folivo/PINA
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

Documentation for v0.1 version (#199)

Browse Source 
* Adding Equations, solving typos
* improve _code.rst
* the team rst and restuctore index.rst
* fixing errors

---------

Co-authored-by: Dario Coscia <dariocoscia@dhcp-015.eduroam.sissa.it>
This commit is contained in:
Dario Coscia
2023-11-08 14:39:00 +01:00
committed by Nicola Demo
parent 3f9305d475
commit 8b7b61b3bd
144 changed files with 2741 additions and 1766 deletions
Download Patch File Download Diff File Expand all files Collapse all files

48
docs/source/_rst/tutorials/tutorial4/tutorial.rst
View File

@@ -21,16 +21,15 @@ First of all we import the modules needed for the tutorial:
import torchvision # for MNIST dataset
from pina.model import FeedForward # for building AE and MNIST classification
The tutorial is structured as follow: \* `Continuous filter
background <#continuous-filter-background>`__: understand how the
convolutional filter works and how to use it. \* `Building a MNIST
Classifier <#building-a-mnist-classifier>`__: show how to build a simple
classifier using the MNIST dataset and how to combine a continuous
convolutional layer with a feedforward neural network. \* `Building a
Continuous Convolutional
Autoencoder <#building-a-continuous-convolutional-autoencoder>`__: show
how to use the continuous filter to work with unstructured data for
autoencoding and up-sampling.
The tutorial is structured as follow:
* `Continuous filter background <#continuous-filter-background>`__: understand how the convolutional filter works and how to use it.
* `Building a MNIST Classifier <#building-a-mnist-classifier>`__: show how to build a simple
classifier using the MNIST dataset and how to combine a continuous
convolutional layer with a feedforward neural network.
* `Building a Continuous Convolutional Autoencoder <#building-a-continuous-convolutional-autoencoder>`__: show
show to use the continuous filter to work with unstructured data for
autoencoding and up-sampling.
Continuous filter background
----------------------------
@@ -153,13 +152,16 @@ where to go. Here is an example for the :math:`[0,1]\times[0,5]` domain:
"direction": [1, 1],
}
This tells the filter: 1. ``domain``: square domain (the only
implemented) :math:`[0,1]\times[0,5]`. The minimum value is always zero,
while the maximum is specified by the user 2. ``start``: start position
of the filter, coordinate :math:`(0, 0)` 3. ``jump``: the jumps of the
centroid of the filter to the next position :math:`(0.1, 0.3)` 4.
``direction``: the directions of the jump, with ``1 = right``,
``0 = no jump``,\ ``-1 = left`` with respect to the current position
This tells the filter:
1. ``domain``: square domain (the only implemented) :math:`[0,1]\times[0,5]`. The minimum value is always zero,
while the maximum is specified by the user
2. ``start``: start position
of the filter, coordinate :math:`(0, 0)`
3. ``jump``: the jumps of the
centroid of the filter to the next position :math:`(0.1, 0.3)`
4. ``direction``: the directions of the jump, with ``1 = right``,
``0 = no jump``,\ ``-1 = left`` with respect to the current position
**Note**
@@ -170,9 +172,7 @@ Filter definition
~~~~~~~~~~~~~~~~~
Having defined all the previous blocks we are able to construct the
continuous filter.
Suppose we would like to get an ouput with only one field, and let us
continuous filter. Suppose we would like to get an ouput with only one field, and let us
fix the filter dimension to be :math:`[0.1, 0.1]`.
.. code:: ipython3
@@ -192,13 +192,7 @@ fix the filter dimension to be :math:`[0.1, 0.1]`.
output_numb_field=1,
filter_dim=filter_dim,
stride=stride)
.. parsed-literal::
/u/d/dcoscia/.local/lib/python3.9/site-packages/torch/functional.py:504: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at ../aten/src/ATen/native/TensorShape.cpp:3483.)
return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined]
That’s it! In just one line of code we have created the continuous
convolutional filter. By default the ``pina.model.FeedForward`` neural