112 lines
3.5 KiB
Python
112 lines
3.5 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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# Device configuration
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device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
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# Hyper-parameters
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sequence_length = 28
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input_size = 28
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hidden_size = 128
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num_layers = 2
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num_classes = 10
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batch_size = 100
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num_epochs = 2
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learning_rate = 0.01
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# MNIST dataset
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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
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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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# Recurrent neural network (many-to-one)
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class RNN(nn.Module):
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def __init__(self, input_size, hidden_size, num_layers, num_classes):
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super(RNN, self).__init__()
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self.hidden_size = hidden_size
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self.num_layers = num_layers
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self.lstm = nn.LSTM(input_size, hidden_size,
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num_layers, batch_first=True)
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self.fc = nn.Linear(hidden_size, num_classes)
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def forward(self, x):
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# Set initial hidden and cell states
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h0 = torch.zeros(self.num_layers, x.size(0),
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self.hidden_size).to(device)
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c0 = torch.zeros(self.num_layers, x.size(0),
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self.hidden_size).to(device)
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# Forward propagate LSTM
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# out: tensor of shape (batch_size, seq_length, hidden_size)
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out, _ = self.lstm(x, (h0, c0))
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# Decode the hidden state of the last time step
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out = self.fc(out[:, -1, :])
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return out
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model = RNN(input_size, hidden_size, num_layers, num_classes).to(device)
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# Loss and optimizer
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criterion = nn.CrossEntropyLoss()
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optimizer = torch.optim.Adam(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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images = images.reshape(-1, sequence_length, input_size).to(device)
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labels = labels.to(device)
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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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model.eval()
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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, sequence_length, input_size).to(device)
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labels = labels.to(device)
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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().item()
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print('Test Accuracy of the model on the 10000 test images: {} %'.format(
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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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