一捕仔、代碼 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()
# 將標(biāo)量值存儲到 tensorboard 中
writer.add_scalar('Train', loss, epoch)
if (epoch + 1) % 5 == 0:
print('Epoch {}/{}, loss={:.4f}'.format(epoch + 1, num_epochs, loss.item()))
# 將網(wǎng)絡(luò)的結(jié)構(gòu)存儲到 tensorboard 中
writer.add_graph(model, (inputs,))
predicated = model(torch.from_numpy(x_train)).detach().numpy()
writer.close()
二匕积、查看 tensorboard
在 tensorboard 生成的 runs 文件夾同級目錄下執(zhí)行命令：
tensorboard --logdir=runs
然后訪問相應(yīng)的鏈接即可。
