First commit for reorganizing beginner tutorials

Author: Seth Weidman

beginner_source/blitz/cifar10_tutorial.py

@@ -185,6 +185,15 @@ def forward(self, x):
 print('Finished Training')
 
 ########################################################################
+# Let's quickly save our trained model:
+
+PATH = './data'
+torch.save(net.state_dict(), PATH)
+
+########################################################################
+# See `here <https://pytorch.org/docs/stable/notes/serialization.html>`_
+# for more details on saving PyTorch models.
+#
 # 5. Test the network on the test data
 # ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 #
@@ -204,6 +213,13 @@ def forward(self, x):
 imshow(torchvision.utils.make_grid(images))
 print('GroundTruth: ', ' '.join('%5s' % classes[labels[j]] for j in range(4)))
 
+########################################################################
+# Next, let's load back in our saved model (note: saving and re-loading the model
+# wasn't necessary here, we only did it to illustrate how to do so):
+
+net = Net()
+net.load_state_dict(torch.load(PATH))
+
 ########################################################################
 # Okay, now let us see what the neural network thinks these examples above are:
 

beginner_source/data_loading_tutorial.py

@@ -1,7 +1,7 @@
 # -*- coding: utf-8 -*-
 """
-Data Loading and Processing Tutorial
-====================================
+Writing Custom Datasets, DataLoaders and Transforms
+===================================================
 **Author**: `Sasank Chilamkurthy <https://chsasank.github.io>`_
 
 A lot of effort in solving any machine learning problem goes in to

beginner_source/pytorch_with_examples.rst

@@ -123,43 +123,6 @@ network:
 
 .. includenodoc:: /beginner/examples_autograd/two_layer_net_custom_function.py
 
-TensorFlow: Static Graphs
--------------------------
-
-PyTorch autograd looks a lot like TensorFlow: in both frameworks we
-define a computational graph, and use automatic differentiation to
-compute gradients. The biggest difference between the two is that
-TensorFlow's computational graphs are **static** and PyTorch uses
-**dynamic** computational graphs.
-
-In TensorFlow, we define the computational graph once and then execute
-the same graph over and over again, possibly feeding different input
-data to the graph. In PyTorch, each forward pass defines a new
-computational graph.
-
-Static graphs are nice because you can optimize the graph up front; for
-example a framework might decide to fuse some graph operations for
-efficiency, or to come up with a strategy for distributing the graph
-across many GPUs or many machines. If you are reusing the same graph
-over and over, then this potentially costly up-front optimization can be
-amortized as the same graph is rerun over and over.
-
-One aspect where static and dynamic graphs differ is control flow. For
-some models we may wish to perform different computation for each data
-point; for example a recurrent network might be unrolled for different
-numbers of time steps for each data point; this unrolling can be
-implemented as a loop. With a static graph the loop construct needs to
-be a part of the graph; for this reason TensorFlow provides operators
-such as ``tf.scan`` for embedding loops into the graph. With dynamic
-graphs the situation is simpler: since we build graphs on-the-fly for
-each example, we can use normal imperative flow control to perform
-computation that differs for each input.
-
-To contrast with the PyTorch autograd example above, here we use
-TensorFlow to fit a simple two-layer net:
-
-.. includenodoc:: /beginner/examples_autograd/tf_two_layer_net.py
-
 `nn` module
 ===========
 

beginner_source/transfer_learning_tutorial.py

@@ -1,12 +1,12 @@
 # -*- coding: utf-8 -*-
 """
-Transfer Learning Tutorial
-==========================
+Transfer Learning for Computer Vision Tutorial
+==============================================
 **Author**: `Sasank Chilamkurthy <https://chsasank.github.io>`_
 
-In this tutorial, you will learn how to train your network using
-transfer learning. You can read more about the transfer learning at `cs231n
-notes <https://cs231n.github.io/transfer-learning/>`__
+In this tutorial, you will learn how to train a convolutional neural network for
+image classification using transfer learning. You can read more about the transfer
+learning at `cs231n notes <https://cs231n.github.io/transfer-learning/>`__
 
 Quoting these notes,
 

index.rst

@@ -38,34 +38,11 @@ Getting Started
    :tooltip: Learn how to load and preprocess/augment data from a non trivial dataset
    :description: :doc:`/beginner/data_loading_tutorial`
 
