HenryAI/KerasBERTv1-Data · Datasets at Hugging Face

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def from_config(cls, config):
return cls(**config)


model = CustomModel([16, 16, 10])
# Build the model by calling it
input_arr = tf.random.uniform((1, 5))
outputs = model(input_arr)
model.save("my_model")

# Option 1: Load with the custom_object argument.
loaded_1 = keras.models.load_model(
"my_model", custom_objects={"CustomModel": CustomModel}
)

# Option 2: Load without the CustomModel class.

# Delete the custom-defined model class to ensure that the loader does not have
# access to it.
del CustomModel

loaded_2 = keras.models.load_model("my_model")
np.testing.assert_allclose(loaded_1(input_arr), outputs)
np.testing.assert_allclose(loaded_2(input_arr), outputs)

print("Original model:", model)
print("Model Loaded with custom objects:", loaded_1)
print("Model loaded without the custom object class:", loaded_2)
INFO:tensorflow:Assets written to: my_model/assets
WARNING:tensorflow:No training configuration found in save file, so the model was not compiled. Compile it manually.
WARNING:tensorflow:No training configuration found in save file, so the model was not compiled. Compile it manually.
Original model: <__main__.CustomModel object at 0x151ad0990>
Model Loaded with custom objects: <__main__.CustomModel object at 0x151b03850>
Model loaded without the custom object class: <tensorflow.python.keras.saving.saved_model.load.CustomModel object at 0x151bb0310>
The first loaded model is loaded using the config and CustomModel class. The second model is loaded by dynamically creating the model class that acts like the original model.

Configuring the SavedModel
New in TensoFlow 2.4 The argument save_traces has been added to model.save, which allows you to toggle SavedModel function tracing. Functions are saved to allow the Keras to re-load custom objects without the original class definitons, so when save_traces=False, all custom objects must have defined get_config/from_config methods. When loading, the custom objects must be passed to the custom_objects argument. save_traces=False reduces the disk space used by the SavedModel and saving time.

Keras H5 format
Keras also supports saving a single HDF5 file containing the model's architecture, weights values, and compile() information. It is a light-weight alternative to SavedModel.

Example:

model = get_model()

# Train the model.
test_input = np.random.random((128, 32))
test_target = np.random.random((128, 1))
model.fit(test_input, test_target)

# Calling `save('my_model.h5')` creates a h5 file `my_model.h5`.
model.save("my_h5_model.h5")

# It can be used to reconstruct the model identically.
reconstructed_model = keras.models.load_model("my_h5_model.h5")

# Let's check:
np.testing.assert_allclose(
model.predict(test_input), reconstructed_model.predict(test_input)
)

# The reconstructed model is already compiled and has retained the optimizer
# state, so training can resume:
reconstructed_model.fit(test_input, test_target)
4/4 [==============================] - 0s 967us/step - loss: 0.8106
4/4 [==============================] - 0s 1ms/step - loss: 0.7184

<tensorflow.python.keras.callbacks.History at 0x151d5ac90>
Limitations
Compared to the SavedModel format, there are two things that don't get included in the H5 file:

External losses & metrics added via model.add_loss() & model.add_metric() are not saved (unlike SavedModel). If you have such losses & metrics on your model and you want to resume training, you need to add these losses back yourself after loading the model. Note that this does not apply to losses/metrics created inside layers via self.add_loss() & self.add_metric(). As long as the layer gets loaded, these losses & metrics are kept, since they are part of the call method of the layer.
The computation graph of custom objects such as custom layers is not included in the saved file. At loading time, Keras will need access to the Python classes/functions of these objects in order to reconstruct the model. See Custom objects.
Saving the architecture
The model's configuration (or architecture) specifies what layers the model contains, and how these layers are connected*. If you have the configuration of a model, then the model can be created with a freshly initialized state for the weights and no compilation information.

*Note this only applies to models defined using the functional or Sequential apis not subclassed models.

Configuration of a Sequential model or Functional API model
These types of models are explicit graphs of layers: their configuration is always available in a structured form.

APIs
get_config() and from_config()
tf.keras.models.model_to_json() and tf.keras.models.model_from_json()
get_config() and from_config()
Calling config = model.get_config() will return a Python dict containing the configuration of the model. The same model can then be reconstructed via Sequential.from_config(config) (for a Sequential model) or Model.from_config(config) (for a Functional API model).

The same workflow also works for any serializable layer.

