Pytorch 使用 TensorBoard 可视化

TensorFlow中强大的可视化工具

TensorBoard ：https://tensorflow.google.cn/tensorboard

TensorBoard provides the visualization and tooling needed for machine learning experimentation:

• Tracking and visualizing metrics such as loss and accuracy
• Visualizing the model graph (ops and layers)
• Viewing histograms of weights, biases, or other tensors as they change over time
• Projecting embeddings to a lower dimensional space
• Displaying images, text, and audio data
• Profiling TensorFlow programs
• And much more

pytorch官方文档：

https://pytorch.org/docs/stable/tensorboard.html?highlight=tensorboard

测试代码：

import numpy as np
from torch.utils.tensorboard import SummaryWriter, writer

writer=SummaryWriter(comment='test tensorboard')

for x in range(100):
    writer.add_scalar('y=2x',x*2,x)
    writer.add_scalar('y=pow(2,x)',2**x,x)
    
    writer.add_scalars('data/scalar_group',{
        "xsinx":x*np.sin(x),
        "xcosx":x*np.cos(x),
        "arctanx":np.arctan(x),
    },x)

writer.close()

运行代码后会生成runs文件夹；

 运行命令：

tensorboard --logdir=./runs


参数：--logdir   runs文件夹所在的路径

 结果：