update for new version of torch

Author: MorvanZhou

tutorial-contents-notebooks/203_activation.ipynb

@@ -59,10 +59,18 @@
    "metadata": {
     "collapsed": true
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\morvanzhou\\AppData\\Local\\Programs\\Python\\Python36\\lib\\site-packages\\torch\\nn\\functional.py:1006: UserWarning: nn.functional.sigmoid is deprecated. Use torch.sigmoid instead.\n  warnings.warn(\"nn.functional.sigmoid is deprecated. Use torch.sigmoid instead.\")\nC:\\Users\\morvanzhou\\AppData\\Local\\Programs\\Python\\Python36\\lib\\site-packages\\torch\\nn\\functional.py:995: UserWarning: nn.functional.tanh is deprecated. Use torch.tanh instead.\n  warnings.warn(\"nn.functional.tanh is deprecated. Use torch.tanh instead.\")\n"
+     ]
+    }
+   ],
    "source": [
     "y_relu = F.relu(x).data.numpy()\n",
-    "y_sigmoid = F.sigmoid(x).data.numpy()\n",
+    "y_sigmoid = torch.sigmoid(x).data.numpy()\n",
     "y_tanh = F.tanh(x).data.numpy()\n",
     "y_softplus = F.softplus(x).data.numpy()\n",
     "\n",

tutorial-contents-notebooks/306_optimizer.ipynb

@@ -249,7 +249,7 @@
     "            opt.zero_grad()                # clear gradients for next train\n",
     "            loss.backward()                # backpropagation, compute gradients\n",
     "            opt.step()                     # apply gradients\n",
-    "            l_his.append(loss.data[0])     # loss recoder\n",
+    "            l_his.append(loss.item())     # loss recoder\n",
     "\n",
     "labels = ['SGD', 'Momentum', 'RMSprop', 'Adam']\n",
     "for i, l_his in enumerate(losses_his):\n",