Layer example:

layer = keras.layers.Dense(3, activation="relu")
layer_config = layer.get_config()
new_layer = keras.layers.Dense.from_config(layer_config)
Sequential model example:

model = keras.Sequential([keras.Input((32,)), keras.layers.Dense(1)])
config = model.get_config()
new_model = keras.Sequential.from_config(config)

def from_config(cls, config):

return cls(**config)

model = CustomModel([16, 16, 10])

# Build the model by calling it

input_arr = tf.random.uniform((1, 5))

outputs = model(input_arr)

model.save("my_model")

# Option 1: Load with the custom_object argument.

loaded_1 = keras.models.load_model(

"my_model", custom_objects={"CustomModel": CustomModel}

)

# Option 2: Load without the CustomModel class.

# Delete the custom-defined model class to ensure that the loader does not have

# access to it.

del CustomModel

loaded_2 = keras.models.load_model("my_model")

np.testing.assert_allclose(loaded_1(input_arr), outputs)

np.testing.assert_allclose(loaded_2(input_arr), outputs)

print("Original model:", model)

print("Model Loaded with custom objects:", loaded_1)

print("Model loaded without the custom object class:", loaded_2)

INFO:tensorflow:Assets written to: my_model/assets

WARNING:tensorflow:No training configuration found in save file, so the model was *not* compiled. Compile it manually.

Original model: <__main__.CustomModel object at 0x151ad0990>

Model Loaded with custom objects: <__main__.CustomModel object at 0x151b03850>

Model loaded without the custom object class: <tensorflow.python.keras.saving.saved_model.load.CustomModel object at 0x151bb0310>

The first loaded model is loaded using the config and CustomModel class. The second model is loaded by dynamically creating the model class that acts like the original model.

Configuring the SavedModel

New in TensoFlow 2.4 The argument save_traces has been added to model.save, which allows you to toggle SavedModel function tracing. Functions are saved to allow the Keras to re-load custom objects without the original class definitons, so when save_traces=False, all custom objects must have defined get_config/from_config methods. When loading, the custom objects must be passed to the custom_objects argument. save_traces=False reduces the disk space used by the SavedModel and saving time.

Keras H5 format

Keras also supports saving a single HDF5 file containing the model's architecture, weights values, and compile() information. It is a light-weight alternative to SavedModel.

Example:

model = get_model()

# Train the model.

test_input = np.random.random((128, 32))

test_target = np.random.random((128, 1))

model.fit(test_input, test_target)

# Calling `save('my_model.h5')` creates a h5 file `my_model.h5`.

model.save("my_h5_model.h5")

# It can be used to reconstruct the model identically.

reconstructed_model = keras.models.load_model("my_h5_model.h5")

# Let's check:

np.testing.assert_allclose(

model.predict(test_input), reconstructed_model.predict(test_input)

)

# The reconstructed model is already compiled and has retained the optimizer

# state, so training can resume:

reconstructed_model.fit(test_input, test_target)

4/4 [==============================] - 0s 967us/step - loss: 0.8106

4/4 [==============================] - 0s 1ms/step - loss: 0.7184

<tensorflow.python.keras.callbacks.History at 0x151d5ac90>

Limitations

Compared to the SavedModel format, there are two things that don't get included in the H5 file:

External losses & metrics added via model.add_loss() & model.add_metric() are not saved (unlike SavedModel). If you have such losses & metrics on your model and you want to resume training, you need to add these losses back yourself after loading the model. Note that this does not apply to losses/metrics created inside layers via self.add_loss() & self.add_metric(). As long as the layer gets loaded, these losses & metrics are kept, since they are part of the call method of the layer.

The computation graph of custom objects such as custom layers is not included in the saved file. At loading time, Keras will need access to the Python classes/functions of these objects in order to reconstruct the model. See Custom objects.

Saving the architecture

The model's configuration (or architecture) specifies what layers the model contains, and how these layers are connected*. If you have the configuration of a model, then the model can be created with a freshly initialized state for the weights and no compilation information.

*Note this only applies to models defined using the functional or Sequential apis not subclassed models.

Configuration of a Sequential model or Functional API model

These types of models are explicit graphs of layers: their configuration is always available in a structured form.

APIs

get_config() and from_config()

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

get_config() and from_config()

Calling config = model.get_config() will return a Python dict containing the configuration of the model. The same model can then be reconstructed via Sequential.from_config(config) (for a Sequential model) or Model.from_config(config) (for a Functional API model).

The same workflow also works for any serializable layer.

Layer example:

layer = keras.layers.Dense(3, activation="relu")

layer_config = layer.get_config()

new_layer = keras.layers.Dense.from_config(layer_config)

Sequential model example:

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

config = model.get_config()

new_model = keras.Sequential.from_config(config)