb00bb2d47b1baedabf303691182",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "8e1f212d58c549bd86f3b37cc2680389",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "db6931ee46294fc094ea47793a27e1ea",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "f632a65ce4684fe991548c9987dafe5a",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "193a863743fb46aa9c27f2dce249756c",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "c1d8cdad3f0c4f64b321b658b819aa7c",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "b1d0aebc303e4788a141c659c07792b2",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "bedcbc16961540c9aadb40b84e4455a8",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "180837e503fd45628137bbfe1e40d46d",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "1f7c70f61c9d4a67b54e0f076619c4fb",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "dedcd0ecb453439bbe420bbffb57d8cb",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "70c434b289a749d088444fb5cdc4e4b0",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "53001010e3d947878223e52f028a24a9",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "6214ff6344ee46e38cc3682bed04ad0d",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "6f425e91874e4a93a6e0a2b93e64abc4",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "0fe15a54b0f24c9691b1c9518abf9729",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "450c17ae4d5f4000abfdbda7599351ab",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "2d7cf02482c84e108c0438dc2a2465db",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "bd597fbc299e4ef2bb715f395236a86b",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "9bec31bae8434c598dae36ab5fb52720",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "8924fcde08da413194504b0bacdfa419",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "4bb514686f84491c9ec5126c74bf6bd7",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "fb8c9e9cf074453db3c5414ab4e29960",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "cb8a1473d9d7486d89445456969316bb",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "20424719dfc54ef69f482f3dfa5b3b5b",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "f7ff7da82d28416e864ab183218280e4",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "664537b276a6414e8d1271579601931e",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "69fb46cc0c6d4276a48a33825f2db255",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "0af380a1d6114cb08eaa69a5f343cf67",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "bec250ed22914a6e91ad2dd4295b85de",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "e1e39f098c914dd8b9987a8f6184e67e",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "f7cf58aa0b07470c9779c54a6b18d5c6",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "55dd379251db4a27a7a4d66298a86280",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "fea78d10f5cf4f3ca7e0de715b580215",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "d3bbc218500443fba98fc76acb9bbdc4",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "105a340670c44d92bd05c341a74ffe7c",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "9b6761e0fb544c608ab6f928acc06fc7",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "40cfd1c84f3747f0a957667b782e5cab",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "2a24bfd0ffd44332a11775600c2f7235",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "8a9be7fe242b4f4ba36b2086a61f35b4",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "9d9ebc4f64854d08836a20caee78bbf0",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "3c40507dcff84cf99cc791124133fcd0",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "439404d7fc6f491fb7af59c93631cd7c",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "fcf2b9b6bb104b46abe722e1e87fcf5b",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "2ca6f2112a484ef6bd45cb0cace32be2",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "8a09ec6fe7b243d9b57aa7f45c2ecd41",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "f16fa70fdccd4cbab4d412fe741d0bb5",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "7799fbaa96b54ce39e08c6afb567a80a",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "81647c01d80b41d5acc1ffab1e03a4ce",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "34d4b861d2894551ab204c7b6e489029",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "599b1aced5ea4251824490cb129494da",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "7fa6878e6fd44cceb46484c49f146eb0",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "2eaf87824ad641998a79c294fed753c2",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "bc9a137c5aad4bc2bb02608147666d1f",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "94de2ea39ea94e4ca958e87c43f58861",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "a684b2beec9942f7a9cd3fe440a3311b",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "b2c46093beb54c0e827eb01ffd6a009b",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "5694565c249942eaa1c661952b81da81",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "2d6d1c3bec3341f788966e12363c5f70",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "368eec3561ef46c29b4dc1316c687a4f",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "3d2d537039ee431b9d88e30ebeeb266c",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "bbbcfb95cb8842718df671875d0dec47",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "4abbafb6a60e48af81ca6e0f3a99eea6",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "9af94e299df3474aa1fdef277c94a45f",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "571fd45f4dce40ffb6c98d54c7c1fb5b",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "c6f402ebbeeb454a80ea82418f81fd1a",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "5ea12728cf3c42b6914cf64cf46c4db4",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "34dc6f89cb534971b6e4a35be6bc53c9",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "807eb3b773e64d57b38c2253b866e060",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "56024512d1ee44a5a69529c401802c5d",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "fd374b3572464920906d509d8b656904",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "eccb86f2b86d408791790d1fab8d9779",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "392293de59db407eb1c35468b498b4a0",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "8f5a100881e947019e99a984040bd044",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "b7ea09e716254b12adbc2e2d469203fb",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "452d66626c3d4c048e5201efb7c9a4b9",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "8a14dae249584173b81b98500fc9adfe",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "cd4d2bd9678244f58d69aa595800bf98",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "f6e5418e96b3464e90f1803862025651",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "6ee90b9b1ded4495941a5223e17e64f3",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "309260b34e8340d59f4c09eaba0a4866",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "932d0252dff6426e939a848583c2fc7f",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "c6621ea6ebfe4cf7974d309d0459ff3e",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "182966ed4c2a4d8fa01484eae010d207",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "a8c202d72ecc4b9297597c499b1ff77b",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "39ed6e8d64664add8dbeb55f00dfab1c",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "db1cd6b2c262491bb9f8de33b152a9fa",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "8d3cf996b27945d38b5a67984b8f262c",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "837c0f1c16c74f6e84fe0aab12e51434",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "572caf8511d24c93b6a166191f5c90d5",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "80e08fd7f52b4998b2044c86f19a5ca6",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "7b47a95d46054aa0bb6baf6e4abb286c",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "f09419e728f147e692becad55223b75c",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "95909e6b64504a678c35f78759e09fe0",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "181dd936ba6d4256961e68c2c7913732",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "12680541e80b41328fe0aec62aa580b7",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "a36165cc50414fbaad828077aeff23d6",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "79ccc180b8444cdcba2be147c1039f31",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "ece3e81b89d349419c1588cd90ea3cc0",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "967d37595ed74558b486659464a1f94b",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "c38659ae194e4cdc824d58122ffb3eb3",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "09b6042ef72f4a55b0381ff9012fa784",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "51362657200f4568a9a717db43be1382",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "2b9931657b7a434380a01a726630cb10",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "7ca09063ad414e5b8001d12e0919acef",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "5715f9e411ce4369985802f3e9ce25fe",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "89773e68dc6a49ab968d7ab2b672ea83",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "3777e6d393e94cd9b7b41814ef2a0436",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "00f2659050df43308a8262ce4dbeeb66",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "413e6f6b09034bdebb1ab9007fad6842",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "2f8c4f30886a45a5ad5410b44d40747e",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "cd0a405ebdd946d8b3844b1a5d16ab8c",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "83242a078e7f4af1bbb0427cf8705062",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "e1056a8d9bc049ac8662678bce176739",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "1cae45c3985149f88c3103fd02473ccf",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "84c17ad6c65942a18d69059094a6b988",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "ef0ca6b75c19434fbc0d60519e6a9b66",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "7203b99fcfd444868ddb87a19ceab6be",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "afde202a6819473393e290c0929b04f4",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "74d249b950484865baf9595780d26866",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "d3f9b8558f1f4490ab3b36b765459de0",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "88407774b61148469f812aa6d030d1c8",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "a71084deeed74f418e2e53fb621f224e",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "9220aef30b554422922af610f0d6a796",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "b0f15888ce8a493fbc8888d77164b4c8",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "27ede5e634e44b4fb769cbac840f1003",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "fc6fc0bb3fad49fca3efc6c9babaad11",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "902f35f3abb9436c9de2ca3c9855855a",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "4c7b093a3c8f4cbbadf40279da30bc39",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "c468462776564618a54348da4befd586",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "7d0cfba016a94656ad326dc945c59e05",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "66751bd56ad74fdfa6ae2dd1a11e73b6",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "1814b26cfd50490bb31989a970cf960c",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "0fea0ea212504841a9d0c41320beaa98",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "56ab221745494708871686d592c893c6",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "d1a21f2e8ee2457f9512f185db09e15d",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "141b64871fca48b5a636a22d5a0a732a",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "c56e5a9ec351479a90ab4d69eaab8ee1",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "ecbc0c537850443e874c5f593c78de0b",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "eb55018a980e426ca1f1ed00e2195c73",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "a5f8e41dfdcc435aac3cab8cc8437b95",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "be339b3edb6140c287f70a0bde6c819f",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "9bf124a6f10347bc8c0ed03176ef8314",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "187de470b0be45e0bad6d72c6a1f89d5",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "d038d881f7e14b21ac25ab36a82f9145",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "c0f6e4356a1540058b2dfd35d9245b2a",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "27273682891340be83ac78efb6ea5359",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "7ea8c0ac157a4843bdadf5660f2f64d2",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "1e80108685b442c59cb577d16e0da6dd",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "8696bdc6b6de45a4bb9bcb311d69b043",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "0a18a70fd37a4110b7abf695355ebec9",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "628ea1fa2ecb4894881a463b363d793b",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "570366ebd1bc45558fd58f315f9555b1",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "d7a93daf2d8d4363aa6d693297febf19",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "5caef6cbc5604fe8b813df097f6deaba",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "e9c3aa3af1cd442796efaaf24ae29207",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "43d1d192aae246f096fdc0e6e9151ee3",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "c0069f96559a4911b61b46ffd5e1a895",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "8f9489cbd5f346ec927d0db0f53fa11f",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "527edcd2f2f74dc594f6b34ccdc7e7ec",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "9745897da99f4d739a867c65a39d0d0c",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "88db9e19037641668e4f2b7c51af42c8",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "ef1954c9aeaa47f9bd02c752ec021392",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "deed63e4b42b49b7a4f35a87e809cf9d",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "6b46b3d1aff448829ac23f1ad942c801",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "991372d37a734371a2f1fa514f57ae08",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "65b19cb57e4542fca1dcb3a79d852fa5",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "d128952080d94b7fb79ac8080e46c441",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "8e888bafb991468a9f4b612c9e1b674d",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "8aba873c37c645469571567d1cf8c56a",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "8faf54ad19774ee1a09a68877e588770",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "3ba13cb176804a45a1d170883266d745",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "3ad93c4d137f4251b1f64ad639457454",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "6ce54b7400094ef9a3cc81de6de936d1",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "a6a93cebcd9645e487ae285110f0418e",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "c00a60be867f4ae098128b4cd9c23383",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "e7d66678950a47c2afda94bedef62fbd",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "d53d0cc10dfd4ebb920c8420bc16831e",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "198f8843e9124c41a2de1017f0906130",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "a10ec736503e41c9aaf16c0ea4ccd49f",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "18957c2af32f44c88fd2d2223aa09469",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "12a57b9aa22643b6a833adcf53b964dc",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "d94655cbbdbc4b58a25a1460bede2ef9",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "181146488aff463ca52e24c6402c694c",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "20bc879b65f44b39a8137028dfd34681",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "b12bf139c2e24eabaeaa207c0b689122",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "f2e1aef4dff64257ba111a768f69b9d2",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "b8f0a4859e6844d197118ba021263d09",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "20c9310cb3ae4e189616867356bdd014",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "8bafe73e43fd48a790a2228e83a5927b",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "ef9e936433ce4b6b8ac93d63a305e322",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "b8e57f80cffb4523b34332ec87d099fe",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "4666b2ca995546118e4c0a7988b3448e",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "849c6d692d3b42219addbcd7044b286a",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "308074a278784ce2ab12e580463861ff",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "23263224429f4f7687e287d9897d8e09",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "63ffcd11ddcf4064903d659e84f596d9",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "75aee6dd99144405aa6d11b4357ae7ff",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "b9bb80ed0e89441f8c692d3da9a93f62",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "ee2ba8d8a6c5456e871dbaf64e0868af",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "ab6af396b7364e958ff4d1f3aa5ee805",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "73ef270285004c74aa8ee604efaa827d",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "c6c0aa7289864b1eaf45c65581e4f260",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "19fd04dac00f4e7bbdbaee97f23b2325",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "d19e982292eb485ea4844ccad3bb8301",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "48a919ee5f5c425a82e5a8c0560a4023",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "3bf4dbcc11b244dd9393b74f1e1065fb",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "727bc229242f45ee80359c57758a4863",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "b2a664f9266e439f8548a302fee602f2",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "30b2051bfd744109a09b71485d2300c2",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "77c1c444b5f94b7ca845d2b54fe39e73",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "a4e48c891eda4ed68d333a48555976ca",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "0c5dbc882a36436ba343fc4e336a18ec",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "384d7654966641f198762098f348707b",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "23b0af9379e041efb0dcbcc1bcafc102",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "fe6f6008dd3243ce8e64065bb780e61a",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "9bc1e140a8a24e5d9d7b8b69af778979",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "81c027d5cb1949338adb7e196ad4193f",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "e7c76b992cea440f9f0999221a7767fb",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "65c87255d68b4c1d906717dd7e80c992",