mirror of
https://github.com/kuhyx/WUT_Computer_Science.git
synced 2026-07-04 20:43:06 +02:00
122 lines
3.4 KiB
Python
122 lines
3.4 KiB
Python
import torch
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import torch.nn as nn
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import torch.optim as optim
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from torchvision import datasets, transforms
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import matplotlib.pyplot as plt
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# Set random seed for reproducibility
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torch.manual_seed(42)
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# Define hyperparameters
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learning_rate = 0.001
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batch_size = 64
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num_epochs = 10
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input_size = 28 * 28 # MNIST images are 28x28 pixels
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hidden_size = 128
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num_classes = 10
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# Load MNIST dataset and apply transformations
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train_dataset = datasets.MNIST(
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root='./data', train=True, transform=transforms.ToTensor(), download=True
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)
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test_dataset = datasets.MNIST(
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root='./data', train=False, transform=transforms.ToTensor(), download=True
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)
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# Create data loaders
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train_loader = torch.utils.data.DataLoader(
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dataset=train_dataset, batch_size=batch_size, shuffle=True
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)
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test_loader = torch.utils.data.DataLoader(
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dataset=test_dataset, batch_size=batch_size, shuffle=False
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)
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# Define the multilayer perceptron model
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model = nn.Sequential(
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nn.Linear(input_size, hidden_size),
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nn.ReLU(),
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nn.Linear(hidden_size, num_classes)
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)
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# Loss function
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criterion = nn.CrossEntropyLoss()
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# Optimizer
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optimizer = optim.Adam(model.parameters(), lr=learning_rate)
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# Lists to store loss and accuracy values
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loss_values = []
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train_acc_values = []
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val_acc_values = []
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# Training loop
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for epoch in range(num_epochs):
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for batch_idx, (data, targets) in enumerate(train_loader):
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# Reshape the input data
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data = data.view(data.size(0), -1)
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# Forward pass
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outputs = model(data)
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loss = criterion(outputs, targets)
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# Backward pass and optimization
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optimizer.zero_grad()
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loss.backward()
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optimizer.step()
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# Append loss value for every learning step
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loss_values.append(loss.item())
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# Calculate accuracy on train set after each epoch
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correct = 0
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total = 0
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for data, targets in train_loader:
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data = data.view(data.size(0), -1)
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outputs = model(data)
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_, predicted = torch.max(outputs.data, 1)
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total += targets.size(0)
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correct += (predicted == targets).sum().item()
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train_accuracy = 100 * correct / total
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train_acc_values.append(train_accuracy)
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# Calculate accuracy on validation set after each epoch
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correct = 0
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total = 0
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for data, targets in test_loader:
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data = data.view(data.size(0), -1)
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outputs = model(data)
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_, predicted = torch.max(outputs.data, 1)
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total += targets.size(0)
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correct += (predicted == targets).sum().item()
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validation_accuracy = 100 * correct / total
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val_acc_values.append(validation_accuracy)
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# Print loss value for every learning step
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print(f'Epoch [{epoch+1}/{num_epochs}], Loss: {loss_values[-1]:.4f}, Train Accuracy: {train_accuracy:.2f}%, Validation Accuracy: {validation_accuracy:.2f}%')
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# Plot the loss value for every learning step
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plt.plot(loss_values)
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plt.xlabel('Learning Step')
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plt.ylabel('Loss')
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plt.title('Loss Value')
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plt.savefig('loss_value.png')
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plt.show()
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# Plot the accuracy on train set after each epoch
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plt.plot(train_acc_values)
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plt.xlabel('Epoch')
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plt.ylabel('Train Accuracy')
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plt.title('Accuracy on Train Set')
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plt.savefig('train_accuracy.png')
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plt.show()
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# Plot the accuracy on validation set after each epoch
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plt.plot(val_acc_values)
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plt.xlabel('Epoch')
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plt.ylabel('Validation Accuracy')
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plt.title('Accuracy on Validation Set')
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plt.savefig('validation_accuracy.png')
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plt.show()
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