linear_regression.py

import torch
import torch.nn as nn
import numpy as np
import matplotlib.pyplot as plt

input_size = 1
output_size = 1
num_epochs = 100
learning_rate = .001

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)

model = nn.Linear(input_size, output_size)

criterion = nn.MSELoss()
optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate)

for epoch in range(num_epochs):
    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()))

# 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()