pytorch 中使用 tensorboard，常用 demo

一、代码 demo

import torch
import torch.nn as nn
import numpy as np

from tensorboardX import SummaryWriter

input_size = 1
output_size = 1
num_epochs = 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_epochs):
    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()

    # 将标量值存储到 tensorboard 中
    writer.add_scalar('Train', loss, epoch)
    if (epoch + 1) % 5 == 0:
        print('Epoch {}/{}, loss={:.4f}'.format(epoch + 1, num_epochs, loss.item()))

# 将网络的结构存储到 tensorboard 中
writer.add_graph(model, (inputs,))

predicated = model(torch.from_numpy(x_train)).detach().numpy()
writer.close()

二、查看 tensorboard

在 tensorboard 生成的 runs 文件夹同级目录下执行命令：

tensorboard --logdir=runs

然后访问相应的链接即可。