def test_json_serialization(self):
inputs = keras.Input(shape=(4,), dtype='uint8')
outputs = math_ops.cast(inputs, 'float32') / 4.
model = saving.model_from_json(keras.Model(inputs, outputs).to_json())
self.assertAllEqual(
self.evaluate(model(np.array([0, 64, 128, 192], np.uint8))),
[0., 16., 32., 48.])
model.summary()
def load_from_saved_model(saved_model_path, custom_objects=None):
"""Loads a keras Model from a SavedModel created by `export_saved_model()`.
This function reinstantiates model state by:
1) loading model topology from json (this will eventually come
from metagraph).
2) loading model weights from checkpoint.
Example:
```python
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:
saved_model_path: a string specifying the path to an existing SavedModel.
custom_objects: Optional dictionary mapping names
(strings) to custom classes or functions to be
considered during deserialization.
Returns:
a keras.Model instance.
"""
# restore model topology from json string
model_json_filepath = os.path.join(
compat.as_bytes(saved_model_path),
compat.as_bytes(constants.ASSETS_DIRECTORY),
compat.as_bytes(constants.SAVED_MODEL_FILENAME_JSON))
model_json = file_io.read_file_to_string(model_json_filepath)
model = model_from_json(model_json, custom_objects=custom_objects)
# restore model weights
checkpoint_prefix = os.path.join(
compat.as_text(saved_model_path),
compat.as_text(constants.VARIABLES_DIRECTORY),
compat.as_text(constants.VARIABLES_FILENAME))
model.load_weights(checkpoint_prefix)
return model
def load_from_saved_model(saved_model_path, custom_objects=None):
"""Loads a keras Model from a SavedModel created by `export_saved_model()`.
This function reinstantiates model state by:
1) loading model topology from json (this will eventually come
from metagraph).
2) loading model weights from checkpoint.
Example:
```python
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:
saved_model_path: a string specifying the path to an existing SavedModel.
custom_objects: Optional dictionary mapping names
(strings) to custom classes or functions to be
considered during deserialization.
Returns:
a keras.Model instance.
"""
# restore model topology from json string
model_json_filepath = os.path.join(
compat.as_bytes(saved_model_path),
compat.as_bytes(constants.ASSETS_DIRECTORY),
compat.as_bytes(constants.SAVED_MODEL_FILENAME_JSON))
model_json = file_io.read_file_to_string(model_json_filepath)
model = model_from_json(model_json, custom_objects=custom_objects)
# restore model weights
checkpoint_prefix = os.path.join(
compat.as_text(saved_model_path),
compat.as_text(constants.VARIABLES_DIRECTORY),
compat.as_text(constants.VARIABLES_FILENAME))
model.load_weights(checkpoint_prefix)
return model
def load_from_saved_model(saved_model_path):
"""Loads a keras.Model from a SavedModel created by keras export().
This function reinstantiates model state by:
1) loading model topology from json (this will eventually come
from metagraph).
2) loading model weights from checkpoint.
Example:
```python
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.
saved_to_path = tf.keras.experimental.export(
model, '/tmp/my_simple_tf_keras_saved_model')
# Load the saved keras model back.
model_prime = tf.keras.experimental.load_from_saved_model(saved_to_path)
model_prime.summary()
```
Args:
saved_model_path: a string specifying the path to an existing SavedModel.
Returns:
a keras.Model instance.
"""
# restore model topology from json string
model_json_filepath = os.path.join(
compat.as_bytes(saved_model_path),
compat.as_bytes(constants.ASSETS_DIRECTORY),
compat.as_bytes(constants.SAVED_MODEL_FILENAME_JSON))
model_json = file_io.read_file_to_string(model_json_filepath)
model = model_from_json(model_json)
# restore model weights
checkpoint_prefix = os.path.join(
compat.as_text(saved_model_path),
compat.as_text(constants.VARIABLES_DIRECTORY),
compat.as_text(constants.VARIABLES_FILENAME))
model.load_weights(checkpoint_prefix)
return model
def load_from_saved_model(saved_model_path):
"""Loads a keras.Model from a SavedModel created by keras export().
This function reinstantiates model state by:
1) loading model topology from json (this will eventually come
from metagraph).
2) loading model weights from checkpoint.
Example:
```python
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.
saved_to_path = tf.keras.experimental.export(
model, '/tmp/my_simple_tf_keras_saved_model')
# Load the saved keras model back.
model_prime = tf.keras.experimental.load_from_saved_model(saved_to_path)
model_prime.summary()
```
Args:
saved_model_path: a string specifying the path to an existing SavedModel.
Returns:
a keras.Model instance.
"""
# restore model topology from json string
model_json_filepath = os.path.join(
compat.as_bytes(saved_model_path),
compat.as_bytes(constants.ASSETS_DIRECTORY),
compat.as_bytes(constants.SAVED_MODEL_FILENAME_JSON))
model_json = file_io.read_file_to_string(model_json_filepath)
model = model_from_json(model_json)
# restore model weights
checkpoint_prefix = os.path.join(
compat.as_text(saved_model_path),
compat.as_text(constants.VARIABLES_DIRECTORY),
compat.as_text(constants.VARIABLES_FILENAME))
model.load_weights(checkpoint_prefix)
return model
def load_model_local(self, model_sha1):
"""
Load model from local filesystem
"""
model = self.get_model(model_sha1)
if model and model.get('model') and model.get(
'class_indices') and model.get('status') == self.READY:
logging.error(
'Successfully loading model {model_sha1}: model'.format(
model_sha1=model_sha1))
return model
model_path = self.get_model_path(model_sha1)
model_path_weights = os.path.join(model_path, 'model')
model_path_json = os.path.join(model_path, 'model.json')
model_class_indices = os.path.join(model_path, 'class_indices.json')
if not os.path.exists(model_path) or not os.path.exists(
model_path_json) or not os.path.exists(model_class_indices):
logging.debug(
'Not loading model {model_sha1}: not all paths exists'.format(
model_sha1=model_sha1))
return model
model = self.models[model_sha1] = {}
with open(model_class_indices, "r") as json_file:
model['class_indices'] = json.load(json_file)
with open(model_path_json, "r") as json_file:
model['model'] = model_from_json(json_file.read())
model['model'].load_weights(model_path_weights)
# Compile model for use optimizers
model['model'].compile(optimizer=self.__get_optimizer(),
loss='categorical_crossentropy',
metrics=['categorical_accuracy', 'accuracy'])
model['status'] = self.READY
return model
