In this tutorial, we implement the 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 with these summary functions can be found here.
$ pip install -r requirements.txt$ python main.pyTo run the TensorBoard, open a new terminal and run the command below. Then, open http://localhost:6006/ in your web browser.
$ tensorboard --logdir='./logs' --port=6006