Merge branch 'master' into cifartut_improve

Author: wAuner

.circleci/scripts/build_for_windows.sh

@@ -49,6 +49,7 @@ if [[ "${CIRCLE_JOB}" == *worker_* ]]; then
   python $DIR/remove_runnable_code.py advanced_source/static_quantization_tutorial.py advanced_source/static_quantization_tutorial.py || true
   python $DIR/remove_runnable_code.py beginner_source/hyperparameter_tuning_tutorial.py beginner_source/hyperparameter_tuning_tutorial.py || true
   python $DIR/remove_runnable_code.py beginner_source/audio_preprocessing_tutorial.py  beginner_source/audio_preprocessing_tutorial.py || true
+  python $DIR/remove_runnable_code.py beginner_source/dcgan_faces_tutorial.py  beginner_source/dcgan_faces_tutorial.py || true
   python $DIR/remove_runnable_code.py intermediate_source/tensorboard_profiler_tutorial.py intermediate_source/tensorboard_profiler_tutorial.py || true
   # Temp remove for mnist download issue. (Re-enabled for 1.8.1)
   # python $DIR/remove_runnable_code.py beginner_source/fgsm_tutorial.py  beginner_source/fgsm_tutorial.py || true

README.md

@@ -28,10 +28,10 @@ In case you prefer to write your tutorial in jupyter, you can use [this script](
 - Then you can build using `make docs`. This will download the data, execute the tutorials and build the documentation to `docs/` directory. This will take about 60-120 min for systems with GPUs. If you do not have a GPU installed on your system, then see next step.
 - You can skip the computationally intensive graph generation by running `make html-noplot` to build basic html documentation to `_build/html`. This way, you can quickly preview your tutorial.
 
-> If you get **ModuleNotFoundError: No module named 'pytorch_sphinx_theme' make: *** [html-noplot] Error 2**, from /tutorials/src/pytorch-sphinx-theme run `python setup.py install`. 
+> If you get **ModuleNotFoundError: No module named 'pytorch_sphinx_theme' make: *** [html-noplot] Error 2** from /tutorials/src/pytorch-sphinx-theme or /venv/src/pytorch-sphinx-theme (while using virtualenv), run `python setup.py install`. 
 
 
 ## About contributing to PyTorch Documentation and Tutorials
 * You can find information about contributing to PyTorch documentation in the 
 PyTorch Repo [README.md](https://github.com/pytorch/pytorch/blob/master/README.md) file. 
-* Additional information can be found in [PyTorch CONTRIBUTING.md](https://github.com/pytorch/pytorch/blob/master/CONTRIBUTING.md).
+* Additional information can be found in [PyTorch CONTRIBUTING.md](https://github.com/pytorch/pytorch/blob/master/CONTRIBUTING.md).

_static/img/profiler_overview1.PNG renamed to _static/img/profiler_overview1.png

@@ -75,7 +75,7 @@
 </noscript>
 
 <script type="text/javascript">
-  var collapsedSections = ['PyTorch Recipes', 'Image and Video', 'Audio', 'Text', 'Reinforcement Learning', 'Deploying PyTorch Models in Production', 'Code Transforms with FX', 'Frontend APIs', 'Extending PyTorch', 'Model Optimization', 'Parallel and Distributed Training', 'Mobile'];
+  var collapsedSections = ['PyTorch Recipes', 'Learning PyTorch', 'Image and Video', 'Audio', 'Text', 'Reinforcement Learning', 'Deploying PyTorch Models in Production', 'Code Transforms with FX', 'Frontend APIs', 'Extending PyTorch', 'Model Optimization', 'Parallel and Distributed Training', 'Mobile'];
 </script>
 
 <img height="1" width="1" style="border-style:none;" alt="" src="https://www.googleadservices.com/pagead/conversion/795629140/?label=txkmCPmdtosBENSssfsC&amp;guid=ON&amp;script=0"/>

_static/img/profiler_overview2.PNG renamed to _static/img/profiler_overview2.png

@@ -47,7 +47,7 @@
 #
 # In this network, ``w`` and ``b`` are **parameters**, which we need to
 # optimize. Thus, we need to be able to compute the gradients of loss
-# function with respect to those variables. In orded to do that, we set
+# function with respect to those variables. In order to do that, we set
 # the ``requires_grad`` property of those tensors.
 
 #######################################################################
@@ -58,7 +58,7 @@
 # A function that we apply to tensors to construct computational graph is
 # in fact an object of class ``Function``. This object knows how to
 # compute the function in the *forward* direction, and also how to compute
-# it's derivative during the *backward propagation* step. A reference to
+# its derivative during the *backward propagation* step. A reference to
 # the backward propagation function is stored in ``grad_fn`` property of a
 # tensor. You can find more information of ``Function`` `in the
 # documentation <https://pytorch.org/docs/stable/autograd.html#function>`__.

_static/img/profiler_trace_view1.PNG renamed to _static/img/profiler_trace_view1.png

@@ -67,7 +67,7 @@ def forward(self, x):
 
 ##############################################
 # We create an instance of ``NeuralNetwork``, and move it to the ``device``, and print 
-# it's structure.
+# its structure.
 
 model = NeuralNetwork().to(device)
 print(model)
@@ -119,7 +119,7 @@ def forward(self, x):
 # nn.Linear 
 # ^^^^^^^^^^^^^^^^^^^^^^
 # The `linear layer <https://pytorch.org/docs/stable/generated/torch.nn.Linear.html>`_
-# is a module that applies a linear transformation on the input using it's stored weights and biases.
+# is a module that applies a linear transformation on the input using its stored weights and biases.
 #
 layer1 = nn.Linear(in_features=28*28, out_features=20)
 hidden1 = layer1(flat_image)