How to save iterative models and best model in tensorflow?

@raphael_tillman 

To save iterative models and the best model in TensorFlow, you can use the ModelCheckpoint callback from TensorFlow's Keras library. Here's how you can do it:

1. Define a ModelCheckpoint callback with a specified file path where you want to save the model:

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from tensorflow.keras.callbacks import ModelCheckpoint

checkpoint_filepath = 'best_model.h5'
model_checkpoint = ModelCheckpoint(filepath=checkpoint_filepath, save_best_only=True, monitor='val_loss', mode='min', verbose=1)

1. When fitting your model, pass the ModelCheckpoint callback to the callbacks parameter:

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model.fit(x_train, y_train, validation_data=(x_val, y_val), epochs=10, callbacks=[model_checkpoint])

1. After training, you can load the best model using the following code:

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from tensorflow.keras.models import load_model

best_model = load_model(checkpoint_filepath)

Now, the best model will be saved to the specified file path during training, and you can load it back using load_model function when needed.