Add a Kaggle tutorial
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fashion-mnist-with-pytorch-93-accuracy.ipynb
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fashion-mnist-with-pytorch-93-accuracy.ipynb
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"cell_type": "code",
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"cell_type": "code",
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"execution_count": 3,
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"execution_count": 23,
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"name": "stdout",
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"name": "stdout",
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"output_type": "stream",
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"output_type": "stream",
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"text": [
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"text": [
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"Shape of X [N, C, H, W]: torch.Size([64, 1, 28, 28])\n",
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"Shape of X [N, C, H, W]: torch.Size([200, 1, 28, 28])\n",
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"Shape of y: torch.Size([64]) torch.int64\n"
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"Shape of y: torch.Size([200]) torch.int64\n"
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]
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"source": [
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"source": [
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"batch_size = 64\n",
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"batch_size = 200\n",
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"\n",
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"\n",
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"# Create data loaders.\n",
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"# Create data loaders.\n",
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"train_dataloader = DataLoader(training_data, batch_size=batch_size)\n",
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"train_dataloader = DataLoader(training_data, batch_size=batch_size)\n",
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"cell_type": "code",
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"cell_type": "code",
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"execution_count": 4,
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"execution_count": 24,
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"metadata": {
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"metadata": {
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"collapsed": false,
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"jupyter": {
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"jupyter": {
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"NeuralNetwork(\n",
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"NeuralNetwork(\n",
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" (flatten): Flatten(start_dim=1, end_dim=-1)\n",
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" (flatten): Flatten(start_dim=1, end_dim=-1)\n",
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" (linear_relu_stack): Sequential(\n",
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" (linear_relu_stack): Sequential(\n",
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" (0): Linear(in_features=784, out_features=512, bias=True)\n",
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" (0): Linear(in_features=784, out_features=1024, bias=True)\n",
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" (1): ReLU()\n",
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" (1): ReLU()\n",
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" (2): Linear(in_features=512, out_features=512, bias=True)\n",
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" (2): Linear(in_features=1024, out_features=1024, bias=True)\n",
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" (3): ReLU()\n",
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" (3): ReLU()\n",
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" (4): Linear(in_features=512, out_features=10, bias=True)\n",
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" (4): Linear(in_features=1024, out_features=10, bias=True)\n",
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" (5): ReLU()\n",
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" (5): ReLU()\n",
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" )\n",
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" )\n",
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")\n"
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")\n"
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" super(NeuralNetwork, self).__init__()\n",
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" super(NeuralNetwork, self).__init__()\n",
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" self.flatten = nn.Flatten()\n",
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" self.flatten = nn.Flatten()\n",
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" self.linear_relu_stack = nn.Sequential(\n",
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" self.linear_relu_stack = nn.Sequential(\n",
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" nn.Linear(28*28, 512),\n",
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" nn.Linear(28*28, 1024),\n",
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" nn.ReLU(),\n",
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" nn.ReLU(),\n",
