be11110bb2
* fnn update, remove labeltensors * allow custom torch models * updating tests --------- Co-authored-by: Dario Coscia <dariocoscia@Dario-Coscia.local> Co-authored-by: Dario Coscia <dariocoscia@dhcp-031.eduroam.sissa.it>
85 lines
3.1 KiB
Python
85 lines
3.1 KiB
Python
"""Module for FeedForward model"""
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import torch
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import torch.nn as nn
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from pina.label_tensor import LabelTensor
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class FeedForward(torch.nn.Module):
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"""
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The PINA implementation of feedforward network, also refered as multilayer
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perceptron.
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:param int input_variables: The number of input components of the model.
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Expected tensor shape of the form (*, input_variables), where *
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means any number of dimensions including none.
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:param int output_variables: The number of output components of the model.
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Expected tensor shape of the form (*, output_variables), where *
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means any number of dimensions including none.
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:param int inner_size: number of neurons in the hidden layer(s). Default is
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20.
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:param int n_layers: number of hidden layers. Default is 2.
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:param func: the activation function to use. If a single
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:class:`torch.nn.Module` is passed, this is used as activation function
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after any layers, except the last one. If a list of Modules is passed,
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they are used as activation functions at any layers, in order.
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:param iterable(int) layers: a list containing the number of neurons for
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any hidden layers. If specified, the parameters `n_layers` e
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`inner_size` are not considered.
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:param bool bias: If `True` the MLP will consider some bias.
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"""
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def __init__(self, input_variables, output_variables, inner_size=20,
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n_layers=2, func=nn.Tanh, layers=None, bias=True):
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"""
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"""
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super().__init__()
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if not isinstance(input_variables, int):
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raise ValueError('input_variables expected to be int.')
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self.input_dimension = input_variables
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if not isinstance(output_variables, int):
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raise ValueError('output_variables expected to be int.')
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self.output_dimension = output_variables
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if layers is None:
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layers = [inner_size] * n_layers
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tmp_layers = layers.copy()
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tmp_layers.insert(0, self.input_dimension)
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tmp_layers.append(self.output_dimension)
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self.layers = []
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for i in range(len(tmp_layers) - 1):
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self.layers.append(
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nn.Linear(tmp_layers[i], tmp_layers[i + 1], bias=bias)
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)
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if isinstance(func, list):
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self.functions = func
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else:
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self.functions = [func for _ in range(len(self.layers) - 1)]
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if len(self.layers) != len(self.functions) + 1:
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raise RuntimeError('uncosistent number of layers and functions')
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unique_list = []
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for layer, func in zip(self.layers[:-1], self.functions):
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unique_list.append(layer)
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if func is not None:
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unique_list.append(func())
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unique_list.append(self.layers[-1])
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self.model = nn.Sequential(*unique_list)
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def forward(self, x):
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"""
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Defines the computation performed at every call.
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:param x: .
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:type x: :class:`pina.LabelTensor`
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:return: the output computed by the model.
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:rtype: LabelTensor
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"""
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return self.model(x)
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