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Deep Learning with JAX and Flax

Transfer Learning using ResNet Model

Explore how to fine-tune and train a ResNet model using the Flax framework with JAX. Learn to monitor training with TensorBoard, save and load model checkpoints, evaluate model performance, and visualize training metrics using Matplotlib. This lesson equips you with practical skills for implementing transfer learning in deep learning projects.

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We train the ResNet model by applying the train_one_epoch function for the desired number of epochs. This is a few epochs since we are fine-tuning the network.

Set up TensorBoard in Flax

To monitor model training via TensorBoard, we can write the training and validation metrics to TensorBoard.

Python 3.8
from torch.utils.tensorboard import SummaryWriter
logdir = "flax_logs"
writer = SummaryWriter(logdir)

In the code above:

  • Line 1: We import the SummaryWriter module from torch.utils.tensorboard to log in to TensorBoard.

  • Line 3: We define the logdir ...