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" nn.Linear(512, 512),\n",
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" nn.Linear(1024, 1024),\n",
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" nn.ReLU(),\n",
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" nn.ReLU(),\n",
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" nn.Linear(512, 10),\n",
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" nn.Linear(1024, 10),\n",
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" nn.ReLU()\n",
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" nn.ReLU()\n",
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" )\n",
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" )\n",
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"\n",
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"\n",
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"cell_type": "code",
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"execution_count": 5,
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"execution_count": 25,
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"cell_type": "code",
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"cell_type": "code",
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"execution_count": 7,
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"execution_count": 28,
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"metadata": {
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"cell_type": "code",
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"cell_type": "code",
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"execution_count": 8,
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"execution_count": 29,
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"metadata": {
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"text": [
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"text": [
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"Epoch 1\n",
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"Epoch 1\n",
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"-------------------------------\n",
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"-------------------------------\n",
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"loss: 2.300270 [ 0/60000]\n",
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"loss: 2.301592 [ 0/60000]\n",
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"loss: 2.290948 [ 6400/60000]\n",
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"loss: 2.289894 [20000/60000]\n",
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"loss: 2.280627 [12800/60000]\n",
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"loss: 2.280160 [40000/60000]\n",
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"loss: 2.283042 [19200/60000]\n",
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"loss: 2.268817 [25600/60000]\n",
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"loss: 2.262104 [32000/60000]\n",
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"loss: 2.248093 [38400/60000]\n",
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"loss: 2.233517 [44800/60000]\n",
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"loss: 2.234299 [51200/60000]\n",
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"loss: 2.234841 [57600/60000]\n",
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"Test Error: \n",
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"Test Error: \n",
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" Accuracy: 51.1%, Avg loss: 2.221716 \n",
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" Accuracy: 30.9%, Avg loss: 2.269258 \n",
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"\n",
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"\n",
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"Epoch 2\n",
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"Epoch 2\n",
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"-------------------------------\n",
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"-------------------------------\n",
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"loss: 2.203925 [ 0/60000]\n",
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"loss: 2.268190 [ 0/60000]\n",
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"loss: 2.200477 [ 6400/60000]\n",
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"loss: 2.259592 [20000/60000]\n",
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"loss: 2.180246 [12800/60000]\n",
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"loss: 2.251709 [40000/60000]\n",
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"loss: 2.213840 [19200/60000]\n",
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"loss: 2.155768 [25600/60000]\n",
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"loss: 2.154045 [32000/60000]\n",
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"loss: 2.130811 [38400/60000]\n",
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"loss: 2.101326 [44800/60000]\n",
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"loss: 2.118105 [51200/60000]\n",
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"loss: 2.116674 [57600/60000]\n",
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"Test Error: \n",
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"Test Error: \n",
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" Accuracy: 51.0%, Avg loss: 2.093179 \n",
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" Accuracy: 34.5%, Avg loss: 2.238048 \n",
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"\n",
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"\n",
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"Epoch 3\n",
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"Epoch 3\n",
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"-------------------------------\n",
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"-------------------------------\n",
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"loss: 2.054594 [ 0/60000]\n",
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"loss: 2.236049 [ 0/60000]\n",
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"loss: 2.047193 [ 6400/60000]\n",
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"loss: 2.230108 [20000/60000]\n",
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"loss: 2.009665 [12800/60000]\n",
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"loss: 2.223023 [40000/60000]\n",
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"loss: 2.093936 [19200/60000]\n",
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"loss: 1.965194 [25600/60000]\n",
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"loss: 1.976750 [32000/60000]\n",
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"loss: 1.938592 [38400/60000]\n",
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"loss: 1.889513 [44800/60000]\n",
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"loss: 1.942611 [51200/60000]\n",
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"loss: 1.936963 [57600/60000]\n",
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"Test Error: \n",
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"Test Error: \n",
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" Accuracy: 51.2%, Avg loss: 1.900150 \n",
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" Accuracy: 34.4%, Avg loss: 2.204923 \n",
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"\n",
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"\n",
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"Epoch 4\n",
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"Epoch 4\n",
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"-------------------------------\n",
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"-------------------------------\n",
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"loss: 1.837173 [ 0/60000]\n",
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"loss: 2.201698 [ 0/60000]\n",
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"loss: 1.822518 [ 6400/60000]\n",
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"loss: 2.199297 [20000/60000]\n",
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"loss: 1.775139 [12800/60000]\n",
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"loss: 2.192280 [40000/60000]\n",
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"loss: 1.925843 [19200/60000]\n",
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"loss: 1.731390 [25600/60000]\n",
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"loss: 1.778743 [32000/60000]\n",
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"loss: 1.714922 [38400/60000]\n",
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"loss: 1.670009 [44800/60000]\n",
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"loss: 1.755909 [51200/60000]\n",
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"loss: 1.763030 [57600/60000]\n",
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"Test Error: \n",
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"Test Error: \n",
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" Accuracy: 52.5%, Avg loss: 1.711389 \n",
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" Accuracy: 34.7%, Avg loss: 2.168218 \n",
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"\n",
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"\n",
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"Epoch 5\n",
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"Epoch 5\n",
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"-------------------------------\n",
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"-------------------------------\n",
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"loss: 1.621799 [ 0/60000]\n",
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"loss: 2.163056 [ 0/60000]\n",
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"loss: 1.615258 [ 6400/60000]\n",
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"loss: 2.165860 [20000/60000]\n",
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"loss: 1.567131 [12800/60000]\n",
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"loss: 2.158360 [40000/60000]\n",
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"loss: 1.768921 [19200/60000]\n",
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"loss: 1.539987 [25600/60000]\n",
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"loss: 1.627408 [32000/60000]\n",
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"loss: 1.533756 [38400/60000]\n",
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"loss: 1.510703 [44800/60000]\n",
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"loss: 1.603583 [51200/60000]\n",
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"loss: 1.636089 [57600/60000]\n",
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"Test Error: \n",
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"Test Error: \n",
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" Accuracy: 53.2%, Avg loss: 1.568043 \n",
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" Accuracy: 35.1%, Avg loss: 2.126790 \n",
