Merge pull request #53 from jaintj95/master

Replace model.forward() with model()

Author: mcleonard

intro-to-pytorch/Part 3 - Training Neural Networks (Exercises).ipynb

@@ -380,7 +380,7 @@
     "optimizer.zero_grad()\n",
     "\n",
     "# Forward pass, then backward pass, then update weights\n",
-    "output = model.forward(images)\n",
+    "output = model(images)\n",
     "loss = criterion(output, labels)\n",
     "loss.backward()\n",
     "print('Gradient -', model[0].weight.grad)"
@@ -463,7 +463,7 @@
     "img = images[0].view(1, 784)\n",
     "# Turn off gradients to speed up this part\n",
     "with torch.no_grad():\n",
-    "    logits = model.forward(img)\n",
+    "    logits = model(img)\n",
     "\n",
     "# Output of the network are logits, need to take softmax for probabilities\n",
     "ps = F.softmax(logits, dim=1)\n",

intro-to-pytorch/Part 3 - Training Neural Networks (Solution).ipynb

@@ -494,7 +494,7 @@
     "optimizer.zero_grad()\n",
     "\n",
     "# Forward pass, then backward pass, then update weights\n",
-    "output = model.forward(images)\n",
+    "output = model(images)\n",
     "loss = criterion(output, labels)\n",
     "loss.backward()\n",
     "print('Gradient -', model[0].weight.grad)"
@@ -581,7 +581,7 @@
     "        # TODO: Training pass\n",
     "        optimizer.zero_grad()\n",
     "        \n",
-    "        output = model.forward(images)\n",
+    "        output = model(images)\n",
     "        loss = criterion(output, labels)\n",
     "        loss.backward()\n",
     "        optimizer.step()\n",
@@ -625,7 +625,7 @@
     "img = images[0].view(1, 784)\n",
     "# Turn off gradients to speed up this part\n",
     "with torch.no_grad():\n",
-    "    logps = model.forward(img)\n",
+    "    logps = model(img)\n",
     "\n",
     "# Output of the network are logits, need to take softmax for probabilities\n",
     "ps = torch.exp(logps)\n",