The tf.keras.Model class in TensorFlow provides a way to create custom models. This class is fundamental for defining complex models in Keras.

  • Overview: tf.keras.Model is the base class for all Keras models. It defines the structure of the model and provides methods for compiling, fitting, evaluating, and predicting.

  • Usage: To use tf.keras.Model, you typically define a subclass of tf.keras.Model and override the build method to specify the layers of the model.

  • Example:

import tensorflow as tf

class MyModel(tf.keras.Model):
  def __init__(self):
    super(MyModel, self).__init__()
    self.conv1 = tf.keras.layers.Conv2D(32, kernel_size=(3, 3), activation='relu')
    self.flatten = tf.keras.layers.Flatten()
    self.d1 = tf.keras.layers.Dense(128, activation='relu')
    self.d2 = tf.keras.layers.Dense(10)

  def call(self, x):
    x = self.conv1(x)
    x = self.flatten(x)
    x = self.d1(x)
    return self.d2(x)