torch 0.4.1

shayneobrien · Aug 1, 2018 · f949337 · f949337
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diff --git a/README.md b/README.md
@@ -1,5 +1,5 @@
 # OVERVIEW
-PyTorch version: 0.4.0 | Python 3.6.5
+PyTorch version: 0.4.1 | Python 3.6.5
 
 Commented / annotated implementations and comparative introductions for minimax, non-saturating, wasserstein, wasserstein gradient penalty, least squares, deep regret analytic, bounded equilibrium generative adversarial networks, relativistic (GANs), and variational autoencoder (VAE). Paper links are supplied at the beginning of each file with a short summary of the paper. See src folder for files to run via terminal, or notebooks folder for Jupyter notebooks via your local browser. The Jupyter notebooks contain in-notebook visualizations. All code in this repository operates in a generative, unsupervised manner on binary MNIST.
 

diff --git a/notebooks/01-nonsaturating-gan.ipynb b/notebooks/01-nonsaturating-gan.ipynb
@@ -507,7 +507,7 @@
     "        \n",
     "    def forward(self, x):\n",
     "        activated = F.relu(self.linear(x))\n",
-    "        generation = F.sigmoid(self.generate(activated))\n",
+    "        generation = torch.sigmoid(self.generate(activated))\n",
     "        return generation\n",
     "\n",
     "\n",
@@ -521,7 +521,7 @@
     "        \n",
     "    def forward(self, x):\n",
     "        activated = F.relu(self.linear(x))\n",
-    "        discrimination = F.sigmoid(self.discriminate(activated))\n",
+    "        discrimination = torch.sigmoid(self.discriminate(activated))\n",
     "        return discrimination\n",
     "\n",
     "\n",

diff --git a/notebooks/02-minimax-gan.ipynb b/notebooks/02-minimax-gan.ipynb
@@ -481,7 +481,7 @@
     "the probability that a sample came from the training data rather than G. The training\n",
     "procedure for G is to maximize the probability of D making a mistake.'\n",
     "\"\"\"\n",
-    "\n",
+    "F.\n",
     "import torch, torchvision\n",
     "import torch.nn as nn\n",
     "import torch.nn.functional as F\n",
@@ -519,7 +519,7 @@
     "        \n",
     "    def forward(self, x):\n",
     "        activated = F.relu(self.linear(x))\n",
-    "        generation = F.sigmoid(self.generate(activated))\n",
+    "        generation = torch.sigmoid(self.generate(activated))\n",
     "        return generation\n",
     "\n",
     "\n",
@@ -534,7 +534,7 @@
     "        \n",
     "    def forward(self, x):\n",
     "        activated = F.relu(self.linear(x))\n",
-    "        discrimination = F.sigmoid(self.discriminate(activated))\n",
+    "        discrimination = torch.sigmoid(self.discriminate(activated))\n",
     "        return discrimination\n",
     "\n",
     "\n",

diff --git a/notebooks/03-wasserstein-gan.ipynb b/notebooks/03-wasserstein-gan.ipynb
@@ -1780,7 +1780,7 @@
     "        \n",
     "    def forward(self, x):\n",
     "        activated = F.relu(self.linear(x))\n",
-    "        generation = F.sigmoid(self.generate(activated))\n",
+    "        generation = torch.sigmoid(self.generate(activated))\n",
     "        return generation\n",
     "\n",
     "\n",

diff --git a/notebooks/04-wasserstein-gan-gradient-penalty.ipynb b/notebooks/04-wasserstein-gan-gradient-penalty.ipynb
@@ -498,7 +498,7 @@
     "        \n",
     "    def forward(self, x):\n",
     "        activated = F.relu(self.linear(x))\n",
-    "        generation = F.sigmoid(self.generate(activated))\n",
+    "        generation = torch.sigmoid(self.generate(activated))\n",
     "        return generation\n",
     "\n",
     "\n",

diff --git a/notebooks/05-least-squares-gan.ipynb b/notebooks/05-least-squares-gan.ipynb
@@ -507,7 +507,7 @@
     "        \n",
     "    def forward(self, x):\n",
     "        activated = F.relu(self.linear(x))\n",
-    "        generation = F.sigmoid(self.generate(activated))\n",
+    "        generation = torch.sigmoid(self.generate(activated))\n",
     "        return generation\n",
     "\n",
     "\n",
@@ -523,7 +523,7 @@
     "        \n",
     "    def forward(self, x):\n",
     "        activated = F.relu(self.linear(x))\n",
-    "        discrimination = F.sigmoid(self.discriminate(activated))\n",
+    "        discrimination = torch.sigmoid(self.discriminate(activated))\n",
     "        return discrimination\n",
     "\n",
     "\n",

diff --git a/notebooks/06-deep-regret-analytic-gan.ipynb b/notebooks/06-deep-regret-analytic-gan.ipynb
@@ -1018,7 +1018,7 @@
     "        \n",
     "    def forward(self, x):\n",
     "        activated = F.relu(self.linear(x))\n",
-    "        generation = F.sigmoid(self.generate(activated))\n",
+    "        generation = torch.sigmoid(self.generate(activated))\n",
     "        return generation\n",
     "\n",
     "\n",
@@ -1034,7 +1034,7 @@
     "        \n",
     "    def forward(self, x):\n",
     "        activated = F.relu(self.linear(x))\n",
-    "        discrimination = F.sigmoid(self.discriminate(activated))\n",
+    "        discrimination = torch.sigmoid(self.discriminate(activated))\n",
     "        return discrimination\n",
     "\n",
     "\n",

diff --git a/notebooks/08-variational-autoencoder.ipynb b/notebooks/08-variational-autoencoder.ipynb
diff --git a/notebooks/09-standard-autoencoder.ipynb b/notebooks/09-standard-autoencoder.ipynb
diff --git a/requirements.txt b/requirements.txt
@@ -2,5 +2,5 @@ ipython
 jupyter
 matplotlib
 tqdm
-torch==0.4.0
+torch==0.4.1
 torchvision