linear_regression.py

# 1) Design model (input, output size, forward pass)
# 2) Construct loss and optimizer
# 3) Training loop
#     - forward pass: compute prediction
#     - backward pass: gradients
#     - update weights


import torch
import torch.nn as nn
import numpy as np
from sklearn import datasets
import matplotlib.pyplot as plt

# 0) prepare data
X_numpy, y_numpy = datasets.make_regression(n_samples=100, n_features=1, noise=20, random_state=1)

X = torch.from_numpy(X_numpy.astype(np.float32))
y = torch.from_numpy(y_numpy.astype(np.float32))

y = y.view(y.shape[0], 1)
n_samples, n_features = X.shape

# 1) model
input_size = n_features
output_size = 1
model = nn.Linear(input_size, output_size)

# 2) loss and optimizer
learning_rate = 0.01
criterion = nn.MSELoss()
optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate)

# 3) training loop
num_epoch = 100
for epoch in range(num_epoch):
    y_predicted = model(X)
    loss = criterion(y_predicted, y)

    #backward pass
    loss.backward()

    #update
    optimizer.step()

    #empty gardient
    optimizer.zero_grad()

    if epoch % 10 == 0:
        [w, b] = model.parameters()
        print(f'epoch {epoch+1}: loss = {loss.item():.8f}')

#plot
predicted = model(X).detach().numpy()
plt.plot(X_numpy, y_numpy, 'ro')
plt.plot(X_numpy, predicted, 'b')
plt.show()