model = tf.keras.Sequential([
tf.keras.layers.Dense(5, input_shape=(3,)),
tf.keras.layers.Softmax()])
model.save('/tmp/model')
loaded_model = tf.keras.models.load_model('/tmp/model')
x = tf.random.uniform((10, 3))
assert np.allclose(model.predict(x), loaded_model.predict(x))
Note that the model weights may have different scoped names after being loaded. Scoped names include the model/layer names, such as "dense_1/kernel:0". It is recommended that you use the layer properties to access specific variables, e.g. model.get_layer("dense_1").kernel.
Arguments
filepath
One of the following:
String or pathlib.Path object, path to the saved model
h5py.File object from which to load the model
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.
A Keras model instance. If the original model was compiled, and saved with the optimizer, then the returned model will be compiled. Otherwise, the model will be left uncompiled. In the case that an uncompiled model is returned, a warning is displayed if the compile argument is set to True.
Raises
ImportError
if loading from an hdf5 file and h5py is not available.
