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03_linear_regression.py
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# -*- coding: utf-8 -*-
# @Author : Miaoshuyu
# @Email : [email protected]
import torch
import torch.nn as nn
from tensorboardX import SummaryWriter
import matplotlib.pyplot as plt
import numpy as np
input_size = 1
output_size = 1
num_epoches = 60
learning_rate = 0.01
writer = SummaryWriter(comment='Linear')
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_epoches):
inputs = torch.from_numpy(x_train)
targets = torch.from_numpy(y_train)
output = model(inputs)
loss = criterion(output, targets)
optimizer.zero_grad()
loss.backward()
optimizer.step()
# 保存loss的数据与epoch数值
writer.add_scalar('Train', loss, epoch)
if (epoch + 1) % 5 == 0:
print('Epoch {}/{},loss:{:.4f}'.format(epoch + 1, num_epoches, loss.item()))
# 将model保存为graph
writer.add_graph(model, (inputs,))
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()
writer.close()