tf.keras.Model

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

Inherits From: Layer

Used in the notebooks

Used in the guide Used in the tutorials

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__(