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77 lines
2.5 KiB
Python
77 lines
2.5 KiB
Python
import torch
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import torch.nn as nn
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import torchvision
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import torchvision.transforms as transforms
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# Hyper-parameters
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input_size = 28 * 28 # 784
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num_classes = 10
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num_epochs = 5
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batch_size = 100
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learning_rate = 0.001
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# MNIST dataset (images and labels)
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train_dataset = torchvision.datasets.MNIST(root='../../data',
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train=True,
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transform=transforms.ToTensor(),
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download=True)
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test_dataset = torchvision.datasets.MNIST(root='../../data',
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train=False,
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transform=transforms.ToTensor())
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# Data loader (input pipeline)
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train_loader = torch.utils.data.DataLoader(dataset=train_dataset,
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batch_size=batch_size,
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shuffle=True)
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test_loader = torch.utils.data.DataLoader(dataset=test_dataset,
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batch_size=batch_size,
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shuffle=False)
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# Logistic regression model
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model = nn.Linear(input_size, num_classes)
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# Loss and optimizer
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# nn.CrossEntropyLoss() computes softmax internally
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criterion = nn.CrossEntropyLoss()
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optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate)
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# Train the model
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total_step = len(train_loader)
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for epoch in range(num_epochs):
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for i, (images, labels) in enumerate(train_loader):
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# Reshape images to (batch_size, input_size)
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images = images.reshape(-1, input_size)
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# Forward pass
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outputs = model(images)
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loss = criterion(outputs, labels)
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# Backward and optimize
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optimizer.zero_grad()
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loss.backward()
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optimizer.step()
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if (i+1) % 100 == 0:
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print ('Epoch [{}/{}], Step [{}/{}], Loss: {:.4f}'
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.format(epoch+1, num_epochs, i+1, total_step, loss.item()))
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# Test the model
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# In test phase, we don't need to compute gradients (for memory efficiency)
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with torch.no_grad():
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correct = 0
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total = 0
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for images, labels in test_loader:
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images = images.reshape(-1, input_size)
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outputs = model(images)
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_, predicted = torch.max(outputs.data, 1)
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total += labels.size(0)
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correct += (predicted == labels).sum()
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print('Accuracy of the model on the 10000 test images: {} %'.format(100 * correct / total))
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# Save the model checkpoint
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torch.save(model.state_dict(), 'model.ckpt')
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