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"\n",
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"Epoch 6\n",
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"-------------------------------\n",
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"loss: 2.118984 [ 0/60000]\n",
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"loss: 2.128839 [20000/60000]\n",
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"loss: 2.120741 [40000/60000]\n",
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"Test Error: \n",
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" Accuracy: 35.5%, Avg loss: 2.080902 \n",
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"\n",
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"Epoch 7\n",
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"-------------------------------\n",
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"loss: 2.069693 [ 0/60000]\n",
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"loss: 2.088246 [20000/60000]\n",
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"loss: 2.080452 [40000/60000]\n",
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"Test Error: \n",
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" Accuracy: 35.9%, Avg loss: 2.032795 \n",
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"\n",
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"Epoch 8\n",
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"-------------------------------\n",
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"loss: 2.017604 [ 0/60000]\n",
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"loss: 2.045965 [20000/60000]\n",
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"loss: 2.040246 [40000/60000]\n",
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"Test Error: \n",
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" Accuracy: 36.2%, Avg loss: 1.986216 \n",
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"\n",
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"Epoch 9\n",
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"-------------------------------\n",
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"loss: 1.966350 [ 0/60000]\n",
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"loss: 2.004781 [20000/60000]\n",
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"loss: 2.002753 [40000/60000]\n",
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"Test Error: \n",
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" Accuracy: 36.6%, Avg loss: 1.944349 \n",
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"\n",
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"Epoch 10\n",
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"-------------------------------\n",
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"loss: 1.919495 [ 0/60000]\n",
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"loss: 1.967277 [20000/60000]\n",
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"loss: 1.969415 [40000/60000]\n",
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"Test Error: \n",
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" Accuracy: 37.1%, Avg loss: 1.908297 \n",
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"\n",
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"Epoch 11\n",
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"-------------------------------\n",
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"loss: 1.878579 [ 0/60000]\n",
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"loss: 1.934277 [20000/60000]\n",
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"loss: 1.940233 [40000/60000]\n",
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"Test Error: \n",
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" Accuracy: 38.0%, Avg loss: 1.877542 \n",
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"\n",
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"Epoch 12\n",
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"-------------------------------\n",
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"loss: 1.843474 [ 0/60000]\n",
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"loss: 1.905277 [20000/60000]\n",
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"loss: 1.914412 [40000/60000]\n",
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"Test Error: \n",
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" Accuracy: 38.9%, Avg loss: 1.850760 \n",
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"\n",
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"Epoch 13\n",
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"-------------------------------\n",
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"loss: 1.812682 [ 0/60000]\n",
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"loss: 1.879232 [20000/60000]\n",
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"loss: 1.890868 [40000/60000]\n",
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"Test Error: \n",
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" Accuracy: 39.6%, Avg loss: 1.826784 \n",
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"\n",
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"Epoch 14\n",
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"-------------------------------\n",
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"loss: 1.785155 [ 0/60000]\n",
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"loss: 1.855434 [20000/60000]\n",
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"loss: 1.868896 [40000/60000]\n",
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"Test Error: \n",
