The Model class

Model class

tf.keras.Model()

Model groups layers into an object with training and inference features.

Arguments

• inputs: The input(s) of the model: a keras.Input object or list of keras.Input objects.
• outputs: The output(s) of the model. See Functional API example below.
• name: String, the name of the model.

There are two ways to instantiate a Model:

1 - With the "Functional API", where you start from Input, you chain layer calls to specify the model's forward pass, and finally you create your model from inputs and outputs:

import tensorflow as tf

inputs = tf.keras.Input(shape=(3,))
x = tf.keras.layers.Dense(4, activation=tf.nn.relu)(inputs)
outputs = tf.keras.layers.Dense(5, activation=tf.nn.softmax)(x)
model = tf.keras.Model(inputs=inputs, outputs=outputs)

2 - By subclassing the Model class: in that case, you should define your layers in __init__ and you should implement the model's forward pass in call.

import tensorflow as tf

class MyModel(tf.keras.Model):

  def __init__(self):
    super(MyModel, self).__init__()
    self.dense1 = tf.keras.layers.Dense(4, activation=tf.nn.relu)
    self.dense2 = tf.keras.layers.Dense(5, activation=tf.nn.softmax)

  def call(self, inputs):
    x = self.dense1(inputs)
    return self.dense2(x)

model = MyModel()

If you subclass Model, you can optionally have a training argument (boolean) in call, which you can use to specify a different behavior in training and inference:

import tensorflow as tf

class MyModel(tf.keras.Model):

  def __init__(self):
    super(MyModel, self).__init__()
    self.dense1 = tf.keras.layers.Dense(4, activation=tf.nn.relu)
    self.dense2 = tf.keras.layers.Dense(5, activation=tf.nn.softmax)
    self.dropout = tf.keras.layers.Dropout(0.5)

  def call(self, inputs, training=False):
    x = self.dense1(inputs)
    if training:
      x = self.dropout(x, training=training)
    return self.dense2(x)

model = MyModel()

Once the model is created, you can config the model with losses and metrics with model.compile(), train the model with model.fit(), or use the model to do prediction with model.predict().

summary method

Model.summary(line_length=None, positions=None, print_fn=None)

Prints a string summary of the network.

Arguments

• line_length: Total length of printed lines (e.g. set this to adapt the display to different terminal window sizes).
• positions: Relative or absolute positions of log elements in each line. If not provided, defaults to [.33, .55, .67, 1.].
• print_fn: Print function to use. Defaults to print. It will be called on each line of the summary. You can set it to a custom function in order to capture the string summary.

Raises

• ValueError: if summary() is called before the model is built.

get_layer method

Model.get_layer(name=None, index=None)

Retrieves a layer based on either its name (unique) or index.

If name and index are both provided, index will take precedence. Indices are based on order of horizontal graph traversal (bottom-up).

Arguments

• name: String, name of layer.
• index: Integer, index of layer.

Returns

A layer instance.

Raises

• ValueError: In case of invalid layer name or index.