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- import torch
- import torch.nn as nn
- import numpy as np
- import matplotlib.pyplot as plt
- # Hyper-parameters
- input_size = 1
- output_size = 1
- num_epochs = 60
- learning_rate = 0.001
- # Toy dataset
- x_train = np.array([[3.3], [4.4], [5.5], [6.71], [6.93], [4.168],
- [9.779], [6.182], [7.59], [2.167], [7.042],
- [10.791], [5.313], [7.997], [3.1]], dtype=np.float32)
- y_train = np.array([[1.7], [2.76], [2.09], [3.19], [1.694], [1.573],
- [3.366], [2.596], [2.53], [1.221], [2.827],
- [3.465], [1.65], [2.904], [1.3]], dtype=np.float32)
- # Linear regression model
- model = nn.Linear(input_size, output_size)
- # Loss and optimizer
- criterion = nn.MSELoss()
- optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate)
- # Train the model
- for epoch in range(num_epochs):
- # Convert numpy arrays to torch tensors
- inputs = torch.from_numpy(x_train)
- targets = torch.from_numpy(y_train)
- # Forward pass
- outputs = model(inputs)
- loss = criterion(outputs, targets)
- # Backward and optimize
- optimizer.zero_grad()
- loss.backward()
- optimizer.step()
- if (epoch+1) % 5 == 0:
- print ('Epoch [{}/{}], Loss: {:.4f}'.format(epoch+1, num_epochs, loss.item()))
- # Plot the graph
- predicted = model(torch.from_numpy(x_train)).detach().numpy()
- plt.plot(x_train, y_train, 'ro', label='Original data')
- plt.plot(x_train, predicted, label='Fitted line')
- plt.legend()
- plt.show()
- # Save the model checkpoint
- torch.save(model.state_dict(), 'model.ckpt')
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