# yunjey/pytorch-tutorial

Switch branches/tags
Nothing to show
78c6afe May 10, 2018
1 contributor

### Users who have contributed to this file

55 lines (43 sloc) 1.52 KB
 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')