[Part 14/20] Visualizing Data and Model Metrics in PyTorch with TensorBoard
Deep Learning with PyTorch — Part 14/20
Table of Contents
1. Setting Up TensorBoard with PyTorch
2. Visualizing Training Progress
2.1. Plotting Loss and Accuracy
2.2. Visualizing Model Graphs
3. Advanced Features in TensorBoard for PyTorch
3.1. Hyperparameter Tuning with TensorBoard
3.2. Custom Visualizations for In-depth Analysis
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1. Setting Up TensorBoard with PyTorch
Integrating TensorBoard with PyTorch is a straightforward process that enhances your ability to monitor and visualize various aspects of your machine learning models. This section will guide you through the initial setup and configuration to get TensorBoard running with your PyTorch projects.
Step 1: Install TensorBoard
First, ensure that you have TensorBoard installed in your environment. You can install it via pip:
