HenryAI/KerasBERTv1-Data · Datasets at Hugging Face

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Functional model example:

inputs = keras.Input((32,))
outputs = keras.layers.Dense(1)(inputs)
model = keras.Model(inputs, outputs)
config = model.get_config()
new_model = keras.Model.from_config(config)
to_json() and tf.keras.models.model_from_json()
This is similar to get_config / from_config, except it turns the model into a JSON string, which can then be loaded without the original model class. It is also specific to models, it isn't meant for layers.

Example:

model = keras.Sequential([keras.Input((32,)), keras.layers.Dense(1)])
json_config = model.to_json()
new_model = keras.models.model_from_json(json_config)
Custom objects
Models and layers

The architecture of subclassed models and layers are defined in the methods __init__ and call. They are considered Python bytecode, which cannot be serialized into a JSON-compatible config -- you could try serializing the bytecode (e.g. via pickle), but it's completely unsafe and means your model cannot be loaded on a different system.

In order to save/load a model with custom-defined layers, or a subclassed model, you should overwrite the get_config and optionally from_config methods. Additionally, you should use register the custom object so that Keras is aware of it.

Custom functions

Custom-defined functions (e.g. activation loss or initialization) do not need a get_config method. The function name is sufficient for loading as long as it is registered as a custom object.

Loading the TensorFlow graph only

It's possible to load the TensorFlow graph generated by the Keras. If you do so, you won't need to provide any custom_objects. You can do so like this:

model.save("my_model")
tensorflow_graph = tf.saved_model.load("my_model")
x = np.random.uniform(size=(4, 32)).astype(np.float32)
predicted = tensorflow_graph(x).numpy()
INFO:tensorflow:Assets written to: my_model/assets
Note that this method has several drawbacks: * For traceability reasons, you should always have access to the custom objects that were used. You wouldn't want to put in production a model that you cannot re-create. * The object returned by tf.saved_model.load isn't a Keras model. So it's not as easy to use. For example, you won't have access to .predict() or .fit()

Even if its use is discouraged, it can help you if you're in a tight spot, for example, if you lost the code of your custom objects or have issues loading the model with tf.keras.models.load_model().

You can find out more in the page about tf.saved_model.load

Defining the config methods
Specifications:

get_config should return a JSON-serializable dictionary in order to be compatible with the Keras architecture- and model-saving APIs.
from_config(config) (classmethod) should return a new layer or model object that is created from the config. The default implementation returns cls(**config).
Example:

class CustomLayer(keras.layers.Layer):
def __init__(self, a):
self.var = tf.Variable(a, name="var_a")

def call(self, inputs, training=False):
if training:
return inputs * self.var
else:
return inputs

def get_config(self):
return {"a": self.var.numpy()}

# There's actually no need to define `from_config` here, since returning
# `cls(**config)` is the default behavior.
@classmethod
def from_config(cls, config):
return cls(**config)


layer = CustomLayer(5)
layer.var.assign(2)

serialized_layer = keras.layers.serialize(layer)
new_layer = keras.layers.deserialize(
serialized_layer, custom_objects={"CustomLayer": CustomLayer}
)
Registering the custom object
Keras keeps a note of which class generated the config. From the example above, tf.keras.layers.serialize generates a serialized form of the custom layer:

{'class_name': 'CustomLayer', 'config': {'a': 2}}
Keras keeps a master list of all built-in layer, model, optimizer, and metric classes, which is used to find the correct class to call from_config. If the class can't be found, then an error is raised (Value Error: Unknown layer). There are a few ways to register custom classes to this list:

Setting custom_objects argument in the loading function. (see the example in section above "Defining the config methods")
tf.keras.utils.custom_object_scope or tf.keras.utils.CustomObjectScope
tf.keras.utils.register_keras_serializable
Custom layer and function example
class CustomLayer(keras.layers.Layer):
def __init__(self, units=32, **kwargs):
super(CustomLayer, self).__init__(**kwargs)
self.units = units

def build(self, input_shape):
self.w = self.add_weight(
shape=(input_shape[-1], self.units),
initializer="random_normal",
trainable=True,
)
self.b = self.add_weight(
shape=(self.units,), initializer="random_normal", trainable=True
)

Functional model example:

inputs = keras.Input((32,))

outputs = keras.layers.Dense(1)(inputs)

model = keras.Model(inputs, outputs)

config = model.get_config()

new_model = keras.Model.from_config(config)

to_json() and tf.keras.models.model_from_json()

This is similar to get_config / from_config, except it turns the model into a JSON string, which can then be loaded without the original model class. It is also specific to models, it isn't meant for layers.

