PyTorch Tutorial 07 - Linear Regressi...

教程Python代码如下：

# 1) Design model(input, output size, forward pass)

# 2) Construct loss and optimizer

# 3) Training loop 训练循环

#  - forward pass: compute prediction

#  - backward pass: gradients

#  - update weights

import torch

import torch.nn as nn

import numpy as np

# ModuleNotFoundError: No module named 'sklearn'：需要注意报错的sklearn是scikit-learn缩写，pip install scikit-learn

# 清华源：pip install scikit-learn -i https://pypi.tuna.tsinghua.edu.cn/simple

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))

#重塑张量

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 #学习速率

criterion = nn.MSELoss() #均方误差

optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate) #优化器

# 3) training loop

num_epochs = 100

for epoch in range(num_epochs):

  # forward pass

  y_predicted = model(X)

  loss = criterion(y_predicted,Y)

  # backward pass

  loss.backward()

  # update

  optimizer.step()

  optimizer.zero_grad() # 清空grad

  if(epoch+1) % 10 == 0:

    print(f'epoch: {epoch+1}, loss = {loss.item():.4f}')

#plot,Matplotlib下的函数，plot函数：绘图

predicted = model(X).detach().numpy()

plt.plot(X_numpy, Y_numpy,'ro')

plt.plot(X_numpy, predicted, 'b')

plt.show()