tf.keras.models.load_model

Loads a model saved via model.save().

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

Used in the notebooks

Used in the guide	Used in the tutorials
Introduction to modules, layers, and models Migrate `tf.feature_column`s to Keras preprocessing layers Migrate the SavedModel workflow Migrating your TFLite code to TF2 Multi-GPU and distributed training	Save and load a model using a distribution strategy Save and load models Distributed training with Keras Multi-worker training with Keras Transfer learning with TensorFlow Hub

Args
filepath	str or pathlib.Path object, path to the saved model file.
custom_objects	Optional dictionary mapping names (strings) to custom classes or functions to be considered during deserialization.
compile	Boolean, whether to compile the model after loading.
safe_mode	Boolean, whether to disallow unsafe lambda deserialization. When safe_mode=False, loading an object has the potential to trigger arbitrary code execution. This argument is only applicable to the Keras v3 model format. Defaults to True.

Returns
A Keras model instance. If the original model was compiled, and the argument compile=True is set, then the returned model will be compiled. Otherwise, the model will be left uncompiled.

Example:

model = keras.Sequential([
    keras.layers.Dense(5, input_shape=(3,)),
    keras.layers.Softmax()])
model.save("model.keras")
loaded_model = keras.saving.load_model("model.keras")
x = np.random.random((10, 3))
assert np.allclose(model.predict(x), loaded_model.predict(x))

Note that the model variables may have different name values (var.name property, e.g. "dense_1/kernel:0") after being reloaded. It is recommended that you use layer attributes to access specific variables, e.g. model.get_layer("dense_1").kernel.