pytorch学习笔记系列（2）：实现Linear Regression

pytorch实现Linear Regression

• 做预测时numpy()函数不能计算带有requires grad属性的参数，因此需要使用detach

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)
    # 每一步都要重新计算loss
    loss = criterion(outputs, targets)

    # Backward and optimize
    # zero the gradient buffers，必须要置零
    optimizer.zero_grad()
    # 每一步都要重新进行反向传播
    loss.backward()
    # step操作相当于对所有需要更新梯度的参数进行了梯度更新：减去学习率乘以梯度
    optimizer.step()

    if (epoch + 1) % 5 == 0:
        print('Epoch [{}/{}], Loss: {:.4f}'.format(epoch + 1, num_epochs, loss.item()))

# Plot the graph
# numpy()函数不能计算带有requires grad属性的参数，因此需要使用detach
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')