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "4a1f02f3e3204a1393044d12a28519ce",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "1490d2bfd0b14782b4f7a6aa6dc85d2d",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "417e4eb9682d440aa3fbfdf98a86401d",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "0120c110ce654a56a32bb2228fdd012f",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "b846ef980205495c85533bd24de3a7ff",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "430ab230f8d74494bcf62f321730611c",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "0e6efc9ac05549a2adbf0680a2dfda0f",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "fbfbea3e906a4cb0abefec6b11fcda0d",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "98619c0c41244f4aa60ae3e960901b06",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "56ac2636887c4f3b9117a751fc6b5b97",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "d1bea164eba342a8ade9c91c990775ef",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "78c7a650bc7840e1ad2e8e401b9964ae",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "7365f306837f4beea44348992f702fcf",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "9f02048b3ce54efdadbf7ca4f032b99f",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "78e655e60bea4262ab4f6806796d042d",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "914320828a394a1c83483e6b85aa6ebf",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "4aa531c7c2db499fbdfe9ae8563883d5",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "a26cd6eee53e4c5da655e5169355db77",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "2d9a20c841254d7e8280867ce5823fd2",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "d4896f5f78ce407c83ba699a2fad53ed",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "ca9fcfc2d8364b2aa27cf33bcba27e9e",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "f9a43ce017944e96993a9ba9e466fcc3",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "78cf59c5c7f345b097c20762f6c6cc87",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "cc006b9c23c547cea9ddf3a7b6c140a5",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "67425f38a8af4bc4bcab6e042d228358",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "c372bff56ac2424f84afb7914232257c",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "9bcfecd806a54213b9d6f931de16860f",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "b6e9635efb8747e1979495341a6a378a",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "623f1fb07bd84f18b9b9b2955267f9b1",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "e78ace2275ef4d2ebccdf18e19b7b89b",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "791b645031b940e09fed4af2206debe4",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "064743b158f547e08634f613e91e8665",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "2fce60d1b9b242c9b36c659511680e4f",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "99534256e2f04678a914f21f5a397cc8",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "c00bca79552044d5ab1e0d2b17562eb9",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "40f6fa2bc17e453c95bb153a99b472b2",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "95186c8bad2d45cf93ed4fa20e019dac",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "bac6449f2bc24dc2a95941f71765b82d",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "e145768b85424cb1888cda97458b3056",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "48e2c8bddc37407e9a044be7cc42a65e",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "3b243a1a14474ae48c0de8d8ae8280fc",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "398b2da4f4b948c0ac3a45bc8c4034d6",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "412c213638d843808c9511efb07d4b28",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "9b98c46aa6cd4058aa11ad033e07dcf0",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "6246d870c1834136b639ecbc3199f9e2",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "22d6d1a951a147c8b77fcde638e9dd07",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "d2a3277368bb496f8f1a4d2cd2411691",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "1ff24386f16842bab1482978fee0c4db",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "64dbeb5123b6425f81beab4529ffdc90",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "5435b67d00b64b25b76f351855fd049b",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "f598ad0b59bb48299ccce4b0ef1d9337",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "a5a7a8fdd24942729c87f4cdd058c980",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "339fb2545eec457cad4d16ba82cb5271",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "fad5656deff7420e8c810987c6761cd4",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "da7f02fe300d409b879fb4727895a3ce",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "c9816b04662f4e4894de0c00a1395805",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "198f7df4157f4b7a8a675836405641d4",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "549c59a8d45d4fefae02432c43c233ef",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "21ac8e030a8c490e8177c4ad82dffe0e",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "f749a665d3b14a8faa85e16428dc147f",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "77196f7f0b664ce9b818e788d9f1680e",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "b9d7f11d69d74e6c8c0fa84ccbad3157",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "fd547461582745f08b4058203ee9747a",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "8dd26ed22faf405c9290268cbb390f03",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "34839f3dfc934f8a98e1a53cdec19fe5",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "fe48b7be5ca94bfdb119e9163b0d6503",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "fb12c141832743a0a3152718d899c508",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "501f853e658546c484ec545270f5cb1f",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "1de35abc007541779f9d3c2d43a44af5",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "83e938d06320497abe7d42d7fbb19138",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "cc026e9391c84365b3e4153a065b53c6",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "66eb7e0f595b4517bf2b16ed001546a6",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "74725bf9bdf44298b4f4c5df042126de",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "f34ec206dd6b45fbaa67b2b7048ca70c",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "22a1aab898d04595b8ae6a4298db5e6d",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "48a857aef08d41769f578f4d333a1cfc",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "36f1991ea0784242bf07d17918e8ec8c",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "75f975681b524a80955261b126836009",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "2d4c8083e6af4710b1dda5aed7ba954b",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "dc03c26780e244f9ac3e6aefa12283a1",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "ba472d9ce93c4192b9c88fc5e254af07",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "a0e1048caaf647deadfbb1e56f74cffc",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "3df5df92a34b45ff8bb49bf94888fa88",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "3722989fa15647c7939b73ecf82e165a",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "63e1dda71de4405c8d9d7f0df0dc193b",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "9c08033c418a44f7bc10f3f345d56463",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "b0a8438c5a3c40b0b3de7557ae67a82a",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "4783b994cd1a4cb195eb31d0f6a723a1",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "c36a760dfef5493ca41ad584e0eec1a7",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "29bf4353d0764b15b8150dbb835c1d33",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "cac6e58b590b4aeeb89b1bec80d3b8e0",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "1c069e13c62e4f3d8c95ca3b8f055c43",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "ee847d69092b45df9d9217bb9ce2e9ed",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "7f111ac5302d48b3bf81da09e7e2dee8",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "6ebad1f215ea4dea8f9016a99ed87131",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "71d1a211f0294ef69458fd25507bde83",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "b99c7806d0d1410799f7796f7aa84e21",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "2e0d7a55736f4e8f98305b19208c5ea6",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "d7d3a2aa867e4b8689a6ba8d42e18839",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "5fcfa7b6875544a089475dd4cf05ff50",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "44f87b69eba14455b167d779c6d82008",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "2a8eb250d1f04278b64dbc499aac728f",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "cd0747e1c0d14325bf86a5fdddc3add4",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "31ec0ad5d44242e39c64a575badd2633",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "3c2017097e30474988dea0ac1e1bf6c8",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "d73fc10cd8244b889e5840f975ee317b",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "dcfb7bfe8a6248fbb951fb4812b95949",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "524eb9a6cc2f4d3ea37ae1ef2d99a892",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "a556c782d2f548e396c5bf8ee297db5b",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "2f431df693b34c6fba8849486369cf07",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "7ae3da108ff240c187920ea47566b439",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "9d9bd1c8dfe24f2bb7dc35bb0140292a",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "075dd742cc014a29920ea2ba7c6bafb8",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "0fe74bb144ec4f85b561fff7eab3af5d",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "48dff3e82766495891dadbddfdfb4c6d",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "627b7deb577a41a98bceaa3040ab847f",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "53d39f14e980499184216f4b23fd1f98",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "68b8aef0d6984067ad58579516785ef3",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "0c40b4165caf4585805024f854570b3b",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "152811fa31ed477b99e8064840e8752b",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "6674a3bd854f4a888c1a69a182d3f53e",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "7313a1aecb7347058763bdc35d89d27c",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "8815e69b3be9474e8eb68fdf4008428c",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "c43b462f06ed40148406d5bd242bd0da",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "4eb15ff41b0243d39f52c064f61dd16e",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "78060d429a3a4f44b7ff099b9410384e",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "4cd1d0d2a15a42f88623350281e8d673",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "b53bd3fbd60c4d028f9735942c07e58e",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "fe85c80e4fc6476c95b684ce8169c525",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "c59c6eeac3de4690aa7a63b51e1b4018",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "fe35b7c688a3448786625bfcf39499c6",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "d8636a4a0bd243658d192012f442b054",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "83cb66d15fb0473abef380905b9d538b",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "77bfd125f42c451fb0f006f733e3bbd6",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "fcc6cb73dbde4497bcb5d1ae08677af8",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "250539a806b746b2913b89b97d9bf3b7",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "98382cc2835143c590e0ed029873308f",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "2230a9f686864b2db815b38eb66472bc",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "6a19b0306c5846539ad90897937ba81c",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "10e7aadf42894ad29c4cc017fe4efcb6",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "ab0e6aa57aa244fdb0c8e7a3a33d57fc",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "beacc286ca60489b8590f5c131f5f594",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "1155ba55ba7349358d813e4854937ed7",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "51694f7e8b5947dba022ae0dbae28398",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "120afa4a721c4ec7828d416ef6e083cb",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "a57de56588d04857bc005b2176f29064",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "33bfc3d19ed0416ba914a99d9717297a",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "61ff1e19ebad4ea1baf7b6ccffd1b472",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "dd8ad8c8dfd340d58d1b11584465f2e3",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "37122fb1eb364abca686dfdf8bc0f729",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "d4fc8320bf0e463bbb337effe9ae41c7",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "47cb2acf374943cead1acf491660f8ce",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "d83dfadd41ab45f4849a300d0881874f",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "d82879b7973948f7929e432899cf1e9a",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "29caf2043e624893a440f71179ad10b2",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "42779dfa50d3439088ac2775f8ef2a39",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "4af178f432ad4a0f9aa55d201e049280",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "6904a7064fd84970828ea861f0ab2898",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "356a2cdc89ea49ddbcd81efa2a78e3d9",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "effabaffa12c493988b74975d3c47352",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "ba0bfae9a75f465285cb1ceaffdd135e",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "de9ebf2861ac4c78b3629f475df4003c",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "f24c7597d8334686bc392d81a0297a8e",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "6594c04662e74413b0262775eaa2712a",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "1b55062b37e34beeb2beb4438734a136",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "5f8f9e3c75c144ac8747e9f036fa4411",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "9715fb54aa83441dafa0d881db7cdab7",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "5e07c9d9075c4a88855624929659974e",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "a4276e4439554434a81c78e37a287b75",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "034b5084976948cc986254f1b496aa74",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "fcb572115ec0403fb3151b07d4387580",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "9aaf79e2c8d7412cb2b357782d14af9c",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "bd09d412b07e41138ab8c6013da0275d",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "1b55609be3a54757a5c2019b95dd4753",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "917563661c6b494581e3311141cb433b",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "f9dceead9ef342c0bab163c70a4cdf9f",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "eed5713d4f774a958124a4e2f7659468",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "ede83150b4f64af6bdf19e4b23c84a58",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "5ced012461554ffeb3c7f58b685696d1",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "a3c2088f3cfa44b2973e94f379f51a70",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "272dd3ee3622444f8002785bf2047ed0",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "61f4ac40d09349f29dd7cca07552845e",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "d759b7f1c7414ae1a1e6ae34c77c9ceb",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "4a94f3cbf5b94856a363317f5e7aaa56",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "a523dc3a198f4415922368c83465fb56",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "8859a5a0165d4638ab261f56f2407fa1",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "495bcac51bb64fb5b6b39c2ef733f802",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "3c3e0da9e0364b97bcd1104a634172a0",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "d53401c3b6644976b29fa3a18f46a55a",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "244d690a389c46e18851168b8f2a67ce",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "242de6edffb34f7b9504baa3fd5ab498",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "0e21c7b5327c4d1ba324c837d12ccc59",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "9916227727394f688a40797fe8d22ccd",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "c56db62e40504b80b85d9c7c30cb2137",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "416cb379b48d4dba89813c8a44dd2d48",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "9703401c35ce4dc09284b04fbf21be7b",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "4d19dcf0a9504baaa11f4c08350d1c7a",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "5696c37614184f3994c7f6139750663f",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "80d8fda591b84d3a8d8fd262acc60f26",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "aaea28cdaa2a43afa3dd2e0b2f1192c4",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "88ceed75d1ce47e9832132de5836af84",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "bcdf7e585584465ab0f51a14aa12ce17",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "2dbbe1df1d79480f90a7b0657ee695bd",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "f34ae0a25ca1455a95d5cb60ce2e86d8",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "bf01260bff594f7ba717c113efbbc9a6",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "a38f0199006f4bdc9aebd6c6582e5c8d",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "774e47ca4fc54c0b9c1748e95f55a78d",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "3b640ddf6e8f40349ee3bc3c3a8b9da3",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "46ec060d50f84f7184e9f87692032bba",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "a5b7dff3bc9b432bbff0c88f5c323d30",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "9f312f6cae84477494d1e834d28662fb",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "3f0e11cc3fe24b2f94b749d409255a67",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "eb32fe854f214eaeacc66fdfbc9dfecd",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "5f6e70e263ce42729125cd164fae739f",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "d59a20fcc0b649659d688a1771fd3458",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "03eadeed87e74a3093cf9340c8d6a8c8",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "8891e0be2b144eafaef9d5a2b6766366",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "d63f55dfeb7f415e9e26ad7488d066b5",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "3326d6c5665e4c7f96a117feb74d9d2d",