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the model's weights
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the model's optimizer's state (if any)
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Thus models can be reinstantiated in the exact same state, without any of the code used for model definition or training.
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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.
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SavedModel serialization format
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Keras SavedModel uses tf.saved_model.save to save the model and all trackable objects attached to the model (e.g. layers and variables). The model config, weights, and optimizer are saved in the SavedModel. Additionally, for every Keras layer attached to the model, the SavedModel stores:
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* the config and metadata -- e.g. name, dtype, trainable status * traced call and loss functions, which are stored as TensorFlow subgraphs.
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The traced functions allow the SavedModel format to save and load custom layers without the original class definition.
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You can choose to not save the traced functions by disabling the save_traces option. This will decrease the time it takes to save the model and the amount of disk space occupied by the output SavedModel. If you enable this option, then you must provide all custom class definitions when loading the model. See the custom_objects argument in tf.keras.models.load_model.
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Arguments
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model: Keras model instance to be saved.
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filepath: One of the following:
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String or pathlib.Path object, path where to save the model
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h5py.File object where to save the model
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overwrite: Whether we should overwrite any existing model at the target location, or instead ask the user with a manual prompt.
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include_optimizer: If True, save optimizer's state together.
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save_format: Either 'tf' or 'h5', indicating whether to save the model to Tensorflow SavedModel or HDF5. Defaults to 'tf' in TF 2.X, and 'h5' in TF 1.X.
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signatures: Signatures to save with the SavedModel. Applicable to the 'tf' format only. Please see the signatures argument in tf.saved_model.save for details.
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options: (only applies to SavedModel format) tf.saved_model.SaveOptions object that specifies options for saving to SavedModel.
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save_traces: (only applies to SavedModel format) When enabled, the SavedModel will store the function traces for each layer. This can be disabled, so that only the configs of each layer are stored. Defaults to True. Disabling this will decrease serialization time and reduce file size, but it requires that all custom layers/models implement a get_config() method.
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Raises
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ImportError: If save format is hdf5, and h5py is not available.
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load_model function
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tf.keras.models.load_model(
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filepath, custom_objects=None, compile=True, options=None
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)
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Loads a model saved via model.save().
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Usage:
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>>> model = tf.keras.Sequential([
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... tf.keras.layers.Dense(5, input_shape=(3,)),
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... tf.keras.layers.Softmax()])
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>>> model.save('/tmp/model')
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>>> loaded_model = tf.keras.models.load_model('/tmp/model')
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>>> x = tf.random.uniform((10, 3))
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>>> assert np.allclose(model.predict(x), loaded_model.predict(x))
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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.
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Arguments
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filepath: One of the following: - String or pathlib.Path object, path to the saved model - h5py.File object from which to load the model
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custom_objects: Optional dictionary mapping names (strings) to custom classes or functions to be considered during deserialization.
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compile: Boolean, whether to compile the model after loading.
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options: Optional tf.saved_model.LoadOptions object that specifies options for loading from SavedModel.
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Returns
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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.
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Raises
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ImportError: if loading from an hdf5 file and h5py is not available.
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IOError: In case of an invalid savefile.
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get_weights method
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Model.get_weights()
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Retrieves the weights of the model.
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Returns
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A flat list of Numpy arrays.
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set_weights method
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Model.set_weights(weights)
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Sets the weights of the layer, from NumPy arrays.
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The weights of a layer represent the state of the layer. This function sets the weight values from numpy arrays. The weight values should be passed in the order they are created by the layer. Note that the layer's weights must be instantiated before calling this function, by calling the layer.
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For example, a Dense layer returns a list of two values: the kernel matrix and the bias vector. These can be used to set the weights of another Dense layer:
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>>> layer_a = tf.keras.layers.Dense(1,
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... kernel_initializer=tf.constant_initializer(1.))
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>>> a_out = layer_a(tf.convert_to_tensor([[1., 2., 3.]]))
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>>> layer_a.get_weights()
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[array([[1.],
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[1.],
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[1.]], dtype=float32), array([0.], dtype=float32)]
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>>> layer_b = tf.keras.layers.Dense(1,
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... kernel_initializer=tf.constant_initializer(2.))
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>>> b_out = layer_b(tf.convert_to_tensor([[10., 20., 30.]]))
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>>> layer_b.get_weights()
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[array([[2.],
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[2.],
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[2.]], dtype=float32), array([0.], dtype=float32)]
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>>> layer_b.set_weights(layer_a.get_weights())
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>>> layer_b.get_weights()
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[array([[1.],
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[1.],
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[1.]], dtype=float32), array([0.], dtype=float32)]
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