Saving And Loading TensorFlow Models – ModelCheckpoint Callback | Deep Learning With TensorFlow

One of the most important aspects of training deep learning models is the ability to save and load the trained models for later use. This allows us to re-use the trained models for inference or continue training from where we left off.

In TensorFlow, we can use the ModelCheckpoint callback to save the model checkpoints during training. This callback allows us to save the model weights and optimizer state after each epoch or based on a specific condition.

Here is an example of how to use the ModelCheckpoint callback in TensorFlow:

“`python
from tensorflow.keras.callbacks import ModelCheckpoint

# Create a ModelCheckpoint callback
checkpoint = ModelCheckpoint(filepath=’model_checkpoint.h5′,
save_weights_only=True,
save_best_only=True,
monitor=’val_loss’,
verbose=1)

# Compile and train the model
model.compile(optimizer=’adam’, loss=’sparse_categorical_crossentropy’, metrics=[‘accuracy’])
model.fit(X_train, y_train, epochs=10, validation_data=(X_test, y_test), callbacks=[checkpoint])
“`

With the above code, the model weights will be saved to a file named ‘model_checkpoint.h5’ after each epoch if the validation loss decreases. This allows us to easily load the trained model later for evaluation or further training.

To load the saved model weights, we can use the following code:

“`python
from tensorflow.keras.models import load_model

# Load the saved model weights
model = load_model(‘model_checkpoint.h5′)

# Compile and evaluate the model
model.compile(optimizer=’adam’, loss=’sparse_categorical_crossentropy’, metrics=[‘accuracy’])
loss, accuracy = model.evaluate(X_test, y_test)
print(f’Test loss: {loss}, Test accuracy: {accuracy}’)
“`

By saving and loading TensorFlow models using the ModelCheckpoint callback, we can easily re-use the trained models for various tasks and experiments in deep learning. This helps in speeding up the development process and allows us to experiment with different model architectures and hyperparameters.