Model plotting utilities

[source]

plot_model function

tf.keras.utils.plot_model(
    model,
    to_file="model.png",
    show_shapes=False,
    show_dtype=False,
    show_layer_names=True,
    rankdir="TB",
    expand_nested=False,
    dpi=96,
    layer_range=None,
    show_layer_activations=False,
)

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.
• layer_range: input of list containing two str items, which is the starting layer name and ending layer name (both inclusive) indicating the range of layers for which the plot will be generated. It also accepts regex patterns instead of exact name. In such case, start predicate will be the first element it matches to layer_range[0] and the end predicate will be the last element it matches to layer_range[1]. By default None which considers all layers of model. Note that you must pass range such that the resultant subgraph must be complete.
• show_layer_activations: Display layer activations (only for layers that have an activation property).

Raises

• ValueError: if plot_model is called before the model is built.

Returns

A Jupyter notebook Image object if Jupyter is installed. This enables in-line display of the model plots in notebooks.

[source]

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,
    layer_range=None,
    show_layer_activations=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.
• layer_range: input of list containing two str items, which is the starting layer name and ending layer name (both inclusive) indicating the range of layers for which the pydot.Dot will be generated. It also accepts regex patterns instead of exact name. In such case, start predicate will be the first element it matches to layer_range[0] and the end predicate will be the last element it matches to layer_range[1]. By default None which considers all layers of model. Note that you must pass range such that the resultant subgraph must be complete.
• show_layer_activations: Display layer activations (only for layers that have an activation property).

Returns

A pydot.Dot instance representing the Keras model or a pydot.Cluster instance representing nested model if subgraph=True.

Raises

• ValueError: if model_to_dot is called before the model is built.
• ImportError: if graphviz or pydot are not available.