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "7f230ada2f3248878b7dc222f2d8b220",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "0652f5ca953544dfa96885120e9b89b1",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "3ea3e71576334fbf8e6860838086d6df",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "b7da9c1f873e438e911c3a353be283b3",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "2f6e69134d0b4669925afc531e355789",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "06f49d272764411eb8d0f90423943c30",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "e3a2e41bee7241618f5d2ddfca983602",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "e26958feeca24082bc8178f4195ba5c4",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "61abbc875639496eba5ff8de97a2c2cc",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "8cae4ad403ea43b6b131e1af5a7c0f9d",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "755319f32a53455c9864f5ba7ee9045b",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "0fd6954d26ee47e382ca6c5a6b5804c8",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "13cbd527ba8c4f4d915ee374d253a055",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "867850d1d4a04e7aa5c42edcbbb961f2",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "dadc017267c44e2ba9a26c3d8e962b4d",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "3ac0113842d0404eaadefb9ee64b6e00",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "fd86ec6264a6418689d99b3db478d759",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "522a54d57ea3404493390d52727e0b6d",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "749ef43d334048d684b4999609511b32",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "6ec9a5674cc3402fb3966bef936c2429",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "ed83debf68574bd4828a9dbfa3cc9a8f",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "e3718d057c19407db08803246365d5fb",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "38629be7c99240749ccb2dfaf6736a36",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "49fab9b06b5f43e5800ea1681fb3f5a2",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "9a41b03afd2b439ca07bb05753610ca1",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "b85fcc2bba5c41f681f279fd474956dc",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "494271707e9c4822be8cf022efb6ff9f",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "902051026d244aceb5677fb31a8a359b",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "2f5f597bd34846fe89729ba26ed557ac",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "4e50a60670db4b4c8c2a5a826b3acb72",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "8e3adccc90374e248d597dfa7913a749",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "6aea0366ce79451cb94ea093340b4589",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "f1dc1506cb2f4c698ddc7c6885f93838",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "221b3ed10bfb41329ec6bcbac274513b",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "4af9e8e0d9e3493e81c4c7678c0f0d89",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "25ff5e77ef1d42c78b577f36521c9bcb",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "b7c9c58d08344d63a5f425159d086cec",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "5237ab73ecf34e8bbbdd6da82fbff0e3",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "b57a201c9c5340f3949180081d8ff691",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "b5732812f6e2468baca316f51167e32b",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "f4cbd8f1d7c1488e9a38aadbf69a9d8a",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "2651d32860a94868bcb457df27bbf9bd",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "8a7830bd2c4948129f92995e92679d20",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "4d307d3485ed467fa33c32bcde430546",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "4527a59997ee4daeac5a9b5cf7a1e0e1",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "cda4ab1c2c314ec7af8de9c1fccffdbc",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "0a4ba986f31645369259548945ab92db",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "a73aa896e41c40789ebacdd8c99dac83",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "d04f087c95644673a631a47150de031a",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "ef7fa8e6e1134bd7ad4cc58baac7c494",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "a6d8ccfbdf414aeea5c25d0051e20a36",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "c8f3f88b9b68456093dac9268b23dfdf",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "ae95c0b809364ee7b353633a231cf7b0",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "66862fbcd17e4773add03c23aa68430a",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "cfb6c37bfbe34344a6748b888ab08d94",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "cefd76e64c7b41c2b9985f8082550fc3",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "9f16e95539644350b8e075519788478a",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "af658180ecc34e5a95e7380701e7a240",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "83cca5f0585e4a5d9a8db2b3a302ce23",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "ae7154a9af3241e0a65400afbfc99040",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "f4858670726d43bc9b771bd0d9bc1839",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "e9c0b5e0793a4ce8b8d9d787a848921f",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "d949a06b95a24a0bae3d102e47d6f9a3",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "0de58651e6d2476ebcc48d13c743b5ce",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "ce65a142c0044ed5b926c5e4ed6e34ce",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "b6ee772a607441b7a3bc490828a10631",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "75823a21491e499c822615bcf91b4d99",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "b76aaced71f147378280e1e5de25b663",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "0894bcb31263465494e9eb5d082d9aec",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "1915e797ad0447e891e4b69f8229c4fe",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "b6ae80da9c38498b9532e721e30a9cfe",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "6d3290f15de14061b54b31aa51845ce2",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "d4100768f8974c90b778f29671c3f8a9",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "5b929bcfd99d430d880f225201a9a02c",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "519fe88143914ab9b68287a6c24d6218",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "e487b32bfb784a5a9f8f9696e2700cfb",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "3b738f0ddc7b4b62be5e0e3a65d08bfe",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "84824839e6ce483091b713747f70f1da",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "0232fe12ad714e15afe1b8253be9fd64",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "c92a99c99e8c427c80e996154a3b4fe5",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "e5a09f10380c4198970f746bb713d5e0",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "83a042f9826049468cd586aba2794093",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "b020e6b6b8a74f8ca26875b3254d6dec",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "c007d471b8e445c38480fe91048e820f",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "e0c4b006fa5e4b9fbd510d59d91ef45a",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "56d9b9bb1df443c3acda2097c4c81149",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "883a657655b947e3a2fd715f842f9cae",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "4c9bc35beb5b49889d8c3e8a41ffcc1f",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "2dc7825734d546d2bc3530a730dd2601",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "b7e3fc4946dd4a29a322942518e24b9e",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "ec59ebb81b39460ab36d2cbffc8d347d",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "4036315c15eb4a98adb7d1d68124e497",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "54ce264b1e154e76bb34620d65d2b01a",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "896b54fa2b3e4cd9b0673abcdc2ad4bb",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "1d37962d01bb474ca4b6f96d93c12887",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "786e9f1d653b4f159d677c8b25d24a6b",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "4e453cc0a89f4a00a3f85ef3ec69fdda",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "fa38b1ee9d624a10977369b0c57045c0",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "d4d10df90d714650ae16d108bd00d28f",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "9e5ddb194f0d46028a38d66539f266a2",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "507d567dbc124af5ab8e55744401ee4e",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "fd971fdfe313439e8e90621a49409b02",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "07253dd2585e4abbb47283c1120f009e",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "cd64164bcf854417bd50e264084ad97d",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "8314791e69f24243935894851aa327a8",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "3938919c4687436282c2b9b8422a2386",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "e3a048032e334c8595b810e52be4f8c9",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "30ab7cd7b41d4b29960577b1a5d3c035",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "27770a9f2ed24b3ab9119d5ebcff3527",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "f06bf2e795304cd1afa58c9f3651fd03",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "2e071e8518a74f8bad8362f49f1d2295",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "c14a360cdc6445d7ab38a1b021d09558",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "07f0f7473c2d40409a41f1dbcea3be64",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "c0e5c10acc52450cbb22ab34ffefdd3f",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "d9dc07aef0e448aeb7b9ec23c175ab08",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "6f2ee10ae3144ab8881204c96c849c7f",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "73959fe0df904248905a1c1ed476c628",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "d4eb2e205d5a4078bc115c7661c375e3",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "5629420260b74c1b9ed648d7f4729a68",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "334325d1444a4e35826480f34a954216",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "f38ab065822f46b5baac0b260fff3bd2",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "8a184cd19f934a758da25a4b189a81c7",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "9bfc19382c4044268327f8f382d2e1e7",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "7623ec23d82042c398d69ae3da1bab3c",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "3a5bc4db5bea45ce8491e75dc5a1abfb",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "b24dae9b099746a4b5206209fe9b1298",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "b2d88ded34eb45608fc2d0bfcdba60a6",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "fa8ea107fbc0416685cc118214c5b0a2",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "90236ae7e2ef4f8ba10b3871deb23d6a",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "5d0de15ef88a443984f824a3d6091ff6",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "2adeefd4cace453db3cc2cf781422712",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "15a870c5c8cf4dab81f05582e6866345",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "09d790b212584e36a87d6b220d7ba539",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "575b0bc5a182402ab62454c74d26510d",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "fc0e8b4373974fa8b9e4093ef6b3a073",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "12dc498e9c4a454286d85d076482082f",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "2e5bdd0eb6cc485e8d47a5ef68abd7d8",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "a72f33390d98487da41f4570efdfa0ab",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "735bb389304b4933b0c50e9461a8ddd3",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "a2f5e125d91c4a3eab6931ece0485b5c",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "f45832a08a7e4bbfb1f87b1972b8d725",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "9ef1133a121e45b69613f01d22e84c33",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "6933f16cafa442d4a72e3b9803c4428c",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "a4367584cdb84923a8b969cf67a3da10",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "4bffb19a623847e5b3eb88ffb46891fc",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "1908851b094d47a1a87d2c3937274c70",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "37abffbe7ef94cc99a89e438a24cd109",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "68d8eacbfe104c73b3698c50fa3792a1",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "c4f38c061e7b48f392c4633e64d8781d",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "74466e85138d4bde97622bc55eb43130",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "c478059c6a0943cdb70a9eb7d9f09514",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "de92e04f348c4edc97e6f02de9dc2ad0",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "fdc1532a7a0f4dd885026a167fe0b199",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "de71ee16bad446e1814bb2081bad7adc",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "f9e2d1f8708c4ef2b217d444262a8475",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "fe8101223dcf499a93c21c2475bb3ba5",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "4a0e4dfe81cc4af9863d7f9e4c3f7ba4",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "5a9d4e99891447de9344c4476d21c376",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "add827b827504806b087622c35c8d3ed",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "69f7566148b34cdf935ba1e8401a15b4",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "ae49dd65596c4d41a6bc97ecddd63d8e",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "479460c87b6047acb1a24e22fa504c55",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "f0c2a0f7d8e846bb9907e2eea47eeb70",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "3049e985e6294939a75c77bb4bda2370",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "5f941241cb1d47318bcc8dab6db36a23",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "9cb3fdf148824214a048669c5eb5da1b",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "66b725ff7bd14c41a8872168dce916b1",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "118ea10c680d48e69aa1cb8e5dd34d5c",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "7a4bc641825146a697491b382dba06be",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "46b570f30148490984e4f30864757c3a",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "142af851333748be91f3d3942926fa5f",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "dc137fa6fac0475fa84d9e96be9d15db",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "f1592897494a463989aaeb3b4164817e",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "3d3763f9a6d1419aaae7d98d08d79dc1",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "1aa590b6ba544102bfffcdfc8f0c7dc5",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "59e6c165d2a14c578c013b444a3eb645",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "dfd9d5d4d5b14b078818d7de15b84e09",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "21e4a92c94fb4c3b870e0cda19ea6a0b",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "95da4ac693ee48a19199e5a24c7e0d98",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "9bfff26b4914470a9bce14963e50946e",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "42e00f3afcf34d01a3a1686c4c47f96e",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "5c983b0fec5f4c72b44e1d2d85e73fc1",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "aad5755148c54ff097bcd8fd4ca9d2f8",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "a5d1079c3bdb4983817693ac09293fa4",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "160ca79522e34a71b79f1127095623a1",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "8c4c1bf38665458ab354e447dc2b7567",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "d67badc401d64672b0c6e4d24b7e9458",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "07032ddeba62474392bcb3a8eeb08b15",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "8644dd378f8844e48fb1259580473ab1",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "dbd5728d18fd4888b85b38b549551882",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "a0aa5a4b3bc44fb4bca933df11a8814a",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "c23be7d561564c3d9c85e99f61a14479",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "a6a17dd8ce504cb38d8b4f98697e3771",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "d73573f21970407cac257d9ac46fcb25",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "90c0e5e941c743df8ed53d6c1ac4c7f3",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "18579ba38e5b42879a70b2ba386cfceb",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "3624b1b76d5b45daa32e29f767cf2ec3",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "27539866b13d4f83a50719656f77a459",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "8afbcee70b4e40908390b09d2970cfec",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "f9f61f6e80da47c5ae049ab8aaca38d3",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "f837823bfb6e4c6cbf90240ce2545669",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "ae98f935f86d4f658ee5ca6d5ddac078",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "dbad564208c54e6eb36201fc51cc583a",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "7746608dfe4a4d5d8f3d51e0f0ae8b39",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "c007a352170c49cdbdbc44afd5276d76",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "955d1e07572d41d783471d399a33185f",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "d4ff06d09d25480286d310720320631e",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "47a98fc19b43400cbc727d460090c315",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"`Trainer.fit` stopped: `max_epochs=1000` reached.\n"
]
}
],
"source": [
"# generate the data\n",
"problem.discretise_domain(1000, 'random', domains=['phys_cond', 'time_cond', 'bound_cond1', 'bound_cond2', 'bound_cond3', 'bound_cond4'])\n",
"\n",
"# create the solver\n",
"pinn = PINN(problem, HardMLP(len(problem.input_variables), len(problem.output_variables)))\n",
"\n",
"# create trainer and train\n",
"trainer = Trainer(pinn, max_epochs=1000, accelerator='cpu', enable_model_summary=False) # we train on CPU and avoid model summary at beginning of training (optional)\n",
"trainer.train()"
]
},
{
"cell_type": "markdown",
"id": "c2a5c405",
"metadata": {},
"source": [
"Notice that the loss on the boundaries of the spatial domain is exactly zero, as expected! After the training is completed one can now plot some results using the `matplotlib`. We plot the predicted output on the left side, the true solution at the center and the difference on the right side."
