Merge branch 'master' into patch-1

Author: holly1238

.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

beginner_source/basics/autogradqs_tutorial.py

@@ -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.
 
 #######################################################################

beginner_source/nlp/pytorch_tutorial.py

@@ -9,7 +9,7 @@
 All of deep learning is computations on tensors, which are
 generalizations of a matrix that can be indexed in more than 2
 dimensions. We will see exactly what this means in-depth later. First,
-lets look what we can do with tensors.
+let's look what we can do with tensors.
 """
 # Author: Robert Guthrie
 
@@ -162,7 +162,7 @@
 # other operation, etc.)
 #
 # If ``requires_grad=True``, the Tensor object keeps track of how it was
-# created. Lets see it in action.
+# created. Let's see it in action.
 #
 
 # Tensor factory methods have a ``requires_grad`` flag
@@ -187,7 +187,7 @@
 # But how does that help us compute a gradient?
 #
 
-# Lets sum up all the entries in z
+# Let's sum up all the entries in z
 s = z.sum()
 print(s)
 print(s.grad_fn)
@@ -222,7 +222,7 @@
 
 
 ######################################################################
-# Lets have Pytorch compute the gradient, and see that we were right:
+# Let's have Pytorch compute the gradient, and see that we were right:
 # (note if you run this block multiple times, the gradient will increment.
 # That is because Pytorch *accumulates* the gradient into the .grad
 # property, since for many models this is very convenient.)

beginner_source/transformer_tutorial.py

@@ -45,15 +45,16 @@
 #
 
 import math
+
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
+from torch.nn import TransformerEncoder, TransformerEncoderLayer
 
 class TransformerModel(nn.Module):
 
     def __init__(self, ntoken, ninp, nhead, nhid, nlayers, dropout=0.5):
         super(TransformerModel, self).__init__()
-        from torch.nn import TransformerEncoder, TransformerEncoderLayer
         self.model_type = 'Transformer'
         self.pos_encoder = PositionalEncoding(ninp, dropout)
         encoder_layers = TransformerEncoderLayer(ninp, nhead, nhid, dropout)
@@ -251,12 +252,13 @@ def get_batch(source, i):
 # function to scale all the gradient together to prevent exploding.
 #
 
+import time
+
 criterion = nn.CrossEntropyLoss()
 lr = 5.0 # learning rate
 optimizer = torch.optim.SGD(model.parameters(), lr=lr)
 scheduler = torch.optim.lr_scheduler.StepLR(optimizer, 1.0, gamma=0.95)
 
-import time
 def train():
     model.train() # Turn on the train mode
     total_loss = 0.

index.rst

@@ -497,7 +497,7 @@ Additional Resources
    :header: PyTorch Cheat Sheet
    :description: Quick overview to essential PyTorch elements.
    :button_link: beginner/ptcheat.html
-   :button_text: Download
+   :button_text: Open
 
 .. customcalloutitem::
    :header: Tutorials on GitHub
@@ -509,7 +509,7 @@ Additional Resources
    :header: Run Tutorials on Google Colab
    :description: Learn how to copy tutorial data into Google Drive so that you can run tutorials on Google Colab.
    :button_link: beginner/colab.html
-   :button_text: Download
+   :button_text: Open
 
 .. End of callout section