Example:

model = keras.Sequential([keras.Input((32,)), keras.layers.Dense(1)])

json_config = model.to_json()

new_model = keras.models.model_from_json(json_config)

Custom objects

Models and layers

The architecture of subclassed models and layers are defined in the methods __init__ and call. They are considered Python bytecode, which cannot be serialized into a JSON-compatible config -- you could try serializing the bytecode (e.g. via pickle), but it's completely unsafe and means your model cannot be loaded on a different system.

In order to save/load a model with custom-defined layers, or a subclassed model, you should overwrite the get_config and optionally from_config methods. Additionally, you should use register the custom object so that Keras is aware of it.

Custom functions

Custom-defined functions (e.g. activation loss or initialization) do not need a get_config method. The function name is sufficient for loading as long as it is registered as a custom object.

Loading the TensorFlow graph only

It's possible to load the TensorFlow graph generated by the Keras. If you do so, you won't need to provide any custom_objects. You can do so like this:

model.save("my_model")

tensorflow_graph = tf.saved_model.load("my_model")

x = np.random.uniform(size=(4, 32)).astype(np.float32)

predicted = tensorflow_graph(x).numpy()

INFO:tensorflow:Assets written to: my_model/assets

Note that this method has several drawbacks: * For traceability reasons, you should always have access to the custom objects that were used. You wouldn't want to put in production a model that you cannot re-create. * The object returned by tf.saved_model.load isn't a Keras model. So it's not as easy to use. For example, you won't have access to .predict() or .fit()

Even if its use is discouraged, it can help you if you're in a tight spot, for example, if you lost the code of your custom objects or have issues loading the model with tf.keras.models.load_model().

You can find out more in the page about tf.saved_model.load

Defining the config methods

Specifications:

get_config should return a JSON-serializable dictionary in order to be compatible with the Keras architecture- and model-saving APIs.

from_config(config) (classmethod) should return a new layer or model object that is created from the config. The default implementation returns cls(**config).

Example:

class CustomLayer(keras.layers.Layer):

def __init__(self, a):

self.var = tf.Variable(a, name="var_a")

def call(self, inputs, training=False):

if training:

return inputs * self.var

else:

return inputs

def get_config(self):

return {"a": self.var.numpy()}

# There's actually no need to define `from_config` here, since returning

# `cls(**config)` is the default behavior.

@classmethod

def from_config(cls, config):

return cls(**config)

layer = CustomLayer(5)

layer.var.assign(2)

serialized_layer = keras.layers.serialize(layer)

new_layer = keras.layers.deserialize(

serialized_layer, custom_objects={"CustomLayer": CustomLayer}

)

Registering the custom object

Keras keeps a note of which class generated the config. From the example above, tf.keras.layers.serialize generates a serialized form of the custom layer:

{'class_name': 'CustomLayer', 'config': {'a': 2}}

Keras keeps a master list of all built-in layer, model, optimizer, and metric classes, which is used to find the correct class to call from_config. If the class can't be found, then an error is raised (Value Error: Unknown layer). There are a few ways to register custom classes to this list:

Setting custom_objects argument in the loading function. (see the example in section above "Defining the config methods")

tf.keras.utils.custom_object_scope or tf.keras.utils.CustomObjectScope

tf.keras.utils.register_keras_serializable

Custom layer and function example

class CustomLayer(keras.layers.Layer):

def __init__(self, units=32, **kwargs):

super(CustomLayer, self).__init__(**kwargs)

self.units = units

def build(self, input_shape):

self.w = self.add_weight(

shape=(input_shape[-1], self.units),

initializer="random_normal",

trainable=True,

)

self.b = self.add_weight(

shape=(self.units,), initializer="random_normal", trainable=True

)