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "c086c05f",
"metadata": {},
"outputs": [],
"source": [
"def fixed_time_plot(fixed_variables, pinn):\n",
" #sample domain points and get values corresponding to fixed variables\n",
" pts = pinn.problem.spatial_domain.sample(256, 'grid', variables=['x','y'])\n",
" grids = [p_.reshape(256, 256) for p_ in pts.extract(['x','y']).T]\n",
" fixed_pts = torch.ones(pts.shape[0], len(fixed_variables))\n",
" fixed_pts *= torch.tensor(list(fixed_variables.values()))\n",
" fixed_pts = fixed_pts.as_subclass(LabelTensor)\n",
" fixed_pts.labels = list(fixed_variables.keys())\n",
" pts = pts.append(fixed_pts).to(device=pinn.device)\n",
" predicted_output = pinn.forward(pts).extract('u').as_subclass(torch.Tensor).cpu().detach().reshape(256,256)\n",
" #get true solution\n",
" true_output = pinn.problem.truth_solution(pts).cpu().detach().reshape(256,256)\n",
" pts = pts.cpu()\n",
" #plot prediction, true solution and difference\n",
" grids = [p_.reshape(256, 256) for p_ in pts.extract(['x','y']).T]\n",
" fig, ax = plt.subplots(nrows=1, ncols=3, figsize=(16, 6))\n",
" cb = getattr(ax[0], 'contourf')(*grids, predicted_output)\n",
" fig.colorbar(cb, ax=ax[0])\n",
" ax[0].title.set_text('Neural Network prediction')\n",
" cb = getattr(ax[1], 'contourf')(*grids, true_output)\n",
" fig.colorbar(cb, ax=ax[1])\n",
" ax[1].title.set_text('True solution')\n",
" cb = getattr(ax[2],'contourf')(*grids,(true_output - predicted_output))\n",
" fig.colorbar(cb, ax=ax[2])\n",
" ax[2].title.set_text('Residual')\n",
" plt.show(block=True)"
]
},
{
"cell_type": "markdown",
"id": "910c55d8",
"metadata": {},
"source": [
"Let's take a look at the results at different times, for example `0.0`, `0.5` and `1.0`:"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "0265003f",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Plotting at t=0\n"
]
},
{
"data": {
"image/png": "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",
"text/plain": [
"<Figure size 1600x600 with 6 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Plotting at t=0.5\n"
]
},
{
"data": {
"image/png": "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",
"text/plain": [
"<Figure size 1600x600 with 6 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Plotting at t=1.0\n"
]
},
{
"data": {
"image/png": "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",
"text/plain": [
"<Figure size 1600x600 with 6 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"print('Plotting at t=0')\n",
"fixed_time_plot(fixed_variables={'t':0.0},pinn=pinn)\n",
"print('Plotting at t=0.5')\n",
"fixed_time_plot(fixed_variables={'t':0.5},pinn=pinn)\n",
"print('Plotting at t=1.0')\n",
"fixed_time_plot(fixed_variables={'t':1.0},pinn=pinn)"
]
},
{
"cell_type": "markdown",
"id": "35e51649",
"metadata": {},
"source": [
"The results are not so great, and we can clearly see that as time progresses the solution gets worse.... Can we do better?\n",
"\n",
"A valid option is to impose the initial condition as hard constraint as well. Specifically, our solution is written as:\n",
"\n",
"$$ u_{\\rm{pinn}} = xy(1-x)(1-y)\\cdot NN(x, y, t)\\cdot t + \\cos(\\sqrt{2}\\pi t)\\sin(\\pi x)\\sin(\\pi y), $$\n",
"\n",
"Let us build the network first"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "33e43412",
"metadata": {},
"outputs": [],
"source": [
"class HardMLPtime(torch.nn.Module):\n",
"\n",
" def __init__(self, input_dim, output_dim):\n",
" super().__init__()\n",
"\n",
" self.layers = torch.nn.Sequential(torch.nn.Linear(input_dim, 40),\n",
" torch.nn.ReLU(),\n",
" torch.nn.Linear(40, 40),\n",
" torch.nn.ReLU(),\n",
" torch.nn.Linear(40, output_dim))\n",
" \n",
" # here in the foward we implement the hard constraints\n",
" def forward(self, x):\n",
" hard_space = x.extract(['x'])*(1-x.extract(['x']))*x.extract(['y'])*(1-x.extract(['y']))\n",
" hard_t = torch.sin(torch.pi*x.extract(['x'])) * torch.sin(torch.pi*x.extract(['y'])) * torch.cos(torch.sqrt(torch.tensor(2.))*torch.pi*x.extract(['t']))\n",
" return hard_space * self.layers(x) * x.extract(['t']) + hard_t"
]
},
{
"cell_type": "markdown",
"id": "5d3dc67b",
"metadata": {},
"source": [
"Now let's train with the same configuration as thre previous test"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "f4bc6be2",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"GPU available: False, used: False\n",
"TPU available: False, using: 0 TPU cores\n",
"HPU available: False, using: 0 HPUs\n"
]
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "bedd3bc14a07423d8bb066c0e0eae71c",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Sanity Checking: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "601aac870d2c449fa2cd3a2e2e13ba99",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Training: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "30e7d1a2a8e5492a92aa88b763d75c1a",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "a119cd3fad7e44e3ab2b6dace0907acf",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "aac3c161828d496ead728cf162953850",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "73e43690e8e84ee1988d65143274246e",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "366dcbbf8a894845b79ad3e170ddde42",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "3f407c0649dd4db1a5f81a913de34eae",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "d6178f4c455545ef8fb46a8fea48cd4f",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "e1f20a67ab634da094d213c09d3978cd",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "7672f4ed9d0847b2a4afce5aac76e8c4",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "f8812742da96410bb246f19eda6c5b71",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "d8c378b76a494b3fb595d5cd08e57c94",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "da45f1e7329c49dbab8278959ee67968",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "7639373e22284ba395ce96f18ab3e5a8",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "00dc597732e7435f84aac65aa8bddf4a",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "82d9391737fc4db9a32184a2689c5e51",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "98833f49580340c9be3f74d5c8b14a30",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "740e4fb3a5a84125b848d05bb90408ac",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "6b6574b69dff409897b97190199c5ef1",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "1bdd4c5f9ae54ec8ac7c116f1a056a65",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "68a7d0ea9ea74ebd8567f64209cd6400",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "40cc37b6d4684ce8ae761972d1188f40",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "46ba58637fab468d96eb06a0c1fb5609",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "8374bcfaa1494bcf8ce0d7fafcba2b84",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "267bfdb40af5491aaea69d74702df363",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "581fb4f92f344f1aa0c8d94de0bad98d",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "6051c8f78d4c480b8de183583d0f2920",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "9337c6a9da7a4feaa10f6d6b4d6c130c",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "458eb179245d4e9b8280145ed32fc7c9",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "294d2a8490864390bd71491c42054de7",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "2164def6a6234cbca811be405b9bc827",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "78e4c15eba224a2fbcc2656f5c47340f",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "8e9797e968574a7aa92fd10f08068402",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "1899e9a3db494818b6da387b68dfb4f2",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "fb4006cee1484d53a3db78dbf5eac964",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "e4c6d6eacb2e4914acb9fb1972d15ab2",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "27c5cc7635e8474db5b18df16b36d0b0",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "1a8025da24444feaafac2d22e0aacf97",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "0ea8f8df6b004638b58b1d53501694bb",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "730a42e6b03445acb9182cacebda3771",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "76b45525617f416bae20116b3eca2dbb",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "52f3e5f8acee4e2f94cc6a9c4bad6960",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "3e1fc3f3aeeb48c994ef8f1f8c8a578f",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "b2a6610799d74fa2aed45d10d34a81bb",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "2b18ce2b1f5f4e9e9649a38b623d8a04",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "e078bf8f8a0841d182247980906b9cc2",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "418c67398c2a41359df17a7032786529",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "49b446bef97245f1af871fc7bc314753",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "d71cf9321f3d4a059ad4757d1e3d2e84",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "65ea9b07c74b46f4afaecc6fdbac90f7",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "1b889e013e3f49aca91410d3de802a8b",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "f47bde8ef42f46dabd58855206625ac8",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "0d321e034b414c9795e28bfb2d4ae341",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "6dd4f19d53f741e4853a0a9a5f214c7c",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "4db264c214c74ca98557f8d4c0617d4c",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "6a7b263d9424487eadde04c6accd00d8",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "36df75cb80774726b4b314a405e996f4",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "af00ef481b594f9c8e9172de44eb59d8",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "a388ede992fc4dff80e4c5999e1b0af5",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "7740b37a54fe4bbeb1cc347c78a23e61",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "f639d8d468144df69cefbbf1f05157e0",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "7f412f5862454911b73933f3c257f835",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "4042bee5fa6443c78ceec7a5f5c76619",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "e94efc5006284f94bf330c8e2d599131",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "4fd62fd3463f4675b96374072b4bccae",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "e82f34fef4554527a2676b176423bc43",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "e4c523d4eb7d46229fca779a077f7ddd",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "ecd34bc817cc4dd3aef1a1ff60c7eda4",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "8bf738368c0a4b6eb38f239b3487bf07",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "883058f0671f405baf130654ee173f72",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "58f2049645984028a86e4d6ea49f810d",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "88cb1c8a905944e4b4e3ba08602b0b70",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "c2d83617ece4488298f308c337f57f4a",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "dc738b08a41147c4aa0de6cc04c63e09",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "3539d874c2c347fda4c804056e2bc3eb",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "befb092c2f674a46832fbee2541e0798",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "5d775d30ce5e4dd197d4a5a9022aa6f4",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "012d9038bbae470b8e7cb58f1ec880c0",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "71c9a196253447bc81d38c013fce98a3",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "3221282949cb447581b36055563171a0",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "e45843c255e24afaa706b9a75dc9b0f9",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "72cdcbf4b6924a4ab9b8add0e68bbb9b",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "5a199f2bae2e4fd69916f32d8ad3c527",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "9081b994345e48b7965221200191cdac",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "894786010c9e43769a7fee5532e723fa",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "843e731825644b3286b60e777c0f658e",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "9ab810b677ed479a958b0d9838b2c734",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "73c817fbb0514525a9ba0741a20fbf9e",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "e9f64bb67b6a4226b670ffd7571597c1",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "d2e2990bb88c41f29de5852cdf8d8ec0",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "72ec0e3e80854d3c9e08545a3b2f0aff",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "5ed5f34fec75462f981dfa8f3e1f9c24",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "7301fb1921aa402d83315674082e2e8a",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "6b726e142e594976a8292108f91bcd10",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "bf7dc652c6d545e8a41007ae5b08f5d0",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "b9d1cb38731b413ba6f16cad80ced998",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "95a36ef387694beb8b295c497046030f",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "21ebd4a2917145e2939820494a897545",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "ea05d8d3ab364bbf93c2bcff0b9a079a",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "48739614a9aa4e78a65e4d612591cfb1",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "4ce2a095e92e404d90084f027787dbb5",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "d8c3c8dc3cd3486fb4b33c861f21dce7",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "0a365d8c91984574a20b6fbffd8e0593",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "3b7a0daca45c4a2c910fa8846b891966",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "2aeafd0ee116458b895167838a7457ed",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "d6ff6b65349144af9bc6decc9835b85b",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "b785dc5c828c4978bcdb91d0e39ee9e7",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "8c09c90d598941e4a78397ec91cba6da",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "d207ff2e07f24535bec7f9062e18988d",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "440815d353df4c2a95bf513a795a3e55",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "e8168950fc184da184915edb433c814e",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "12d5d60e337543e2b503c775e0cee811",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "5dfeb276584249c6b588da403b510c66",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "a85507d40fae490cbff33f5df7a0b219",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "26a561bd07054aa09378e64090414780",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "487f4c78964944d994bf4529620b8878",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "a1e24888964243d3a80e674172a5e148",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "769576dfbc724b8ea0071dfc7b4ebaa3",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "d91a0a6fd31e4940ba01f00665ad5a81",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "2333d8442f424fd3bbaaad9fe85094b5",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "b65c179d65fe42928e58a19e0e134cca",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "d8b53626daf04ecf87f6900af04492a5",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "57e0f1ef1a75488ba84afc2d3ec95108",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "a27f9b14d45b4e5e920659638e92e9a6",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "8f2c9e41d6b5480bb8a316f078822e37",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "cd16b1fa4cb240a5ab96466f07d677d6",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "1286555627f44751beac02ed6e200c0a",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "f83cd1db46ed4ea39034af5b4350775c",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "5592dab9fc224c1ca22d3c5358d3cf05",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "b1aa49f931d74a21adb78be894d8fd52",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "89430f0f0c3f4065bfd47a68914589cf",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "e650a2468d2741eca38633e75a58c9e7",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "772c139aa23f493fa0920a113bfba0db",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "85088a28082444b2a9be0de5abf6b161",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "9d8964039baf46f0a04f8fc4673e4214",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "8b6c4ca368ee481f9fd596dc2ea3922d",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "0a7c18c2c5d64bce8b218c11f3518b51",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "abbd7eba67e549168b0ba9b0b9aacb5d",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "731785a453614fd2827c70152a77d38e",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "1e76a6760cc44f478f4c67b6046edcb1",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "bac09e6f93454092918df2d9f2374e69",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "89598ef602e74cc1a05bd24e75c14b7f",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "ed085b88a6394fdb8fde94cc074d78a8",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "bed995f798ee418c9115fc8f4e8f6922",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "e663ae55f224449aad8465f3cfc96c18",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "890e5d4080784599a0e198d6e5c0b9a0",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "e05dce0ca9a64240a1d6e102a9f22b07",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "f5d2485a9ddf47ed9dec13cf2119b31f",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "caf132f21f7d449883b27df3a54b232e",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "202c1d002e7a44ab98b79990ee33c0c1",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "8617d7eec58046d5b82dccfe500a7ae5",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "f8aef452c27a4d02948ba29a75e89efb",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "31283a34a08c4533892220eb627da6b4",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "8e005e98f15a4659b97d5cbd6bd9fca7",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "8fd3ce9002c7497ea334bb8496f4b80c",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "0a47ee5c2b0f4f22baf257faa5e936c9",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "bae0a634ac3544f0a623fe5172c67f7e",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "313277a0c1e049998d900701ddc53211",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "77b23f5a70534cf78cf4a97f076ef776",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "89260eac8c754c5e8286d1e1a62566d6",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "1d331a9d20a94a1aaee4013504a8d08b",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "3194dbf4bd6245819116c48df0d96c74",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "73776778e0014eddb779f0f05a23d6c3",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "66cf413d252c42d4bdd762ceb5059ced",