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Exports a tf.keras.Model
as a Tensorflow SavedModel. (deprecated)
Aliases:
tf.compat.v1.keras.experimental.export_saved_model
tf.compat.v2.keras.experimental.export_saved_model
tf.contrib.saved_model.save_keras_model
tf.keras.experimental.export_saved_model(
model,
saved_model_path,
custom_objects=None,
as_text=False,
input_signature=None,
serving_only=False
)
Note that at this time, subclassed models can only be saved using
serving_only=True
.
The exported SavedModel
is a standalone serialization of Tensorflow objects,
and is supported by TF language APIs and the Tensorflow Serving system.
To load the model, use the function
tf.keras.experimental.load_from_saved_model
.
The SavedModel
contains:
- a checkpoint containing the model weights.
- a
SavedModel
proto containing the Tensorflow backend graph. Separate graphs are saved for prediction (serving), train, and evaluation. If the model has not been compiled, then only the graph computing predictions will be exported. - the model's json config. If the model is subclassed, this will only be
included if the model's
get_config()
method is overwritten.
Example:
import tensorflow as tf
# Create a tf.keras model.
model = tf.keras.Sequential()
model.add(tf.keras.layers.Dense(1, input_shape=[10]))
model.summary()
# Save the tf.keras model in the SavedModel format.
path = '/tmp/simple_keras_model'
tf.keras.experimental.export_saved_model(model, path)
# Load the saved keras model back.
new_model = tf.keras.experimental.load_from_saved_model(path)
new_model.summary()
Args:
model
: Atf.keras.Model
to be saved. If the model is subclassed, the flagserving_only
must be set to True.saved_model_path
: a string specifying the path to the SavedModel directory.custom_objects
: Optional dictionary mapping string names to custom classes or functions (e.g. custom loss functions).as_text
: bool,False
by default. Whether to write theSavedModel
proto in text format. Currently unavailable in serving-only mode.input_signature
: A possibly nested sequence oftf.TensorSpec
objects, used to specify the expected model inputs. Seetf.function
for more details.serving_only
: bool,False
by default. When this is true, only the prediction graph is saved.
Raises:
NotImplementedError
: If the model is a subclassed model, and serving_only is False.ValueError
: If the input signature cannot be inferred from the model.AssertionError
: If the SavedModel directory already exists and isn't empty.