Merge pull request #60 from AllanHasegawa/fix/crash_when_batch_size_uneven

Fix crash when using an uneven batch size

Author: jchernus

convolutional-neural-networks/mnist-mlp/mnist_mlp_exercise.ipynb

@@ -309,7 +309,7 @@
     "    # compare predictions to true label\n",
     "    correct = np.squeeze(pred.eq(target.data.view_as(pred)))\n",
     "    # calculate test accuracy for each object class\n",
-    "    for i in range(batch_size):\n",
+    "    for i in range(len(target)):\n",
     "        label = target.data[i]\n",
     "        class_correct[label] += correct[i].item()\n",
     "        class_total[label] += 1\n",

convolutional-neural-networks/mnist-mlp/mnist_mlp_solution.ipynb

@@ -424,7 +424,7 @@
     "    # compare predictions to true label\n",
     "    correct = np.squeeze(pred.eq(target.data.view_as(pred)))\n",
     "    # calculate test accuracy for each object class\n",
-    "    for i in range(batch_size):\n",
+    "    for i in range(len(target)):\n",
     "        label = target.data[i]\n",
     "        class_correct[label] += correct[i].item()\n",
     "        class_total[label] += 1\n",

convolutional-neural-networks/mnist-mlp/mnist_mlp_solution_with_validation.ipynb

@@ -509,7 +509,7 @@
     "    # compare predictions to true label\n",
     "    correct = np.squeeze(pred.eq(target.data.view_as(pred)))\n",
     "    # calculate test accuracy for each object class\n",
-    "    for i in range(batch_size):\n",
+    "    for i in range(len(target)):\n",
     "        label = target.data[i]\n",
     "        class_correct[label] += correct[i].item()\n",
     "        class_total[label] += 1\n",