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "0f5dd1de51384cd18e3ad663811972b5",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "9f18476ecfb2427ca81d6a3d9b0f9807",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "3175557151b04df18a02b2aaad02032e",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "810f694bf77c491cadb8b36533ffbeda",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "de90668269d94e1289da2fa941b86acd",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "ec739a6695404349bcd5f978d87f97e4",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "ce9b29ea4f254ecfb6cca55bb6c92baa",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "ad0e5e90ed0d4edca85d25a7f35aa8de",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "1ecc01d9e4b04f6e8206119fd6e777e8",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "2ff009ee34514f8a8bf4fc18f62c698e",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "3ac72faa486746c09f6a6e632a2fd982",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "509540ef72b540e0b2ca1bc6ad9d54a8",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "8826b7d2c1e6429d95a2d6e71d79d783",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "e3726bcd74c44d8285bbd09e53591bba",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "2450a9d082a943ea8c25bd469b248db7",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "4b81587987384b6cb76958d96756db5f",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "b2525488a80b44daa3f5e08d5693d4c6",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "100faf02253c4459a0fc5b6a809ba959",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "576aabaf0e2148a0961a576f72443047",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "4d70a7cb37a44af083bd245b5ade1682",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "85de2e2481024897be1ca42b93b1bf8d",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "77a9c54227e64dccae50d7f3a32f3378",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "6d270ec78b784b95863c4ffbbfd890f4",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "889482ab97ce4543bf66cfc611ab1d47",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "5ade71a1caa644bdb8304f9c942feb2d",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "ff2792278e4c4fbe85073e58d787379c",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "cbf28d1f72c74ead9d50bdb36d9dbf85",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "3fc7c48c07be4b1899666b70b20122a3",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "13c83bcee3ba4fefb79bf912d7906416",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "4f5a50ffddf1460c9c32d8fc55b70518",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "a5a6f134fc6448deb2e7506109496a29",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "2e790be27e8e459e855ec476d1a5b588",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "057b7226e9ab48cb89e99e34c29a9334",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "24b0bc8aa18d464e8ea9cab0fe658e7a",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "dadb2cb099fb43fa8e4fe5abfd138796",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "32b29d8c20574cf0a7dbe7822f7f44ed",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "2ee588a1d9dc4d2f9f96aca3ba621488",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "2d851c4ef1594a199905aa49ec147bcf",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "85cb88b89d6a46a490268abe36f4b2e9",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "64a1be35cd1f4c60a69830ee55787b24",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "7e9421e5f8cb499a9a5500cc10e1dd23",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "fd8b536ef2744ad188981ea74043a999",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "878a3ef425e74d928ebeeef56dde0dfa",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "b50d79e3957c4c4190bcf43c3c0f40cc",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "8a64ef95678e404eac61267e75c6c0ef",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "fffd05ccb5244575ba420e62162e8bfe",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "54adcabd2e614225a3b6a6d7e5c51b6e",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "26067b3c038b476c83de5ccc4a0c99a2",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "2b2c546317a540b1ab288af2685a1cc9",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "f3f6d786e9b8425ea19cf8c67dc545b6",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "495007e80a8f4c9596aeca58af268daa",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "3609e03c07f0417a87531fa1652c385a",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "35015d16dc064427a646522e06d39d47",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "88e24d30af4a4b3592ba6af4f7ea2aab",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "97ffd3853ad14f7ba908850258748240",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "6c366431505a4d96b4e013e98cb252bc",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "068f0f7645d04e2982e13f726b6f6bf4",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "da4aebed4a5d420cadf8165007438c1f",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "cb0113c36710471fb86d792508647ada",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "8678d56190584f36ad97263e4b46af79",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "ad6406f07c4641b682f1f58a04c7c631",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "9a9433f09673445aa8744be50caf51c1",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "a136cec1818749e78e34c998436221db",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "55d651c0aeac413890fbde98965dd1a2",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "85d776775dc647b5bf144fc3bcc41f6f",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "ff060948737e4b9f8e79051c99ec61d9",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "9670c40e38054b6a870d0f1899b0d91d",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "d4fa543422ac416b87f0d1dc009bec70",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "566a456a736f4f538ee0c1c369ef1bc0",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "24d549cc14304bdc909bdf7b8f684793",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "80a768e2d8b845ce80f751c8b8b23165",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "bceabd2fb9bb4bd38be9e4ad1b960660",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "cd916eea060241f48ec4c650109e9f09",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "eacbab331a5947539ed578ed97a35bd5",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "b185a8d5a78c4366a2ddddfcd86adb66",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "7e6ff3f74e4a40da8e1b56bfb76f8a27",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "8df148f77fba4e3c8f16c8a36725aafd",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "4a7e412c0f024c778ae1b7e275ed065f",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "56bc540b2bdd4e2780e3f23780309168",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "86b192e12ca54b57924a1beb6456f819",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "bed6ff84725345a6b596fdf87d2fd2f7",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "455eafdef42a494fa6e8862029f15f36",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "ae2de45a2bc34e4c88b3b2e0f0e51a89",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "1639ff7b62e5494aaa8371e79d870c8e",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "be43652934c44f9b896dc774225a0d9d",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "401da8cc0a6947be8a7041c2068b9b61",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "b87227c5a61a407e99bf5d53f85bd75f",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "e94d7d0bb8f940248a0a99e5117f387c",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "699a2d618b2b4a6eb60aeb9e353397de",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "b8791fdb4fe9424a917d4c2c05ad6ca9",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "25cd49a0e403434799712e4f4b159180",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "7116096fd7084503826e2251dd8eae49",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "9a20e11a8cf64aac8fefc71cd0e8472f",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "8150a18122b34be4980bb192f98998e0",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "e9ae0a9d58d5484fa55643e3c49cb529",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "379bde8a4b414283b19f6f357d9ff81b",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "5e755aba377d409c856b9fc93fc821b0",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "0a9f51cd632448fe830a9f229929ec60",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "8783a1225100444da818dbe3ff69267b",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "8856b12e89a04aef801719441ad86426",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "d3ae0603b198456fbd3707792817e4e5",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "f2aaf544e77e4418a199b056bf131f71",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "02ac3eb2f67b4db1a23335717a6e6977",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "6ebc16ba25074f81b2356424a92c1815",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "60c284f1571445be9b5739a5d6ee3fbf",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "8840a965ee2640869e3848d863b777e1",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "9749e4353f234e37bc34ea7e461bac13",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "77d1efb39f3b48a2ae9bd4c834818b2a",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "d6e73f8c82644100a5f1304dabe86376",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "dc94a1c8f1ce4d8bbea5f6687fc3853e",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "67164d2f8d624bc3a12c49e82b232236",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "828368ca1424417eb6382877a8d61c0b",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "b796770fd44a4c0788710c3f501c0492",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "a8b22d3870af419bb93fb357e2f71135",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "dc79658772034e7680cb93e2dd2dff37",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "89e41754ce2748919bc79f5a9b611e39",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "76b297a0ba7040be8cc60139eb6ec4db",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "d2d816cc22904b0f9a61805fd2b6f2c8",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "c79311baac5c4af1bed076e62321351f",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "3c6d517d11384fe0aeb7a9ef8b79ae3b",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "b4245310306f41d9a2599fb531b7a9cd",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "499f8cabafaf4f94ba2c20a4564cc00d",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "813e03d5a7e4429e94f5c3f823be2b41",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "532034b7d7f649f3bb90d0fe0768d04a",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "865c47ddf4884ef891365ca118454ff3",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "f41fd89e6070411f8a09d38a29d2d82f",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "aa4006a92b404c9faf0aa1dae8c0ad93",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "e9175856b30349f58d9c2366d631d452",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "d353800b8aa84a3ab60906a4f2b556da",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "2099d4e036b0483c937da82d516ce676",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "0bee9805867c4c7da26108c2e37ca4b8",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "0c4a6b6c2e9f4af1bbfa885fc28ac967",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "b029d963c40441598e54662c58906a31",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "95084fc8d67e456aa27db6ff7c6c1433",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "174d5d790f0c4c73bae24363bc078bf6",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "1b226371b9a542fcb5fe7b8d433dbd08",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "a0d7dca599e94534a40d324e09a99dca",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "db8f04286bf54a1589d3df4a69043bf9",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "05ecc684f998471dad69f6c296598007",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "0071ea6708104e3d94f55e6435b830dc",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "49bb3c6ed0d8475ea89f6e9cd15e6c96",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "c443f2465dcb46f59e3f92aae27e803e",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "001ee66be01748879224f361d4dadea9",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "7cc804917c304569b236bb7352a83482",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "ff655b386b3e499a86003daa9f936a8c",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "cc6916b874604bdfb0e6cdafe59a5014",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "d40f4e295228401a815bdb7381fd4655",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "1872d1898186449a97a4f58218d2b263",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "b01a9137702c48a9b631b16b1e50f5ba",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "bf11f11917354dbb90b8b511863125c5",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "79a5c262f6884d5db954c8bff4e8db90",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "b6b838ab261e404286b883ed1fd1f956",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "ee92d9d05e444cdebdacb16d97c96fed",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "d593c1d9f8aa40aaa4dd23ebf3c0fb0c",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "9137ca9dacd542f5a84130fe15f38e15",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "f781992b149e4464ad129f434be7a241",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "020d772871ed45f1ae3d4840284f3f3a",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "0102d9fa4a4544dbb42da895a5de540d",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "c9594d523a0c45bba69927506d3f2e9a",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "185be2ec3bc34682930474eeb55b9a27",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "e575fce1a5094728a5da35c25a4619c9",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "8a5a23da50dc41bc96281c28fcc547b0",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "b74e8d5f8700413b8829da5327b30964",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "7a98b8dd467e45d5ab84240c0f5bb56c",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "cc1c07c4b3694def9edb9e48e3eb56ee",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "0a04624b9a4e4bd2871322e1c7ed7283",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "b0d53ff617164306aa15de025f1fb652",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "b0a3e63da0784e1ca0826378f122abf6",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "d3a0efa79c72440ea055f71f9d1e4878",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "2294a61ecdbb4df8891ea1f0dfc3228d",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "460e1c6f22b74931b9ba93bd4ae75d6f",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "4f462a771a484dfe9ce60c45c3d08ea0",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "0f18f6dbc5634cce9b1ad06e309d5562",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "2f9207f956a34c7495f66646115ff563",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "6b32ee492e844c9b93de0f31169c2b17",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "3e0aec09d5d74756be5a3acd89baa379",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "415de42b70844f40845a74d3f9a493bb",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "e77eefa18e5b4e17a66c868a170ac158",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "5e9d41326c2249c7b6335b347a65eb02",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "9929a004e1aa413a906ed9f707b113ae",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "151bfa5c4f274789b9b5a8b8ec30abc7",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "0e0683ed09854b228dec9fd180da1742",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "67e1687a94084e63acdd4ca18db0ac6c",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "5d258a88d6454e0fa14a02176c913fdd",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "4ff3199385b641a5ab189432756d0aca",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "be27faed83c9428d91903e7387030bdf",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "786dcc71dce94a63b50481c73088bb6d",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "cfa6b071f8904c81b025704375c4418f",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "19bd77b0661c45628e1a9bec3bf925b9",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "671049ab1d574475b0d0ae4914e52660",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "00ab51ac82294152aa12aa3e4a4c9c14",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "5c1596e3452a4d59b4eb68e005268b36",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "5b543485a66c4feb8e2834fc7dfe3526",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "38e1bec1db994e55ab630b773b136fed",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "b16a34d4901644c89c391c4b8bd05f3e",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "44957e8fefa6482da0f23f365b6df086",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "9ad10f1c9d684157b08f942b16e60c4e",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "c209486c59fb45e4a8d17d22f8e83760",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "d1f673d53197458a82672bef69e1d534",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "03dacea588214c9e9c0754d18a1f5488",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "5e62689fa628489fb9567666b9788581",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "76fc62d0ad164c63bef2a200af667be6",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "004d66e939d5443aa31e160a09f9a5aa",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "b08830c7187a4a319e29c120f81ab0b5",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "2b65aa1511194dec96570093446edd32",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "a6dc67589c8746ce8f6ade3aba9541db",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "6712e6abce6b4f28871e7ac33b66b047",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "dbf7546a8dde463bb5ec38b467f97815",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "adb9406b42164859baeb325f83202441",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "e3556e1a7071479b838334ef80fcd0bc",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "1a592fecf7314dac89a4ce00c24f98fb",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "bf73abe6ef27409d9cbccf25d68de692",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "81d76e743b1c447bab00244d194c0371",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "b39750f96cf348fe9673044939b28218",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "b9b97aa7d6984a51a11396db20b0307d",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "e461515b354a41269b922983a9e76de4",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "325d7b60d00d4edd8ef7613c2cdb15fe",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "b8305284c79849e580728baa47fcdebc",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "35d3b94e309340e7adfa4fe261f057da",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "70d4a22e0a7e499eb1d420569aba2afe",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "1c7b184204b14d7f911dba406453151e",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "9c29764f121b4098afec6e487f2e57cd",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "78612c06562749158036d4f3519e8d5d",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "35fc0d192e7f4c4fb62b91cefb84eb19",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "4337323875bb4d4fbdc205404c55fa37",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "454390ab4790449bb3cc632141b685bc",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "17964e4676424465aab3e5e2e01b0bf2",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "e84630383471455e986d7cda5a40f0b1",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "f80237ecf0f3462ca02b4a11129ec351",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "f8ed8bb6f8364a82800284a48f31d4a0",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "589e3a92c18b414a9ccf9d9acd2d0088",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "54a484c1541e4624aeb45e15fe06f27f",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "776d8d302e484e4bb48d61a2b20118ef",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "b42c5ff554c34a3f92296be0dc332d26",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "94cf6877a3754844870c0f523f2e16b7",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "3f8e4400aa3a4f0a8571f4805c2465fb",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "08489efc07f54d0298424c310a7256a2",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "fe9f9e3a4a544e8b94ec3796e3b6b3dd",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "56f147c4f53f4d2aa72904dbea8d85e9",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "42c70f63a2c1461e96adc3065abd5470",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "fd1ad2362104453b8034847b0e53ece0",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "6baaf2fbef4e447fa74189137117c0fa",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "870e240dde424613856d2d15ded9a022",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "9e7c1b7cabd14b958be21e482946809a",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "e06fda14dc46498aabee7ed285a945c6",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "f095acf247614a2489f312a9a1a6cce1",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "b1ffa659d5514ea5b23cf8fad3f54386",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "6ecb7c4dc66d44c09f674b55d3ddda57",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "df8d7434d4db4adda9ab2f7b8fba659f",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "f9a97feb72c54f2face2bc89f5631ba9",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "f3bd94c175194be0ae7b25aedf6bd105",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "74bb8c156c8040d6949a06af3d3d25a2",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "6a4e5916ebf241938990b674d7b84675",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "7bd6cef7efb74ca494ca246262807437",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "6441128563854914b02b4d0ae0b85cfd",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "501917893d1f4d60b14454b8646e8e5d",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "74baea5a8c404392a037a000d2197bb5",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "9b74242a7ada481498d18275f6ce0f4b",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "7e559bd8eb044a69a98937117e40e60d",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "c1e83131348449be904027bb29473a25",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "2a74795ba65c4c959fbd24d519f848b6",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "39f9091095a745388cd473b0ce9d39cf",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "8c1e9b19e08f44c19bf8290291930eb4",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "ffa74f154f22411db3c163d28d2c3ea5",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "46c723973ab04395b0558fed62b5f87f",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "38f398b537044999b56762030f9df4c9",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "8867b2b66c614e509876ba40d122f5ef",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "6724c94e99a84b898eb78a9279a424a6",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "f36c632dee4342a69da7ba0ae2c9034d",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "16f160fde4c547deb830eace5cffba5e",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "9a8574b48f994c46a90d0e67b22b06e7",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "b2fcec3a1eae4261b43fc1d5a3f2701a",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "1129c27500e34629a9bc2b91b0a72c85",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "9e264726b4a04cd480789bebc5d475dc",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "b65d5576d42e4e66ad027c2f8ebb7f4f",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "8cef8837e2e748d987997a12eb221d65",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "4e2ce75cb5b04d8ebac7ae094031c7d8",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "48e796e47ffc4265b9a14cc6eb299688",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "dda60f4f9261445184d4e280fc1a33d2",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "ebff5493b2bc4047bbcb7739bae6b6e4",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "143844834da342c1a809c5b3cf9e9e56",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "d6ff3fdf80084684b559e14ebb6407f1",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "560adfe349d148a9a325b144d08ff5b8",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "8fc9132e9761480e8b0425e0a2eb891e",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "6f8a21c4541744f7aec20c7a569eb3d2",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "1098ae190df5468c8992486642cda62b",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "c1202d83273f4879b8b5abde0684880a",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "7f9b363feb8341f7babb76a02d7f9678",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "231523f9ab9e44cbaac9aa71c97672c4",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "9a3753b5b0994a828b40adeac695780c",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "dd41397ceebf455592d2e1ac80799a92",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "79eb4b67e8ba49f4932a5a9a4c1aab04",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "f76988113a4149d6840c2b16f0971b47",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "e6d44ef569ef44e2abdc1c77fc34bcf1",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "5da22550f4774341ad2ae1024f2b000e",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "2daa54f553ea4522ba1696f876fa98f2",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "807b2fbebb814c9cbb1b33e5f7eef4f6",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "8135d650e96b45adb1d6ea5e8961ccf0",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "7797f5a80efc4ae88b28b82618fffae0",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "974d5ca0bc364067bf7dd5bf1f3e64e6",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "6517726774994a2cb1ba2a2553533124",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "b55e843e5ff64fbfaf9fff1a9d74e688",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "fec598cda3864fc48cb47125d41c5036",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "bc729b6fc0c641648356c4b6c5ef8509",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "93fb349b140d4c4986e374223cdb0f2e",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "baccf3d9594b42a394f5cfdef38dd745",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "fa7d95ee0a9b4fa397e5579b9c8403ab",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "dde41da42c9f42f6aec439d106894af5",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "9415b246174248f7b25121450bbc7d8c",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "c3b19660ffcc41b8a6f0d86645e2a9f6",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "639450be2ab54e2cb37c54bb98a8f42b",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "ee60ead0f55149a29e7ea8142139f9fe",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "96beea4b87924cbba006959601668700",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "bb025f5e7bab4c5ebe6aa9a29626975d",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "1a7aec6b711a404f97a07a4d2d0271cf",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "2b31655b2560498dafff10a695123d58",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "41e8786542e44ec0890c7b77062c9f23",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "518cd88037174c5a8221cf2fe149e808",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "3ad049bbe2774bdc854c6988cd76c05d",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "fa585086f4be4b91a7890ee624b87a94",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "88d54b62fe3f4b899e6bdf4670f0dc4a",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "a7c5d7fd9ab4441d829a87f30acdc7e4",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "8cf350e163b14d1aa32994128ee97eba",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "6abc9a8d97334774be3272329ad7a6c5",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "de214047cc7f43bf8aa37419b2dc8339",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "be9f6ba972e241939bbaa86a688c62e1",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "f95e3e649548426a98d70fe254f1ced0",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "b3b76b817b654ddc9cf2f0b8f51a662a",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "873852d6b7b34e9497834e3c8f2c0e45",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "e694c496f5ed41ea88e2f25aa1a9e13e",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "194652325c684080b62821f3ebccebab",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "75d473f5a6464dd58fb3703872addff2",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "dfc671f29c2145799a0dfdf1a142e31b",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "8816a82000fc454691fd082245831b5c",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "13e9a134eaa94751becfc4927bfe9e4e",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "8a1ba3c0e2bf467a928dac0f20c14dc6",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "0d3bf38c21a74664968453f4728a321c",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "75d55285b2484e08a7abd164ccd9a050",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "f6abfb7848bd4f249447847df656cccf",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "3d6382c54c2540b3ac41701e5dd8ea95",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "827ddfcd23f54dd89e19c5fe78678407",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "15535e42a1424c00a1b28bde3b9bd603",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "a8a3882e7eb3489691494a2bafd9ad99",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "b55a9d0237d64e2ca7e48bd32186cbfc",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "60817aee7a614369989b8622d35a2f6a",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "efb83e053edd472f987695a6352f0e0a",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "a72ddd77a49b45078d55089c914164c3",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "b41cddb5fa1247d68f7b73dcb569eb87",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "a55e9016b80843e888b3feaf322228b0",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "55304283247343e09033bbaf563768bd",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "89ab13a032b8441393473e517b06bb5b",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "cd893a93f27640ffa428f70a60f7d62c",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "de4355e6a50f478383409865b7a95ba0",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "26c49ad31b1a43dcb22d27e1abb346c0",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "63c6a7dcf0ac42f0bec666c87f30dbc8",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "7585d426f4e04673972c65496e442989",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "59e84b53d720452691479f0ddd596be6",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "7aaa91aeae624164a92c92e628ac2d46",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "db91e94e3e9743fba1ed0b78caf80a02",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "5153a8f2eebe4a2ba9a0d5037416283c",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "03ae7858933246b2b874976e08c5ec85",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "68d78a917b7b4a07b3cb4c973c623aba",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "f88e4f7a84ba4e9d9bc03bd572cb2e5e",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "41402d243064430db817e801e40ca448",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "9acc1b77fe95444a8f423268ae46a7d7",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "4dc592b53bb44656ae9ec9c5e0ab783f",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "f4e43fbd44b841b9a09f9bea436efb50",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "e1769061ada7444688db35e94c3193a6",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "4bf3fec777534386903723b992397f6e",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "e0bb6fbc929149b1a6240a50def6ebc3",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "a19989c5929f454f84af07ce601b4e46",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "cd44ba14cc984d06a8e5ebc3b4136958",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "3b9561f66da944e7ac51c1f079259df2",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "78bd354a38e64f85b5885a53b0603dde",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "602e66b4616d44719c04a8bb723dbde3",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "78def4049a774b05ab9e0cd84adf5254",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "9edec4c7547b4a1fb5b406140792f391",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "3b143dc95dbd44519dc6cd519d51cdaf",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "21c04beaac87404bbab1486c1058a928",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "9e113ef49346465e80362c5462c7dc08",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "0f72fd4e99a246ee80436a7cceb5c0bd",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "050f54e63e8c48be9f1011e05124014d",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "a808aa1e99b845a9b965a772ac8d7e6e",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "d98c90ef21ca4d73a627df9b2cc02fbc",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "b4d62756dba046139de68205f4dae4fb",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "78f84ee41d0a4f709b3eafc39400306d",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "1f1e227aca6d41a783156bd1cbf1c780",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "db207ee95f9740748a144ddcd6184c9a",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "435245201dac4a018331b8dda6302087",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "073c0163219847128577381e54a3dc27",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "1e46030eadf84c7abaf77813f1730773",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "48e76bae210c42dd93d7be51fbe6b38e",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "b1fcaf0819434cf9b2eb5facff3cd307",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "928f551145d74eb48667628130269199",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "49acd32ed428452a88b58b98be4007b6",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "97d3a69ce440449780315f9160aa240e",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "2a545897131c4b99b7baec9b6cf9d128",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "aef2f59d59f94cbb8eed67c3be513dd7",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "348975d5a166423ca1e3c15a69dc06c5",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "cd60b94a94ca4248a8014972af1d5f1b",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "6381d433e530407088b40b19cd0e90be",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "af20ecbb69f249f08ba4cced5da05a60",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "eaf03fc4565a421582a6b93e92948e1e",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "6ceb7e75efc94848862b3f4c598c47e1",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "005b8b43b6794dff8f4b9ea2c927ad2e",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "5d8eac28dfc24b15baea7114f803bfc2",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "76276bd0cac84ce8ab55946f1b23f887",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "cd40bc2c309a412ba3f45190734cd5a4",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "21a43acbad4a43cdb335b32d854de53d",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "d3bfbb7dfdc84d7593c7bbf80b189e43",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "42367248635a47b78a03cece6ee35b75",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "21006dbef9a1448e8dd091bac3c75c1c",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "fa64a4f4df7943788736f3958861572a",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "db395ca9fe7942158cf1f0582671c0fc",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "d929b5d771954bdf8778a37de3f421f4",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "465f82e02cfa44c3b478a37fc2ba2ee1",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "602e6e82d5eb4a98a485fcb88ca45e3e",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "a24760f54b34449a874485d38826dcf7",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "358c30ae87144b7ea16704f5e2675fe6",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "f8f385a7df4540579e54313871673995",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "e303805ad40446b8825369516f04942d",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "a97d11a1105e421e8bd24381c09f8496",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "8099293746b643dfb91ec4ecc0d52cb0",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "0f41cd1e0d7e474b8b6997ee6773658a",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "c70e97f114a34a56820ebf4d536ffb44",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "9e845506d3a447cfaa20a14ac0fcedf5",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "696b617dfaa64582907ae9fd5db09251",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "bb6624e915644b78950f00724e694ace",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "395cf1da697347fbb02a8784140d73fe",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "c77abdc025ee413a948426496e25bd64",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "5b51e5b557df46389e9e3666e0fe4e9e",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "c4ec5c6e1681408d92c79344f9288276",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "b3a19f33e639446ab0c785ab5a2e0bdf",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "0ac5f68ac7f047819e60b9f3d54fd880",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "15f4973c486440f5a75e87d