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updating fixes and issues of notebook #11

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updating fixes and issues of notebook #11

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24 changes: 12 additions & 12 deletions notebooks/pytorch_mnist.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -21,8 +21,6 @@
"outputs": [],
"source": [
"#Import Libraries\n",
"\n",
"\n",
"import argparse\n",
"import torch\n",
"import torch.nn as nn\n",
Expand Down Expand Up @@ -91,7 +89,7 @@
"class Net(nn.Module):\n",
" #This defines the structure of the NN.\n",
" def __init__(self):\n",
" NUM_CLASSES = 0\n",
" NUM_CLASSES = 10\n",
" super(Net, self).__init__()\n",
" self.conv1 = nn.Conv2d(1, 10, kernel_size=5)\n",
" self.conv2 = nn.Conv2d(10, 20, kernel_size=5)\n",
Expand Down Expand Up @@ -149,14 +147,16 @@
" model.eval()\n",
" test_loss = 0\n",
" correct = 0\n",
" for data, target in test_loader:\n",
" if CUDA:\n",
" data, target = data.cuda(), target.cuda()\n",
" data, target = Variable(data, volatile=True), Variable(target)\n",
" output = model(data)\n",
" test_loss += F.nll_loss(output, target, size_average=False).data # sum up batch loss\n",
" pred = output.data.max(1, keepdim=True)[1] # get the index of the max log-probability\n",
" # ToDo: correct = ???\n",
" with torch.no_grad():\n",
" for data, target in test_loader:\n",
" if CUDA:\n",
" data, target = data.cuda(), target.cuda()\n",
" data, target = Variable(data), Variable(target)\n",
" output = model(data)\n",
" test_loss += F.nll_loss(output, target, reduction='sum').item() # sum up batch loss\n",
" pred = output.data.max(1, keepdim=True)[1] # get the index of the max log-probability\n",
" # ToDo: correct = ???\n",
" correct += pred.eq(target.view_as(pred)).sum().item()\n",
"\n",
" test_loss /= len(test_loader.dataset)\n",
" print('\\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\\n'.format(\n",
Expand Down Expand Up @@ -216,7 +216,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.7"
"version": "3.11.0rc1"
}
},
"nbformat": 4,
Expand Down