-.. customgalleryitem::
-   :tooltip: This tutorial introduces the fundamental concepts of PyTorch through self-contained examples
-   :figure: /_static/img/thumbnails/examples.png
-   :description: :doc:`/beginner/pytorch_with_examples`
-
-.. customgalleryitem::
-   :figure: /_static/img/thumbnails/sphx_glr_transfer_learning_tutorial_001.png
-   :tooltip: In transfer learning, a model created from one task is used in another
-   :description: :doc:`beginner/transfer_learning_tutorial`
-
-.. customgalleryitem::
-   :figure: /_static/img/thumbnails/floppy.png
-   :tooltip: Explore use cases for the saving and loading of PyTorch models
-   :description: :doc:`beginner/saving_loading_models`
-
-.. .. galleryitem:: beginner/saving_loading_models.py
-
 .. customgalleryitem::
    :figure: /_static/img/thumbnails/pytorch_tensorboard.png
    :tooltip: Learn to use TensorBoard to visualize data and model training
    :description: :doc:`intermediate/tensorboard_tutorial`
 
-.. customgalleryitem::
-   :figure: /_static/img/torch.nn.png
-   :tooltip: Use torch.nn to create and train a neural network
-   :description: :doc:`beginner/nn_tutorial`
-
-
 .. raw:: html
 
     <div style='clear:both'></div>
@@ -80,9 +57,9 @@ Image
    :description: :doc:`intermediate/torchvision_tutorial`
 
 .. customgalleryitem::
-   :figure: /_static/img/thumbnails/eye.png
-   :tooltip: Finetune and feature extract the torchvision models
-   :description: :doc:`beginner/finetuning_torchvision_models_tutorial`
+   :figure: /_static/img/thumbnails/sphx_glr_transfer_learning_tutorial_001.png
+   :tooltip: In transfer learning, a model created from one task is used in another
+   :description: :doc:`beginner/transfer_learning_tutorial`
 
 .. customgalleryitem::
    :figure: /_static/img/stn/Five.gif
@@ -247,7 +224,7 @@ Extending PyTorch
     <div style='clear:both'></div>
 
 PyTorch in Other Languages
--------------
+--------------------------
 
 .. customgalleryitem::
     :tooltip: Using the PyTorch C++ Frontend
@@ -258,6 +235,24 @@ PyTorch in Other Languages
 
     <div style='clear:both'></div>
 
+PyTorch Fundamentals In-Depth
+-----------------------------
+
+.. customgalleryitem::
+   :tooltip: This tutorial introduces the fundamental concepts of PyTorch through self-contained examples
+   :figure: /_static/img/thumbnails/examples.png
+   :description: :doc:`/beginner/pytorch_with_examples`
+
+.. customgalleryitem::
+   :figure: /_static/img/torch.nn.png
+   :tooltip: Use torch.nn to create and train a neural network
+   :description: :doc:`beginner/nn_tutorial`
+
+.. raw:: html
+
+    <div style='clear:both'></div>
+
+
 .. -----------------------------------------
 .. Page TOC
 .. -----------------------------------------
@@ -269,12 +264,7 @@ PyTorch in Other Languages
 
    beginner/deep_learning_60min_blitz
    beginner/data_loading_tutorial
-   beginner/pytorch_with_examples
-   beginner/transfer_learning_tutorial
-   beginner/deploy_seq2seq_hybrid_frontend_tutorial
    intermediate/tensorboard_tutorial
-   beginner/saving_loading_models
-   beginner/nn_tutorial
 
 .. toctree::
    :maxdepth: 2
@@ -283,7 +273,7 @@ PyTorch in Other Languages
    :caption: Image
 
    intermediate/torchvision_tutorial
-   beginner/finetuning_torchvision_models_tutorial
+   beginner/transfer_learning_tutorial
    intermediate/spatial_transformer_tutorial
    advanced/neural_style_tutorial
    beginner/fgsm_tutorial
@@ -357,3 +347,12 @@ PyTorch in Other Languages
    :caption: PyTorch in Other Languages
 
    advanced/cpp_frontend
+
+.. toctree::
+   :maxdepth: 2
+   :includehidden:
+   :hidden:
+   :caption: PyTorch Fundamentals In-Depth
+
+   beginner/pytorch_with_examples
+   beginner/nn_tutorial