4767c65e0",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "8af1618400ab4430ac1e58e6c94b84a1",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "48c27137ca184557ae2c665e8cb560bc",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "4323d266218940c29623f9191411c13e",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "dcf7b37208964134a639c918bbe07385",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "71eafa81d23a41689f390d000ad5ca65",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "d2a49a4dc088474db9cc9ce9c02cca8d",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "e47607606f1b41bca2c8dd49ef7458aa",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "9931d6f86f35452e920d93143c3e14ae",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "b22b064501824f80a0f572dfd87c40eb",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "086ab2e4735a44f6a220f390d4392535",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "c9f4dcce21434a1ab0b8cb6b11d243a3",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "f357a422ea1541579acd5d9362721885",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "61db23b5b185497485cb387b202e6c8c",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "0c19e83546aa4806a7ce2f2a4ee100c0",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "332c3da0191042e9870fece4dc6f02ee",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "576b8d97939e476bbca124414d2c7b29",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "a0c8f923fc4a4299b3969bff3e3506ca",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "646e951812b54c3eaa693a5db8f8c33b",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "2ef289d2281d4cbeb37fcb45f9cc00cf",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "0d31368b3b6c45ef8133923bb236ae93",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "ca281fe5c55c4601aeb3270b0b7cba82",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "57169e89d97742aaaf6f7a8d52b2c599",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "474456148d484ae09eeee7618514d505",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "a639899e22de470986ba8631f06cb2cf",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "6572da4577ba41c3bbebb5ece1146cb4",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "c259f25dde3842098e8188dcfd30e3b3",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "c4668f1a146041b9bee00c2ee36f0153",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "52b5896d5b844c038002e6246192397b",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "ca10723b76524a22bf7a2b21a4e927fe",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "6217bbcf886d41c7a4bdd716e2a72fb3",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "3123f6c2539541078d4122c1db11be71",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "f3eb7a03d20346da87db99a18a780714",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "1488dafbc18a4778a2c1a1c4da0c4924",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "c945833c9e77478e91bf7af4e1327c77",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "8d26cb3727014658a2bf5b14dad7b98f",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "0f1e89521bba4760948d5ef0c3e52b30",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "82d70dc839da4a0a9ca1e63ccae34a67",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "e25c71ddb21c4e9697a48260e5d66c18",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "3c3a2bfcdc1e4b09b339dc6d6ee39fd5",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "d712afd776e046c7a075520ead1489b4",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "33cb7a4511574f4b824c05df15b5c0a5",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "cb4b00ac1d4946a78d8a2657710601c7",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "02cf0f22a2ea417cb93621bfcb0bc9b5",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "149d67a49fb4472aba8b5a7c688f52a8",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "ea3b6527582045e7994d3efde138606c",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "1eb9b3d4b0e24908998059a0f4cfeebe",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "140316e644d142a7ae0391bc86ef2ad5",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "4d818252c2ab47eabe2057cb630c5760",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "02ae574d0bab4574ba5bd920f8a96f89",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "89912d0efc2e4e28af411e5467b23711",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "6600d3f8931b4b2e9f9f7972ea919c86",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "7129383e2d8f4b6fb9596c3f3faf63f8",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "c4ba295d00b94586a95de8c62f22d7b2",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "8c0fdbaaaa5a430da6bbca542ff2a06a",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "497f4c89969e4e99b134874a49eccbb4",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "48ba6c4cf1ca43fab6d94d20e608ab62",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "1c48a0ce2ef84244bf3a6d756268bdc2",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "4ba6f75b857a4a9b850e646f000d1704",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "6d3ee120c66f4d6ca312d11f9a46d382",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "998463c7bf324b238a4479549d4e2181",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "90d568166c4c4ae69d6b91147149094b",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "4f25e7b818ee45b5b50e740cc72e0caf",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "4b66d9c883374ef09f72581e639f07f4",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "b9aa1763b00c4564bb5aabf61c5612c7",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "f5c1931e3e3d4fb5ae1e7dbcfaa7f547",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "bf7bb94eacbc41a483246cffa0ab1148",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "3167f000d8a842889e2e15dcd5bd4e90",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "c9e9111574254db897c44e6bde551c7c",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "523106ebebaa45fcb2edf63d38c94f2c",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "332d1f54f29f426c84f1bc356e38032b",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "04b0af76521a4a90bf7be53b8d8a6892",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "fc833ea561b44c1293c7bd366081c609",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "f00a9c130af94f378bc2395b61a86936",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "f1ec091d85c841ba869a1b4148f0e361",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "442409b7b543493692f4df45daedd41d",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "d328f25d4a7f43dcb76c6b43c9766c1e",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "5fcbee5e5e104b5f975daea44e649021",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "688b1167c8ac4284ba57bf1e249c5bba",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "4fc221440fd341ad810f05ad398422cd",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "23a6fff5908b4bdcb696057160572353",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "b0988c1a5856429fae75e6470c892ce3",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "2b07b520ff004e988e8727a9729ebda6",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "3b3b668e040d4773a1a4a40d656d506b",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "077da953f1ac4a0782abd6de796e2444",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "d99313f861e94bec8dbdb7ac870f9f99",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "de0dafadd9454b5a919f602d07d81739",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "d0184243016e4257951980d56db3db9c",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "a6b200ec9de449f593b8a628a94aefaa",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "cfd189ea0b8f49f3be04d72212a87535",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "1aeca993f22545428fd832013dbe0591",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "6c6b9a3994b54a6684aedff5fb984589",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "2bdbf2288ed243f4bba81e63db4003dd",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "ecc342e1c06b488a886d6f320a6f3fc0",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "54df3d9806e64e13b264e24b54ef224b",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "cdb304e86d9e4c8b9b2621ca522d5b5b",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "102504d901be4434a0b09c78398a1688",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "27ec6a4e63454d709bd023b6fffcf6c1",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "9cc15c9ac5a048b18ec660c360110eaa",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "c93438503dd34b63a0314cb1248f84a8",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "82a1b5199ffa4926a41e1ab8d6ff496b",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "76e5757c3bb54a55a501eb674d0a3e13",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "a6fc7f15240a4f44b868061b40e4f38c",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "abd7991eef4c4f1d9849361a9fe6549f",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "feaf06eb238b41559c889dfb83b1f12d",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "d17c07f19b6f4acd9c44f276ebdccac2",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "6651325a41204ed6ad935f2b7a4fe870",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "ef0fd7b91c47455dab6b33e760375d54",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "5f1ce7fae2414706b28950900880ff97",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "3a42665ca79c4de2a31d8a08bd287567",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "f09fe9e1d84b48a4be71128968155f57",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "c9f4c39e9ed245728acb346974d3c5f7",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "f0c832adce4549278d29a9fa14bb8bc4",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "370484b8ceeb4dd39764e7d6e50ea4a8",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "0062ccd3ec7d47d7ba2df94a3f2ec5c5",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "72e1f0212d55452ba0e4d205d82f834f",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "05c1685449b04f1da56f9d7dc367265b",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "d68fa80ea22344fb89de1a968a145a5c",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "63b51606570541358ac2986b79d65e29",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "b22d709d7c744127bfaad1213824e826",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "2b2d4de20b5a472f8fc64d65ca925436",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "06cf9b157fdd4f5298c3fbb495a591dd",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "163d613f322c48089bda1adfaecfb8e3",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "5b60756ee8524c28a867a419041751dc",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "c6396e09bde34ff39fac65c26a3d314d",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "26316cb2ade04097bcb12a65c2bce1a1",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "95f4412e5d604d2aa4964a703d168b59",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "ec4f83c5d6f54ca7af9ddc60bbaf7f3f",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "044f41d0ac474f138acb1f8b4eedf019",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "c569a8f3556344ef90a11633034a27ec",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "b00387688ff04ca88c2d8749f1eae891",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "438b782fdeff48349709f766d871c2bc",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "8de1e220336e4a5b92b94ec11cbca777",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "522ad2af02e9412c86e2b198ecefab92",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "d1cd6cf2fe164b3eb8f07899dcaf0a84",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "d249bdc5906f4489a29ca4e514dc15d1",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "18366a76cc5f434fb5a186fc74fd95cd",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "0a738a789f684f348fb62c9aed190fd4",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "d8fb53ba37a14f4bae6a13d9132b5e44",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "b6004bab24d74a9e90e93216742fd98f",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "2d86160eb69f4fb386801279c0c920a4",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "044fa47d9a0941e8a2a8ebdc9c1cb980",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "5b7626de8d6345b49b8639b8420cab90",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "a5be1f386c814a9c9196df0b4cf3b2c7",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "f492cedd663848e3ac2b4d5b5d162c88",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "01c20ed6696848089084f2b3a270f3ac",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "4c3cadafdfc344348369e6328f015d19",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "ed182c98b68b4c31b5a623a2b0ba3760",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "95bc6e8d1caa498a8406cc8df9774402",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "0602060fae5645aa9da1dc5a0b4a3caf",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "f1ae61a493a5414ab3b131b70525b3fe",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "e4060c572c5d4e69948326b9be7512d1",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "16038242a7db45b5a5bedb9308901049",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "c697bfa3da054cd5ae0bdb2af7200576",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "d89b940d03d34b45ac93b58462ec4f2a",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "3c5b89bdb16e49d4a4513c45dd4c4516",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "6a1a9c56a82141a786628b0baaaaa23f",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "b902d38104664d40bcd1c7bb3d7fd4eb",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "05a586511ca841edab8016c0ec414879",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "c17687304f7543c8b24797e2f1223de6",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "95f8e4e542de42369ef8bba0a7fa5ed7",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "0d00fceab7664d8bb9e1faa71c5d5afc",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "5162a14ddce5430db021e9ef172730cf",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "11ca61b579a54601a7ec2831878899fa",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "afc76b677a3743e79c34578da92146e9",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "603fa68c4e444564a4d09fd5a46c5403",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "144e5aaf274f46c8b89f55085b8d32ed",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "be84cd1080d141db96cc82d51f64be18",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "5e210e9b25c348689aa6a3012842d3c8",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "94d08b28f134495eabc21150bed44ee9",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "7874599fbac448e9a2012ae9d9503411",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "a0a6f5d9720a486ab54bc8e73b773a46",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "fe990b76a3df48718c77250612abf757",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "0d2ed2fd398142c4af7d08aad5cc7208",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "e123c02e30d64e83982ae7273a64eef9",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "0f905509bdb249a8a0d5dad010e5b4a5",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "3f10fbfdaa9046bea3a65fc8e0206b1f",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "06ee6b284795475fa74e3671018a9acc",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "0c1d5d96fcb441f0a683bd96c882146e",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "d997902a6fd7494fae249ced3b3ff20d",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "1d9c3783118d455f9ce4bd2161c23ec0",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "91c7b2720fb249fc8684fe5428e9e752",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "c59226d9e0d242b29c87334597cb778f",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "d9fe28a846d5487888328c302c38e729",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "7a866caea16f48a3b9f878b0681768e8",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "ada774b773c84c16aa9a6baf79579fe2",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "3395e8d97d864bf892cf3c39eb3bd792",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "f4067c17087d402fbbaf99c37af1d3f5",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "3f9b164f9eb24e09ba754da2bd06e631",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "3c75dea2a7cc4184aa8bf7668c3fd1c3",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "26ea885c4744400bb77327c3af5a162a",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "d87767de15e04750a72fb3774fe5f276",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "200c77645f1947538eefcf66e7ce186c",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "998915b3086644a2884b22c72025a95c",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "6f21f0dcb1ec447297b8f1af6fc3b242",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "6b8cfdcf769a49638f49626266e9a565",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "19628d2ab56a4017bc6ad444ac6b967e",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "df6c2760c5f14bde89e58d44ab698ada",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "634204e20fdb4e6394ae665de0f849e6",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "c2bae19ad9154a27a58965eb20053332",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "e63cb194819a4ee0b903108371ba42f2",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "9935479584384dfba39c3d094f499d0f",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "84b4274431a1485c8f1fa4d1e2ee9d69",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "dd8b67d99e60408da1d0d3dbc8400f9b",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "2408f5b4b7ad41cfada87b1ad11a7dab",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "2fbedaf8e7144e06893a057137d027f3",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "27c6c2df22274c0daae0f73d470508d9",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "4755efa867064ab1b3c017d4d9efa2ee",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "c0025ae21bcb46a4b9faafc8844d4b0f",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "4af491d678b242e0a4473aee35323819",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "0b5f01fc02024521a61a1abbe0862fd6",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "bcce2758f36945cfaefe75507c6d428a",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "e00f5c0b817a4b8c848ed31df0f97f58",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "5380186438154529952863ddfc4050fe",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "7a8eae37cb3148fa80ece2493c70d8bd",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "953d3965f6604d4283777f66cd67dd68",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "6b5e0e8e98d4431fbb38ac7ce9a2ea32",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "e5ab6e2129e04c52b93b86e3925db485",