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Remove hardcoded values and use train() and evaluate() functions' input parameters #199

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Remove hardcoded values and use train() and evaluate() functions' input parameters #199

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4973561
Update PT_Part1_MNIST_Solution.ipynb
1Nigar357 Jul 26, 2025
622caca
Update PT_Part1_MNIST.ipynb
1Nigar357 Jul 26, 2025
2b1f75c
Update PT_Part1_MNIST_Solution.ipynb
1Nigar357 Jul 26, 2025
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8 changes: 4 additions & 4 deletions lab2/PT_Part1_MNIST.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -413,17 +413,17 @@
" correct_pred = 0\n",
" total_pred = 0\n",
"\n",
" for images, labels in trainset_loader:\n",
" for images, labels in dataloader:\n",
" # Move tensors to GPU so compatible with model\n",
" images, labels = images.to(device), labels.to(device)\n",
"\n",
" # Forward pass\n",
" outputs = fc_model(images)\n",
" outputs = model(images)\n",
"\n",
" # Clear gradients before performing backward pass\n",
" optimizer.zero_grad()\n",
" # Calculate loss based on model predictions\n",
" loss = loss_function(outputs, labels)\n",
" loss = criterion(outputs, labels)\n",
" # Backpropagate and update model parameters\n",
" loss.backward()\n",
" optimizer.step()\n",
Expand Down Expand Up @@ -498,7 +498,7 @@
" total_pred = 0\n",
" # Disable gradient calculations when in inference mode\n",
" with torch.no_grad():\n",
" for images, labels in testset_loader:\n",
" for images, labels in dataloader:\n",
" # TODO: ensure evalaution happens on the GPU\n",
" images, labels = # TODO\n",
"\n",
Expand Down
12 changes: 6 additions & 6 deletions lab2/solutions/PT_Part1_MNIST_Solution.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -417,15 +417,15 @@
" correct_pred = 0\n",
" total_pred = 0\n",
"\n",
" for images, labels in trainset_loader:\n",
" for images, labels in dataloader:\n",
" # Move tensors to GPU so compatible with model\n",
" images, labels = images.to(device), labels.to(device)\n",
" # Clear gradients before performing backward pass\n",
" optimizer.zero_grad()\n",
" # Forward pass\n",
" outputs = fc_model(images)\n",
" outputs = model(images)\n",
" # Calculate loss based on model predictions\n",
" loss = loss_function(outputs, labels)\n",
" loss = criterion(outputs, labels)\n",
" # Backpropagate and update model parameters\n",
" loss.backward()\n",
" optimizer.step()\n",
Expand Down Expand Up @@ -500,7 +500,7 @@
" total_pred = 0\n",
" # Disable gradient calculations when in inference mode\n",
" with torch.no_grad():\n",
" for images, labels in testset_loader:\n",
" for images, labels in dataloader:\n",
" # TODO: ensure evalaution happens on the GPU\n",
" images, labels = images.to(device), labels.to(device)\n",
" # images, labels = # TODO\n",
Expand Down Expand Up @@ -533,7 +533,7 @@
" return test_loss, test_acc\n",
"\n",
"# TODO: call the evaluate function to evaluate the trained model!!\n",
"test_loss, test_acc = evaluate(fc_model, trainset_loader, loss_function)\n",
"test_loss, test_acc = evaluate(fc_model, testset_loader, loss_function)\n",
"# test_loss, test_acc = # TODO\n",
"\n",
"print('Test accuracy:', test_acc)"
Expand Down Expand Up @@ -1026,4 +1026,4 @@
},
"nbformat": 4,
"nbformat_minor": 0
}
}