init

LICENCE

@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2017
+
+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

@@ -30,6 +30,7 @@ In these tutorials for pyTorch, we will build our first Neural Network and try t
   * [RNN-Regression](https://github.com/MorvanZhou/PyTorch-Tutorial/blob/master/tutorial-contents/403_RNN_regressor.py)
   * [AutoEncoder](https://github.com/MorvanZhou/PyTorch-Tutorial/blob/master/tutorial-contents/404_autoencoder.py)
   * [DQN Reinforcement Learning](https://github.com/MorvanZhou/PyTorch-Tutorial/blob/master/tutorial-contents/405_DQN_Reinforcement_learning.py)
+  * GAN (WIP)
 * Others (WIP)
   * [Why torch dynamic](https://github.com/MorvanZhou/PyTorch-Tutorial/blob/master/tutorial-contents/501_why_torch_dynamic_graph.py)
   * [Train on GPU](https://github.com/MorvanZhou/PyTorch-Tutorial/blob/master/tutorial-contents/502_GPU.py)

tutorial-contents/404_autoencoder.py

@@ -139,4 +139,3 @@ ax.set_xlim(X.min(), X.max())
 ax.set_ylim(Y.min(), Y.max())
 ax.set_zlim(Z.min(), Z.max())
 plt.show()
-