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",