Datasets:
HenryAI
/
KerasBERTv1-Data

Dataset card Data Studio Files Community
text
stringlengths
0
4.99k
show_shapes=False,
show_dtype=False,
show_layer_names=True,
rankdir="TB",
expand_nested=False,
dpi=96,
)
Converts a Keras model to dot format and save to a file.
Example
input = tf.keras.Input(shape=(100,), dtype='int32', name='input')
x = tf.keras.layers.Embedding(
output_dim=512, input_dim=10000, input_length=100)(input)
x = tf.keras.layers.LSTM(32)(x)
x = tf.keras.layers.Dense(64, activation='relu')(x)
x = tf.keras.layers.Dense(64, activation='relu')(x)
x = tf.keras.layers.Dense(64, activation='relu')(x)
output = tf.keras.layers.Dense(1, activation='sigmoid', name='output')(x)
model = tf.keras.Model(inputs=[input], outputs=[output])
dot_img_file = '/tmp/model_1.png'
tf.keras.utils.plot_model(model, to_file=dot_img_file, show_shapes=True)
Arguments
model: A Keras model instance
to_file: File name of the plot image.
show_shapes: whether to display shape information.
show_dtype: whether to display layer dtypes.
show_layer_names: whether to display layer names.
rankdir: rankdir argument passed to PyDot, a string specifying the format of the plot: 'TB' creates a vertical plot; 'LR' creates a horizontal plot.
expand_nested: Whether to expand nested models into clusters.
dpi: Dots per inch.
Returns
A Jupyter notebook Image object if Jupyter is installed. This enables in-line display of the model plots in notebooks.
model_to_dot function
tf.keras.utils.model_to_dot(
model,
show_shapes=False,
show_dtype=False,
show_layer_names=True,
rankdir="TB",
expand_nested=False,
dpi=96,
subgraph=False,
)
Convert a Keras model to dot format.
Arguments
model: A Keras model instance.
show_shapes: whether to display shape information.
show_dtype: whether to display layer dtypes.
show_layer_names: whether to display layer names.
rankdir: rankdir argument passed to PyDot, a string specifying the format of the plot: 'TB' creates a vertical plot; 'LR' creates a horizontal plot.
expand_nested: whether to expand nested models into clusters.
dpi: Dots per inch.
subgraph: whether to return a pydot.Cluster instance.
Returns
A pydot.Dot instance representing the Keras model or a pydot.Cluster instance representing nested model if subgraph=True.
Raises
ImportError: if graphviz or pydot are not available.Serialization utilities
CustomObjectScope class
tf.keras.utils.custom_object_scope(*args)
Exposes custom classes/functions to Keras deserialization internals.
Under a scope with custom_object_scope(objects_dict), Keras methods such as tf.keras.models.load_model or tf.keras.models.model_from_config will be able to deserialize any custom object referenced by a saved config (e.g. a custom layer or metric).
Example
Consider a custom regularizer my_regularizer:
layer = Dense(3, kernel_regularizer=my_regularizer)
config = layer.get_config() # Config contains a reference to `my_regularizer`
...
# Later:
with custom_object_scope({'my_regularizer': my_regularizer}):
layer = Dense.from_config(config)
Arguments
*args: Dictionary or dictionaries of {name: object} pairs.
get_custom_objects function
tf.keras.utils.get_custom_objects()
Retrieves a live reference to the global dictionary of custom objects.
Updating and clearing custom objects using custom_object_scope is preferred, but get_custom_objects can be used to directly access the current collection of custom objects.
Example
get_custom_objects().clear()
get_custom_objects()['MyObject'] = MyObject
Returns
Global dictionary of names to classes (_GLOBAL_CUSTOM_OBJECTS).
register_keras_serializable function