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