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" Accuracy: 40.1%, Avg loss: 1.804814 \n",
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"\n",
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"Epoch 15\n",
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"-------------------------------\n",
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"loss: 1.760130 [ 0/60000]\n",
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"loss: 1.833383 [20000/60000]\n",
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"loss: 1.848235 [40000/60000]\n",
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"Test Error: \n",
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" Accuracy: 40.5%, Avg loss: 1.784439 \n",
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"\n",
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"Epoch 16\n",
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"-------------------------------\n",
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"loss: 1.737113 [ 0/60000]\n",
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"loss: 1.812643 [20000/60000]\n",
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"loss: 1.828667 [40000/60000]\n",
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"Test Error: \n",
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" Accuracy: 40.9%, Avg loss: 1.765419 \n",
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"\n",
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"Epoch 17\n",
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"-------------------------------\n",
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"loss: 1.715825 [ 0/60000]\n",
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"loss: 1.792752 [20000/60000]\n",
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"loss: 1.809863 [40000/60000]\n",
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"Test Error: \n",
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" Accuracy: 41.2%, Avg loss: 1.747591 \n",
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"\n",
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"Epoch 18\n",
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"-------------------------------\n",
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"loss: 1.695961 [ 0/60000]\n",
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"loss: 1.773857 [20000/60000]\n",
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"loss: 1.791943 [40000/60000]\n",
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"Test Error: \n",
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" Accuracy: 41.5%, Avg loss: 1.723384 \n",
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"\n",
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"Epoch 19\n",
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"-------------------------------\n",
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"loss: 1.669920 [ 0/60000]\n",
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"loss: 1.726300 [20000/60000]\n",
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"loss: 1.758728 [40000/60000]\n",
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"Test Error: \n",
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" Accuracy: 42.2%, Avg loss: 1.677625 \n",
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"\n",
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"Epoch 20\n",
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"-------------------------------\n",
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"loss: 1.626003 [ 0/60000]\n",
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"loss: 1.674431 [20000/60000]\n",
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"loss: 1.733464 [40000/60000]\n",
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"Test Error: \n",
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" Accuracy: 43.5%, Avg loss: 1.645380 \n",
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"\n",
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"\n",
|
||||||
"Done!\n"
|
"Done!\n"
|
||||||
]
|
]
|
||||||
}
|
}
|
||||||
],
|
],
|
||||||
"source": [
|
"source": [
|
||||||
"epochs = 5\n",
|
"epochs = 20\n",
|
||||||
"for t in range(epochs):\n",
|
"for t in range(epochs):\n",
|
||||||
" print(f\"Epoch {t+1}\\n-------------------------------\")\n",
|
" print(f\"Epoch {t+1}\\n-------------------------------\")\n",
|
||||||
" train(train_dataloader, model, loss_fn, optimizer)\n",
|
" train(train_dataloader, model, loss_fn, optimizer)\n",
|
||||||
|
|
@ -504,7 +589,7 @@
|
||||||
},
|
},
|
||||||
{
|
{
|
||||||
"cell_type": "code",
|
"cell_type": "code",
|
||||||
"execution_count": 12,
|
"execution_count": 9,
|
||||||
"metadata": {
|
"metadata": {
|
||||||
"collapsed": false,
|
"collapsed": false,
|
||||||
"jupyter": {
|
"jupyter": {
|
||||||
|
|
@ -539,7 +624,7 @@
|
||||||
},
|
},
|
||||||
{
|
{
|
||||||
"cell_type": "code",
|
"cell_type": "code",
|
||||||
"execution_count": 13,
|
"execution_count": 10,
|
||||||
"metadata": {
|
"metadata": {
|
||||||
"collapsed": false,
|
"collapsed": false,
|
||||||
"jupyter": {
|
"jupyter": {
|
||||||
|
|
@ -553,7 +638,7 @@
|
||||||
"<All keys matched successfully>"
|
"<All keys matched successfully>"
|
||||||
]
|
]
|
||||||
},
|
},
|
||||||
"execution_count": 13,
|
"execution_count": 10,
|
||||||
"metadata": {},
|
"metadata": {},
|
||||||
"output_type": "execute_result"
|
"output_type": "execute_result"
|
||||||
}
|
}
|
||||||
|
|
@ -573,7 +658,7 @@
|
||||||
},
|
},
|
||||||
{
|
{
|
||||||
"cell_type": "code",
|
"cell_type": "code",
|
||||||
