Basic Tensorboard with Code
Tensorboard
- Supports PyTorch
- Shows scalars (accuracy, loss etc)
- Shows the computational graph
- Shows the histogram of weights
- How to Use
- Create directory to save log
import os log_dir = 'log_file' os.makedirs(log_dir)
- Import tensorboard log writer
from torch.utils.tensorboard import SummaryWriter import numpy as np
- create different experiments
experiment_dir = log_dir + '/exp1' writer = SummaryWriter(experiment_dir)
- Log content
for iter_nth in range(100): writer.add_scalar('loss/train' value, iter_nth) writer.flush() # save log on the drive
- Activate Tensorboard on Jupyter Notebook
%load_ext tensorboard %tensorboard --logdir {log_dir}
- Use Tensorboard
tensorboard --logdir PATH --host ADDR --port PORT
- Create directory to save log