Merge branch 'master' into master

Author: vijaykriishna

_static/img/thumbnails/cropped/parametrizations.png

@@ -115,7 +115,7 @@ If you need to exclude some methods in your ``nn.Module``
 because they use Python features that TorchScript doesn't support yet,
 you could annotate those with ``@torch.jit.ignore``
 
-``my_module`` is an instance of
+``sm`` is an instance of
 ``ScriptModule`` that is ready for serialization.
 
 Step 2: Serializing Your Script Module to a File
@@ -132,7 +132,7 @@ on the module and pass it a filename::
   traced_script_module.save("traced_resnet_model.pt")
 
 This will produce a ``traced_resnet_model.pt`` file in your working directory.
-If you also would like to serialize ``my_module``, call ``my_module.save("my_module_model.pt")``
+If you also would like to serialize ``sm``, call ``sm.save("my_module_model.pt")``
 We have now officially left the realm of Python and are ready to cross over to the sphere
 of C++.
 

advanced_source/cpp_export.rst

@@ -12,7 +12,7 @@
 Optimizing Model Parameters
 ===========================
 
-Now that we have a model and data it's time to train, validate and test our model by optimizing it's parameters on 
+Now that we have a model and data it's time to train, validate and test our model by optimizing its parameters on 
 our data. Training a model is an iterative process; in each iteration (called an *epoch*) the model makes a guess about the output, calculates 
 the error in its guess (*loss*), collects the derivatives of the error with respect to its parameters (as we saw in 
 the `previous section  <autograd_tutorial.html>`_), and **optimizes** these parameters using gradient descent. For a more 

beginner_source/basics/optimization_tutorial.py

@@ -58,7 +58,7 @@ def __init__(self):
     def forward(self, x):
         # Max pooling over a (2, 2) window
         x = F.max_pool2d(F.relu(self.conv1(x)), (2, 2))
-        # If the size is a square you can only specify a single number
+        # If the size is a square, you can specify with a single number
         x = F.max_pool2d(F.relu(self.conv2(x)), 2)
         x = x.view(-1, self.num_flat_features(x))
         x = F.relu(self.fc1(x))

beginner_source/blitz/neural_networks_tutorial.py

@@ -14,9 +14,9 @@ Deep Learning for NLP with Pytorch
 	https://pytorch.org/tutorials/beginner/nlp/word_embeddings_tutorial.html
 
 4. sequence_models_tutorial.py
-	Sequence Models and Long-Short Term Memory Networks
+	Sequence Models and Long Short-Term Memory Networks
 	https://pytorch.org/tutorials/beginner/nlp/sequence_models_tutorial.html
 
 5. advanced_tutorial.py
 	Advanced: Making Dynamic Decisions and the Bi-LSTM CRF
-	https://pytorch.org/tutorials/beginner/nlp/advanced_tutorial.html
+	https://pytorch.org/tutorials/beginner/nlp/advanced_tutorial.html

beginner_source/nlp/README.txt

@@ -1,6 +1,6 @@
 # -*- coding: utf-8 -*-
 r"""
-Sequence Models and Long-Short Term Memory Networks
+Sequence Models and Long Short-Term Memory Networks
 ===================================================
 
 At this point, we have seen various feed-forward networks. That is,

beginner_source/nlp/sequence_models_tutorial.py

@@ -268,6 +268,8 @@ def forward(self, inputs):
     losses.append(total_loss)
 print(losses)  # The loss decreased every iteration over the training data!
 
+# To get the embedding of a particular word, e.g. "beauty"
+print(model.embeddings.weight[word_to_ix["beauty"]])
 
 ######################################################################
 # Exercise: Computing Word Embeddings: Continuous Bag-of-Words

beginner_source/nlp/word_embeddings_tutorial.py

@@ -324,6 +324,13 @@ Welcome to PyTorch Tutorials
    :link: beginner/hyperparameter_tuning_tutorial.html
    :tags: Model-Optimization,Best-Practice
 
+.. customcarditem::
+   :header: Parametrizations Tutorial
+   :card_description: Learn how to use torch.nn.utils.parametrize to put constriants on your parameters (e.g. make them orthogonal, symmetric positive definite, low-rank...)
+   :image: _static/img/thumbnails/cropped/parametrizations.png
+   :link: intermediate/parametrizations.html
+   :tags: Model-Optimization,Best-Practice
+
 .. customcarditem::
    :header: Pruning Tutorial
    :card_description: Learn how to use torch.nn.utils.prune to sparsify your neural networks, and how to extend it to implement your own custom pruning technique.
@@ -620,6 +627,7 @@ Additional Resources
 
    beginner/profiler
    beginner/hyperparameter_tuning_tutorial
+   intermediate/parametrizations
    intermediate/pruning_tutorial
    advanced/dynamic_quantization_tutorial
    intermediate/dynamic_quantization_bert_tutorial