Merge remote-tracking branch 'origin/master'

Author: MorvanZhou

tutorial-contents-notebooks/301_regression.ipynb

@@ -125,7 +125,7 @@
    },
    "outputs": [],
    "source": [
-    "optimizer = torch.optim.SGD(net.parameters(), lr=0.5)\n",
+    "optimizer = torch.optim.SGD(net.parameters(), lr=0.2)\n",
     "loss_func = torch.nn.MSELoss()  # this is for regression mean squared loss"
    ]
   },
@@ -261,9 +261,9 @@
     "        plt.cla()\n",
     "        plt.scatter(x.data.numpy(), y.data.numpy())\n",
     "        plt.plot(x.data.numpy(), prediction.data.numpy(), 'r-', lw=5)\n",
-    "        plt.text(0.5, 0, 'Loss=%.4f' % loss.data[0], fontdict={'size': 20, 'color':  'red'})\n",
+    "        plt.text(0.5, 0, 'Loss=%.4f' % loss.data.numpy(), fontdict={'size': 20, 'color':  'red'})\n",
     "        plt.show()\n",
-    "        plt.pause(0.2)\n",
+    "        plt.pause(0.1)\n",
     "\n",
     "plt.ioff()\n"
    ]

tutorial-contents/502_GPU.py

@@ -70,8 +70,8 @@ def forward(self, x):
             # !!!!!!!! Change in here !!!!!!!!! #
             pred_y = torch.max(test_output, 1)[1].cuda().data.squeeze()  # move the computation in GPU
 
-            accuracy = torch.sum(pred_y == test_y) / test_y.size(0)
-            print('Epoch: ', epoch, '| train loss: %.4f' % loss.data.numpy(), '| test accuracy: %.2f' % accuracy)
+            accuracy = torch.sum(pred_y == test_y).type(torch.FloatTensor) / test_y.size(0)
+            print('Epoch: ', epoch, '| train loss: %.4f' % loss.data.cpu().numpy(), '| test accuracy: %.2f' % accuracy)
 
 
 test_output = cnn(test_x[:10])