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TensorBoard in PyTorch

In this tutorial, we implement a MNIST classifier using a simple neural network and visualize the training process using TensorBoard. In training phase, we plot the loss and accuracy functions through scalar_summary and visualize the training images through image_summary. In addition, we visualize the weight and gradient values of the parameters of the neural network using histogram_summary. PyTorch code for handling these summary functions can be found here.

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1. Install the dependencies

$ pip install -r requirements.txt

2. Train the model

$ python main.py

3. Open the TensorBoard

To run the TensorBoard, open a new terminal and run the command below. Then, open http://localhost:6006/ on your web browser.

$ tensorboard --logdir='./logs' --port=6006