"execution_count": 17,
|
"execution_count": 22,
|
||||||
"metadata": {
|
"metadata": {
|
||||||
"collapsed": false,
|
"collapsed": false,
|
||||||
"jupyter": {
|
"jupyter": {
|
||||||
|
|
@ -585,7 +670,56 @@
|
||||||
"name": "stdout",
|
"name": "stdout",
|
||||||
"output_type": "stream",
|
"output_type": "stream",
|
||||||
"text": [
|
"text": [
|
||||||
"Predicted: \"Ankle boot\", Actual: \"Ankle boot\"\n"
|
"Predicted: \"Sandal\", Actual: \"Ankle boot\"\n",
|
||||||
|
"Predicted: \"Shirt\", Actual: \"Pullover\"\n",
|
||||||
|
"Predicted: \"Trouser\", Actual: \"Trouser\"\n",
|
||||||
|
"Predicted: \"Trouser\", Actual: \"Trouser\"\n",
|
||||||
|
"Predicted: \"Shirt\", Actual: \"Shirt\"\n",
|
||||||
|
"Predicted: \"Trouser\", Actual: \"Trouser\"\n",
|
||||||
|
"Predicted: \"Shirt\", Actual: \"Coat\"\n",
|
||||||
|
"Predicted: \"Shirt\", Actual: \"Shirt\"\n",
|
||||||
|
"Predicted: \"Sneaker\", Actual: \"Sandal\"\n",
|
||||||
|
"Predicted: \"Sneaker\", Actual: \"Sneaker\"\n",
|
||||||
|
"Predicted: \"T-shirt/top\", Actual: \"Coat\"\n",
|
||||||
|
"Predicted: \"Sandal\", Actual: \"Sandal\"\n",
|
||||||
|
"Predicted: \"Sandal\", Actual: \"Sneaker\"\n",
|
||||||
|
"Predicted: \"Dress\", Actual: \"Dress\"\n",
|
||||||
|
"Predicted: \"Shirt\", Actual: \"Coat\"\n",
|
||||||
|
"Predicted: \"Trouser\", Actual: \"Trouser\"\n",
|
||||||
|
"Predicted: \"Shirt\", Actual: \"Pullover\"\n",
|
||||||
|
"Predicted: \"Shirt\", Actual: \"Coat\"\n",
|
||||||
|
"Predicted: \"Bag\", Actual: \"Bag\"\n",
|
||||||
|
"Predicted: \"T-shirt/top\", Actual: \"T-shirt/top\"\n",
|
||||||
|
"Predicted: \"Shirt\", Actual: \"Pullover\"\n",
|
||||||
|
"Predicted: \"Sneaker\", Actual: \"Sandal\"\n",
|
||||||
|
"Predicted: \"Sneaker\", Actual: \"Sneaker\"\n",
|
||||||
|
"Predicted: \"Sneaker\", Actual: \"Ankle boot\"\n",
|
||||||
|
"Predicted: \"Trouser\", Actual: \"Trouser\"\n",
|
||||||
|
"Predicted: \"Shirt\", Actual: \"Coat\"\n",
|
||||||
|
"Predicted: \"Shirt\", Actual: \"Shirt\"\n",
|
||||||
|
"Predicted: \"Dress\", Actual: \"T-shirt/top\"\n",
|
||||||
|
"Predicted: \"Sandal\", Actual: \"Ankle boot\"\n",
|
||||||
|
"Predicted: \"Dress\", Actual: \"Dress\"\n",
|
||||||
|
"Predicted: \"Bag\", Actual: \"Bag\"\n",
|
||||||
|
"Predicted: \"Bag\", Actual: \"Bag\"\n",
|
||||||
|
"Predicted: \"Dress\", Actual: \"Dress\"\n",
|
||||||
|
"Predicted: \"Dress\", Actual: \"Dress\"\n",
|
||||||
|
"Predicted: \"Bag\", Actual: \"Bag\"\n",
|
||||||
|
"Predicted: \"T-shirt/top\", Actual: \"T-shirt/top\"\n",
|
||||||
|
"Predicted: \"Sneaker\", Actual: \"Sneaker\"\n",
|
||||||
|
"Predicted: \"Sandal\", Actual: \"Sandal\"\n",
|
||||||
|
"Predicted: \"Sneaker\", Actual: \"Sneaker\"\n",
|
||||||
|
"Predicted: \"T-shirt/top\", Actual: \"Ankle boot\"\n",
|
||||||
|
"Predicted: \"T-shirt/top\", Actual: \"Shirt\"\n",
|
||||||
|
"Predicted: \"Trouser\", Actual: \"Trouser\"\n",
|
||||||
|
"Predicted: \"Dress\", Actual: \"Dress\"\n",
|
||||||
|
"Predicted: \"Sneaker\", Actual: \"Sneaker\"\n",
|
||||||
|
"Predicted: \"Shirt\", Actual: \"Shirt\"\n",
|
||||||
|
"Predicted: \"Sandal\", Actual: \"Sneaker\"\n",
|
||||||
|
"Predicted: \"Shirt\", Actual: \"Pullover\"\n",
|
||||||
|
"Predicted: \"Trouser\", Actual: \"Trouser\"\n",
|
||||||
|
"Predicted: \"Trouser\", Actual: \"Pullover\"\n",
|
||||||
|
"Predicted: \"Shirt\", Actual: \"Pullover\"\n"
|
||||||
]
|
]
|
||||||
}
|
}
|
||||||
],
|
],
|
||||||
|
|
@ -604,21 +738,20 @@
|
||||||
"]\n",
|
"]\n",
|
||||||
"\n",
|
"\n",
|
||||||
"model.eval()\n",
|
"model.eval()\n",
|
||||||
"x, y = test_data[0][0], test_data[0][1]\n",
|
"for i in range(0, 50):\n",
|
||||||
"with torch.no_grad():\n",
|
" x, y = test_data[i][0], test_data[i][1]\n",
|
||||||
" pred = model(x)\n",
|
" with torch.no_grad():\n",
|
||||||
" predicted, actual = classes[pred[0].argmax(0)], classes[y]\n",
|
" pred = model(x)\n",
|
||||||
" print(f'Predicted: \"{predicted}\", Actual: \"{actual}\"')"
|
" predicted, actual = classes[pred[0].argmax(0)], classes[y]\n",
|
||||||
|
" print(f'Predicted: \"{predicted}\", Actual: \"{actual}\"')"
|
||||||
]
|
]
|
||||||
},
|
},
|
||||||
{
|
{
|
||||||
"cell_type": "markdown",
|
"cell_type": "code",
|
||||||
|
"execution_count": null,
|
||||||
"metadata": {},
|
"metadata": {},
|
||||||
"source": [
|
"outputs": [],
|
||||||
"Read more about `Saving & Loading your model <saveloadrun_tutorial.html>`_.\n",
|
"source": []
|
||||||
"\n",
|
|
||||||
"\n"
|
|
||||||
]
|
|
||||||
}
|
}
|
||||||
],
|
],
|
||||||
"metadata": {
|
"metadata": {
|
||||||
|
|
|
||||||
35
requirements.txt
Normal file
35
requirements.txt
Normal file
|
|
@ -0,0 +1,35 @@
|
||||||
|
backcall==0.2.0
|
||||||
|
cycler==0.10.0
|
||||||
|
debugpy==1.4.1
|
||||||
|
decorator==5.0.9
|
||||||
|
entrypoints==0.3
|
||||||
|
ipykernel==6.2.0
|
||||||
|
ipython==7.26.0
|
||||||
|
ipython-genutils==0.2.0
|
||||||
|
jedi==0.18.0
|
||||||
|
jupyter-client==7.0.1
|
||||||
|
jupyter-core==4.7.1
|
||||||
|
kiwisolver==1.3.1
|
||||||
|
matplotlib==3.4.3
|
||||||
|
matplotlib-inline==0.1.2
|
||||||
|
nest-asyncio==1.5.1
|
||||||
|
numpy==1.21.2
|
||||||
|
pandas==1.3.2
|
||||||
|
parso==0.8.2
|
||||||
|
pexpect==4.8.0
|
||||||
|
pickleshare==0.7.5
|
||||||
|
Pillow==8.3.1
|
||||||
|
prompt-toolkit==3.0.20
|
||||||
|
ptyprocess==0.7.0
|
||||||
|
Pygments==2.10.0
|
||||||
|
pyparsing==2.4.7
|
||||||
|
python-dateutil==2.8.2
|
||||||
|
pytz==2021.1
|
||||||
|
pyzmq==22.2.1
|
||||||
|
six==1.16.0
|
||||||
|
torch==1.9.0
|
||||||
|
torchvision==0.10.0
|
||||||
|
tornado==6.1
|
||||||
|
traitlets==5.0.5
|
||||||
|
typing-extensions==3.10.0.0
|
||||||
|
wcwidth==0.2.5
|
||||||
Loading…
Reference in a new issue