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Introduction to PyTorch TensorBoard: A Tutorial
TensorBoard is a visualization tool provided by TensorFlow, but it can also be used with PyTorch to visualize training and evaluation metrics. In this tutorial, we'll explore how to integrate TensorBoard with PyTorch using the torch.utils.tensorboard module.
Step 1: Install the necessary libraries
Make sure you have PyTorch and TensorBoard installed:
Step 2: Import the required libraries
Step 3: Define your neural network
In this example, we'll use a simple convolutional neural network (CNN) for image classification.
Step 4: Set up the data loaders
Step 5: Initialize the model, loss function, and optimizer
Step 6: Set up TensorBoard
Step 7: Train the model with TensorBoard logging
Step 8: Visualize in TensorBoard
Run the following command in your terminal to start TensorBoard:
Visit http://localhost:6006 in your web browser to view the training progress and other metrics.
This tutorial covered the basics of integrating PyTorch with TensorBoard for visualizing training metrics. You can customize the logging and explore additional features to gain deeper insights into your model's performance.
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