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "54672953829e413bafe2cb23f3c5c618",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "418c8e90f1c343b88e3dc33cdb6ced40",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "5a24d19836a34eaaad38a8ee01f03a3b",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "aa4f0aa5acc64282a4aaaa6f5d730565",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "782bbf09775444a98e470b60d806f599",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "63913bdc097d41b09c7fbede78dc5c33",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "695ea489b48148cb8f1be6cb93d56368",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "a36f4bf7584f4f7ab328d894d85600fd",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "a64e37a133c9438298265971d3d6c38b",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "2de53678670f44109677beb829d1fe6e",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "adbda9d293fe4c13982cb775b5f8985d",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "0ab11acd6a4a4c7a85fccc12346e34f3",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "937ea80f0ad940eab12983555d5138c5",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "598fd3b8bf2c45f08eb67e495e036e58",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "c28839ba132f4cb5bb441f3a5bbaf6e4",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "59acd9bd2e0d4e9ca2e4af43918d8ace",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "63362a28645d4723be622d2bdd3e55e7",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "76a9553b95a649e98609d6edec2aafcb",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "267b3e2bab2548319ea0cb3a4f5c78b5",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "c217a0fef847480885e6359652321585",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "a4784c6f1cff43afb516f8f092a81bd4",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "0c412156bf824bbaaa61fab9bc23f2c2",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "9a7ac00a7cb44b7d8e1efbf2a3700e46",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "944fe0a3f1034186ac0a5fef87f20468",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "d91b6da0421241f6906b4fa387a3fb87",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "0d701a00ddc04e0b9b293c2d8a3baf34",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "f46308a5e042433d9302c62c0f0deea4",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "5a4d89ca167f4f3db2d2aacfb122cc4c",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "2857d14272634eef861e92a52881e656",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "0a2ac871278941ecb7ff5df479177366",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "9573c518402b4a92983e419835f58131",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "194f4f19ff214aeeabf60c3f7e5e7309",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "030fa3e809e24a158e1e2578844af7f8",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "b8f95ea03ba44921950513740cc55f91",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "7f16b879d4c046dab35c920165b99c7e",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "903ecfe268a64820ab8dd96671feeb6a",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "d524f1be1e224a208973edc761fdd2af",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "2b420431920a439dbee3bd18b33bde26",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "e8992ee66eb64248b9ce47f8cc961898",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "28fe58e33e364035a0701e19d2ef13fe",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "359c1b912a8b4d2bb21c26c4d4e0e23e",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "f49ba45be8844ffb889c771a83a7fc58",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "fc55123848da410c87d25f7f12cb1a09",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "d99b3f7a9ce347559048a13ea42c6875",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "56264252d1c34dc280a48dc4f9f5c86e",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "d070910420f04b5499f500be439981cd",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "19c4c2e63b154c7591b71eb214cd5553",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "b6c70e6eb2bc483295163bbfe50af9a3",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "7a33fde54a6d49d786155fcfc93b50b1",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "d1cc8b9cfcbd4b16b679e54ae0b15cc8",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "5086ef91d33a4175ab7ac38d3a1fef89",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "5f2b693d960c4f909352726c778dca08",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "577f3da864134ab58f5a3d09221705b2",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "f19b403dae91432781111183492588ae",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "9a23f2c24ff74f33885f55c488876a5f",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "8da60a2b994f424ab59697eeaa1e2c85",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "4026e6ed1b2345a4810f86d0ee2e694b",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "d9aa572b26a44662bfc10c41fa658020",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "1aa0629723304dcfa41e871fb301b815",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "b59709d907684acd998fd0ce106cc2b7",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "44396dbdc0a74fd48b2a3461e61df15e",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "e5144eed59e448dd8c6a5a3e87a7c60c",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "70e0c687bd0340aabae76a171ce99451",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "2ec50f07bb1547c69e1465de0dd16e9b",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "e9072458f70247469fa7f21de8d9c219",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "efea950a6c9141cea3b6ae089dfce6d5",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "63c8031901254322940a1bbfdf28be71",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "579a2f39f3ec41bda44e573368846411",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "4f4d8716d8c0471b866c0b9f76066955",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "493342885749445a81bd7fff573c93f3",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "53497915c06c40eab4326679b96f9a23",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "e9468ca1cb474072bab62a7e61c2bd78",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "f6dd4c63b8ff49a0a7aa74372d29a96a",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "a99e9d4a243540429fece45ee77d1c9a",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "cb0d4709a4984543a91aa1943e8ae025",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "76928e0b73ff4db7ac16dd5d21087197",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "d77afb9d79b14a00a9cb5c24d280696e",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "633b1066e5ae459da57d1838ca6f654a",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "cc38dc2f58434b3d851d0b97a7bbe37a",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "92d163360b14452c8697f0874801f535",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "87ab3fd97a2c4e5fba94dceb07c0f429",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "8ce0e50bc6404eb2811ce7fc715d66c6",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "fba98a7c6445429f9d63ca8560cf411b",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "6755328eb731470ca1f49be8ff541ee6",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "f77ea2ba27114cc5864807a95dbc06b6",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "aa702917828d49a486abea393ccaa36d",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "d439dc6fb86e46bca30f0caa7ebe3f5b",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "fb3719f706394116baa8e6cff6d88487",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "aada311e61d146018e0d1c60d1243fc1",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "0c9c2b7eff3b49ab8b9d47e1103e23d5",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "5b453a1591814178b8780955d99bc60b",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "8222ffa8e7134ac2bbb29b1bb2e209e6",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "f9d082c0879a418d85b3a6816acb4f39",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "62e3e86553564afca3bcfd63c305b7f6",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "d4d9a6b79d8d4e8b90fca3ffdf77946c",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "e4f01e40e7a54a1ba8501b3eddf2e845",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "94254a11928649e1acc6e47ea290c860",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "6c0bd505c7e84c78b71f39ba42255d36",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "aeb22232018040bca6ca246023974efb",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "edcf626dfaa24a7c8863d0df669f0908",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "83861f58c93344e2b929afc145896eba",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "8ce8cdbbb2c245e7a88a00be500c2048",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "15c2ac0bc62a40468b0eff0ff3b5d1a4",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "208cbe90073c4e5090356df88e5195ae",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "0235c93fbc514b0ab07d725b27381c15",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "a941fbae1a2d495695555e66277aaf5c",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "893cbc4489264e6e83ebe1d9a6c250d1",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "b990e8de7fa9407da3ce7a575a39fff6",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "6d49574b63ff415987ff57452b1fbd24",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "2897aa6ee3bc4d109a1482417aa594e7",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "d1a551df01f74e67aea133b7d5e80858",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "0fbec195a52446029258a468dad4876d",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "e055fb0c30c84492932b260178d5e119",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "6144814bfe6b40a6a3c3cea305abe7ef",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "468e9c8b8760457fa488702486fe2f89",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "e6a20ee628f14c00bb11dfb3f540d4d7",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "20bd9ba0514f44b6b5489cd6483a2916",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "d89d78d971464c24ad076b2ff958b070",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "a16edf4e85964a45a805a19cd65ac07c",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "afa9270c08b8424a9ff0d864fcef4f8b",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "2cc464e8135d41d9b8d0bf5220ecd084",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "76c35da852c84312bb48d58ac07c4a97",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "fffab2778e6946ed949aba9272a9df48",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "9bda7da47c8541cd85801011c7097c94",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "d76f5060f42e4efa89c1a6f584dd67bb",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "f5a7482d264a4939bc3927923318af6e",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "91bcda959abb46bebad5a3947b30a737",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "5989c98a68a64d3ab984b6cd01445afc",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "581e80686b8a401fa38942046dfba052",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "8d60f8678bee4935a11933331ec2446d",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "0e6f308188a1406faaed78b639fd4de1",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "22a2181179294caf9764805509c64a21",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "471d491c0cdc4643a62468314e87e450",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "eb38025b44c74504b01138515827e625",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "58fd525b46ec49f8965b029e6246b727",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "e9519ab756ba4015aaec0b1b1eefb94c",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "5e2c5e07fbc047c4bcde67c6f692e2df",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "4392175cbd6b4867bf619621b9695400",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "06a85ace6b0c416696e06275a39cceac",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "9bd6588dd0b84a02adf5a3d1e46fe786",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "b01c604bdcc248c1897f91068a8d3214",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "4087cf4175314940bea6d13576a9f11a",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "6ad1a683eea3477dbf82f9f2a0b060bf",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "242320985bda4b5db21867976c310bfe",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "699ee0865c9a4c3990a4a3825a2c3dc3",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "44cac5db95454806bcf8175137a018a9",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "dfb93fe6d2f648cf8be9006940d59d1f",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "14193de437bd4eb4b64e224fe9300e8b",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "1144b6edeec04995934070030d69bd04",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "6d34fd38130d4fe2806a8f9242bf606d",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "72b2e864aea04d8ca1ae592c7ad73b31",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "248eb3255e7f4094b239d197e9a538a7",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "06b27105fad2447c8fcf5ce69acb9db8",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "b719de1aee154cb0a41b18a93a160136",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "05a20aed8986475e85392e24792f510d",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "c6644a7c3a3d4d0e9a9d11951e29faab",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "0ccfab609e564630ad40c9dc0d8b1b4f",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "a06fac7b09884da0870b13ded8c8dae7",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "d3e057a937f44d6f8ab9d075a43dbd99",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "3ba50bfc46294956b9bb88ed1e271117",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "7161fc3f9ea44a73b69708bf7a7931a6",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "c2fbc7ca49f84db4a1b53c89d0923c18",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "8373113baef94bfdbf3a742bf0854f7c",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "4c67524d84574ab79f3a64ab3bbc10dd",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "d54733d5df8540f3907fe4179c57646f",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "1653c4a2137347cea0a35fd32364791a",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "236eaba1d0dd41b8af99a4b9a5fae4a1",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "0aae7f45b98a4397a5ad5d64a06504d0",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "777c6db9f14c47a3b6d3775d05fe4653",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "9e614430d2e845a382929877a64c73ed",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "3c1e1bd3ae5447888ccab232858b9873",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "11f10500df514c7b8436b483d1b4e8cb",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "345867062f9e49e2b40567cf2ed6a2d6",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "f34debc6820b4f50a8e2ca988c5d6a7d",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "cee0d2a2619946bfbb62246e862cd7b6",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "4cf6e4d55e7e430eac96855b564fa95a",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "636414fb58c34a05b4f57464f4f7598c",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "fd592c422d214af0aec1184bbaa66a23",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "2bf216ee9f3c4f7d8a9f3fe0fa2f05aa",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "6257f1aaa9a74b34a0f81eaecca5b27d",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "3a568ca856e7429984b261add4144935",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "12040c1ced6340088acc5f70e7ed87f4",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "31ac5e48be9046f9a909d34c391d8fa4",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "b76e4defc3bc43bda9804c93f19dce61",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "060e35d1a47a4581b2f4b85b8e7a39e0",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"`Trainer.fit` stopped: `max_epochs=1000` reached.\n"
]
}
],
"source": [
"# generate the data\n",
"problem.discretise_domain(1000, 'random', domains=['phys_cond', 'time_cond', 'bound_cond1', 'bound_cond2', 'bound_cond3', 'bound_cond4'])\n",
"\n",
"# crete the solver\n",
"pinn = PINN(problem, HardMLPtime(len(problem.input_variables), len(problem.output_variables)))\n",
"\n",
"# create trainer and train\n",
"trainer = Trainer(pinn, max_epochs=1000, accelerator='cpu', enable_model_summary=False) # we train on CPU and avoid model summary at beginning of training (optional)\n",
"trainer.train()"
]
},
{
"cell_type": "markdown",
"id": "a0f80cb8",
"metadata": {},
"source": [
"We can clearly see that the loss is way lower now. Let's plot the results"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "019767e5",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Plotting at t=0\n"
]
},
{
"data": {
"image/png": "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",
"text/plain": [
"<Figure size 1600x600 with 6 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Plotting at t=0.5\n"
]
},
{
"data": {
"image/png": "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",
"text/plain": [
"<Figure size 1600x600 with 6 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Plotting at t=1.0\n"
]
},
{
"data": {
"image/png": "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",
"text/plain": [
"<Figure size 1600x600 with 6 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"print('Plotting at t=0')\n",
"fixed_time_plot(fixed_variables={'t':0.0},pinn=pinn)\n",
"print('Plotting at t=0.5')\n",
"fixed_time_plot(fixed_variables={'t':0.5},pinn=pinn)\n",
"print('Plotting at t=1.0')\n",
"fixed_time_plot(fixed_variables={'t':1.0},pinn=pinn)\n"
]
},
{
"cell_type": "markdown",
"id": "b7338109",
"metadata": {},
"source": [
"We can see now that the results are way better! This is due to the fact that previously the network was not learning correctly the initial conditon, leading to a poor solution when time evolved. By imposing the initial condition the network is able to correctly solve the problem."
]
},
{
"cell_type": "markdown",
"id": "61195b1f",
"metadata": {},
"source": [
"## What's next?\n",
"\n",
"Congratulations on completing the two dimensional Wave tutorial of **PINA**! There are multiple directions you can go now:\n",
"\n",
"1. Train the network for longer or with different layer sizes and assert the finaly accuracy\n",
"\n",
"2. Propose new types of hard constraints in time, e.g. $$ u_{\\rm{pinn}} = xy(1-x)(1-y)\\cdot NN(x, y, t)(1-\\exp(-t)) + \\cos(\\sqrt{2}\\pi t)sin(\\pi x)\\sin(\\pi y), $$\n",
"\n",
"3. Exploit extrafeature training for model 1 and 2\n",
"\n",
"4. Many more..."
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.12.7"
}
},
"nbformat": 4,
"nbformat_minor": 5
}