Model Checkpoint and Callback in Keras

ModelCheckpoint callback is used in conjunction with training using model. fit() to save a model or weights (in a checkpoint file) at some interval, so the model or weights can be loaded later to continue the training from the state saved.
from tensorflow.keras.callbacks import ModelCheckpoint
Checkpoint to save best model per epoch

model_filepath = "/content/drive/My Drive/model-{epoch:02d}-{val_accuracy:.4f}.hdf5"
checkpoint = ModelCheckpoint(
   filepath=model_filepath,
   monitor='val_accuracy',
   mode='max',
   save_best_only=True,
   verbose=1
)
history=model.fit(
 train_ds,
 validation_data=val_ds,
 epochs=50,callbacks=[checkpoint]
)

• #science_technology
• #model_checkpoint_keras
• #model_checkpoint_save_best_only
• #callback_in_keras
• #monitor_the_performance_of_the_deep_learning_model
• #saving_deep_learning_model