To resume training from a checkpoint in Keras, you can use the
ModelCheckpoint callback to save the best model during training and then load that saved model to continue training from where it left off. Here’s how to resume training from a saved checkpoint:
Training the Model and Saving Checkpoints:
# Import necessary libraries import keras from keras.models import Sequential from keras.layers import Dense from keras.callbacks import ModelCheckpoint # Create a Keras model (for demonstration) model = Sequential() model.add(Dense(units=64, activation='relu', input_dim=10)) model.add(Dense(units=32, activation='relu')) model.add(Dense(units=1, activation='sigmoid')) # Compile the model model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy']) # Define the ModelCheckpoint callback to save the best model checkpoint = ModelCheckpoint( "best_model.h5", # Filepath to save the best model monitor='val_accuracy', # Metric to monitor (e.g., validation accuracy) save_best_only=True, # Save only the best model mode='max', # 'max' if monitoring validation accuracy, 'min' for loss verbose=1 # Display messages when saving ) # Train the model with the ModelCheckpoint callback model.fit(X, y, epochs=10, validation_split=0.2, callbacks=[checkpoint])
Resuming Training from the Checkpoint:
# Import necessary libraries import keras from keras.models import load_model # Load the best model checkpoint saved during training model = load_model("best_model.h5") # Resume training from where it left off model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy']) # Continue training on new data or for additional epochs model.fit(new_X, new_y, epochs=5) # Replace new_X and new_y with your new data
In this example:
- First, you train the model with the
ModelCheckpointcallback, which saves the best model based on the validation accuracy.
- After training, you load the best model using
load_model. The model is loaded with the weights and architecture that performed best during training.
- You compile the model again with the same optimizer, loss function, and metrics.
- You can then continue training the model from the point it left off. This is useful for further fine-tuning, additional training epochs, or working with new data.
By saving and loading the best model using checkpoints, you can ensure that you continue training from a point of good performance, even if you need to stop and resume the training process.