Merge remote-tracking branch 'upstream/master' into mp

Author: mrshenli

beginner_source/chatbot_tutorial.py

@@ -632,8 +632,8 @@ def batch2TrainData(voc, pair_batch):
 #
 # Finally, if passing a padded batch of sequences to an RNN module, we
 # must pack and unpack padding around the RNN pass using
-# ``torch.nn.utils.rnn.pack_padded_sequence`` and
-# ``torch.nn.utils.rnn.pad_packed_sequence`` respectively.
+# ``nn.utils.rnn.pack_padded_sequence`` and
+# ``nn.utils.rnn.pad_packed_sequence`` respectively.
 #
 # **Computation Graph:**
 #
@@ -679,11 +679,11 @@ def forward(self, input_seq, input_lengths, hidden=None):
         # Convert word indexes to embeddings
         embedded = self.embedding(input_seq)
         # Pack padded batch of sequences for RNN module
-        packed = torch.nn.utils.rnn.pack_padded_sequence(embedded, input_lengths)
+        packed = nn.utils.rnn.pack_padded_sequence(embedded, input_lengths)
         # Forward pass through GRU
         outputs, hidden = self.gru(packed, hidden)
         # Unpack padding
-        outputs, _ = torch.nn.utils.rnn.pad_packed_sequence(outputs)
+        outputs, _ = nn.utils.rnn.pad_packed_sequence(outputs)
         # Sum bidirectional GRU outputs
         outputs = outputs[:, :, :self.hidden_size] + outputs[:, : ,self.hidden_size:]
         # Return output and final hidden state
@@ -755,18 +755,18 @@ def forward(self, input_seq, input_lengths, hidden=None):
 #
 
 # Luong attention layer
-class Attn(torch.nn.Module):
+class Attn(nn.Module):
     def __init__(self, method, hidden_size):
         super(Attn, self).__init__()
         self.method = method
         if self.method not in ['dot', 'general', 'concat']:
             raise ValueError(self.method, "is not an appropriate attention method.")
         self.hidden_size = hidden_size
         if self.method == 'general':
-            self.attn = torch.nn.Linear(self.hidden_size, hidden_size)
+            self.attn = nn.Linear(self.hidden_size, hidden_size)
         elif self.method == 'concat':
-            self.attn = torch.nn.Linear(self.hidden_size * 2, hidden_size)
-            self.v = torch.nn.Parameter(torch.FloatTensor(hidden_size))
+            self.attn = nn.Linear(self.hidden_size * 2, hidden_size)
+            self.v = nn.Parameter(torch.FloatTensor(hidden_size))
 
     def dot_score(self, hidden, encoder_output):
         return torch.sum(hidden * encoder_output, dim=2)
@@ -1021,8 +1021,8 @@ def train(input_variable, lengths, target_variable, mask, max_target_len, encode
     loss.backward()
 
     # Clip gradients: gradients are modified in place
-    _ = torch.nn.utils.clip_grad_norm_(encoder.parameters(), clip)
-    _ = torch.nn.utils.clip_grad_norm_(decoder.parameters(), clip)
+    _ = nn.utils.clip_grad_norm_(encoder.parameters(), clip)
+    _ = nn.utils.clip_grad_norm_(decoder.parameters(), clip)
 
     # Adjust model weights
     encoder_optimizer.step()

beginner_source/dcgan_faces_tutorial.py

@@ -218,7 +218,7 @@
 # ----
 # 
 # In this tutorial we will use the `Celeb-A Faces
-# dataset <https://mmlab.ie.cuhk.edu.hk/projects/CelebA.html>`__ which can
+# dataset <http://mmlab.ie.cuhk.edu.hk/projects/CelebA.html>`__ which can
 # be downloaded at the linked site, or in `Google
 # Drive <https://drive.google.com/drive/folders/0B7EVK8r0v71pTUZsaXdaSnZBZzg>`__.
 # The dataset will download as a file named *img_align_celeba.zip*. Once
@@ -283,7 +283,7 @@
 # 
 # From the DCGAN paper, the authors specify that all model weights shall
 # be randomly initialized from a Normal distribution with mean=0,
-# stdev=0.2. The ``weights_init`` function takes an initialized model as
+# stdev=0.02. The ``weights_init`` function takes an initialized model as
 # input and reinitializes all convolutional, convolutional-transpose, and
 # batch normalization layers to meet this criteria. This function is
 # applied to the models immediately after initialization.