tf.keras.models.load_model

tf.keras.models.load_model function is used to load saved models from storage for further use. It allows users to easily retrieve trained models from disk or other storage mediums.

The syntax of the tf.keras.models.load_model function is as follows:

tf.keras.models.load_model(filepath, custom_objects=None, compile=True)

where,

• file path: This argument specifies the path to the saved model file or an h5py.File object from which to load the model.
• custom_objects: An optional dictionary that maps names (strings) to custom classes or functions to be considered during deserialization. This parameter proves invaluable when dealing with models containing custom layers or loss functions.
• compile: A boolean flag indicating whether to compile the loaded model after loading. When set to True, the model will be compiled, leveraging the optimizer and loss function specified during training. Conversely, setting it to False allows for skipping compilation, useful when solely interested in model inference.

TensorFlow is an open-source machine-learning library developed by Google. In this article, we are going to explore the how can we load a model in TensorFlow.