Merge branch 'master' of https://github.com/jcjohnson/pytorch-examples

Author: jcjohnson

LICENSE

@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2017 Justin Johnson
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

README.md

@@ -209,7 +209,7 @@ for t in range(500):
 
   # Use autograd to compute the backward pass. This call will compute the
   # gradient of loss with respect to all Variables with requires_grad=True.
-  # After this call w1.data and w2.data will be Variables holding the gradient
+  # After this call w1.grad and w2.grad will be Variables holding the gradient
   # of the loss with respect to w1 and w2 respectively.
   loss.backward()
 

autograd/two_layer_net_autograd.py

@@ -53,7 +53,7 @@
 
   # Use autograd to compute the backward pass. This call will compute the
   # gradient of loss with respect to all Variables with requires_grad=True.
-  # After this call w1.data and w2.data will be Variables holding the gradient
+  # After this call w1.grad and w2.grad will be Variables holding the gradient
   # of the loss with respect to w1 and w2 respectively.
   loss.backward()