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2. The Layer class


The Layer class is easy to understand. It is the base class for the neural network layers in Keras. It defines the forward pass of the computation in a layer.

Here is a simple example of inheriting the Layer class to build a custom layer.

from tensorflow import keras

class SimpleDense(keras.layers.Layer):
  def __init__(self, units=32):
      super(SimpleDense, self).__init__()
      self.units = units
  def build(self, input_shape):
      self.w = self.add_weight(shape=(input_shape[-1], self.units),
      self.b = self.add_weight(shape=(self.units,),
  def call(self, inputs):
      return tf.matmul(inputs, self.w) + self.b

From this example, we can see that a Layer instance is a collection of tensors and computations between them. It trackes the tensors in its attributes, and defines the computation in call(). There are 4 methods to look into in the example. They are __init__(), build(), add_weight(), and call(). The __init__(), build(), and call() are expected to be overriden by the users, while add_weight() is not. Let's see how they work one by one.

The __init__() function is easy to understand. It just records the arguments from the caller with the attributes.

The function


The build() function is to create the tf.Variables in the layer, which are the weight and bias in the example above. Because the tf.Variables are used by the call() function, it would have to be created before the call() function is called. Moreover, we don't want the variables to be created multiple times. The question we want to answer here is how the build function is called under the hood.

A lazy mechanism is implemented for build() with the Layer._maybe_build() function, whose core logic is shown as follows. The Layer instance would use the self.built attribute to record whether build() has been called. Any code that would need the layer to be built would call this _maybe_build() function to ensure the layer is built and built only once.


def _maybe_build(self, inputs):
  if not self.built:
    input_shapes = tf_utils.get_shapes(inputs)

The tf_utils.get_shapes(inputs) is a function in Keras to get the shapes of the input tensors.

Here is an example of calling _maybe_build() secretly. We create a layer. We call the layer with a tensor without explicitly calling build().

layer = SimpleDense(4)
layer(tf.ones((2, 2)))


<tf.Tensor: shape=(2, 4), dtype=float32, numpy=
array([[ 0.02684689, -0.07216483, -0.04574138,  0.03925534],
       [ 0.02684689, -0.07216483, -0.04574138,  0.03925534]],

The example runs successfully because the layer call would call the __call__() function, which calls the call() function. Before calling the call() function, __call__() would call _maybe_build() first to ensure the tf.Variables are created. The pseudo-code is shown as follows.


class Layer(module.Module, ...):
  def __call__(self, inputs, **kwargs):

This lazy pattern appears many times in Keras source code. When ensuring something is called and don't want it to be called multiple times, you should use this pattern.

The Layer.add_weight() function


We would also like to see how these tf.Variables are created in the add_weight() function. Here is the pseudo-code for the core logic of the add_weight() function.

It creates the variable and asks the backend to track the variable. The variable will be appended to different lists depending on if it is trainable.

The backend is a file containing some abstractions of the tensorflow functionalities. In many cases, the Keras code would interact with the backend instead of directly calling the TensorFlow APIs.


class Layer(module.Module, ...):
  def add_weight(self, ...):
    variable = ...  # Create the variable.
    backend.track_variable(variable)  # Track the variable.
    if trainable:

The process for creating the variable is a function call, which is not so different from using tf.Variable(...) to directly create the variable.

We need the backend to track the variable for model saving and computation optimization. Now, the question is how the backend is tracking the variable. The code is shown as follows.


def track_variable(v):
  """Tracks the given variable for initialization."""
  if context.executing_eagerly():
  graph = v.graph if hasattr(v, 'graph') else get_graph()

We encountered two important concepts: eager mode and graph mode. You can click the link for detailed introductions.

Here is a short explanation. You can think of eager execution as plain Python code execution. The tensors are all concrete values instead of placeholders. The operations are read and executed only when we run that line of code in the Python interpreter.

However, in graph mode, all the tensors and operations are collected in advance to build the computational graph before any actual value is input for computation. The graph is then optimized for execution speed. It is similar to a compiled language, like the C programming language, which you can turn on various optimization options to make the compiled executable file run faster.

TensorFlow API
tf.executing_eagerly() is to check whether TensorFlow is running in eager mode or not. (Link)

By default, everything runs in eager mode. As shown in the code above, in eager mode, we don't need to track the variables because it would not compile the computation graph.

In graph mode, the code above would record the tf.Variable to the _GRAPH_VARIABLES, which is a dictionary mapping the TensorFlow computational graphs to a list of tf.Variables. With this dictionary, Keras can track all the tf.Variables for features like clearing the value of them.

The function


Let's see another use case of a layer. Instead of being part of a model, the layer can directly be called to get the output. We can call the layer with a Numpy array, it returns a tf.Tensor as the result.

import tensorflow as tf
import numpy as np

layer = tf.keras.layers.Dense(input_shape=(10,), units=15)
x = np.random.rand(20, 10)
output = layer(x)
print(output.shape)  # (20, 15)

When we call layer(x), it calls the __call__() function of the Layer() class. The __call__() function would call the call() function, which implements the forward pass of the layer.

Calling the layer with a Numpy array, the __call__() function will just convert it to a tf.Tensor and call the call() function. If in eager mode, the function are directly called and executed eagerly. If in graph mode, we will need to convert the function to a computational graph before calling it.

TensorFlow API
tf.function() is the public API in TensorFlow for converting a normal function into a computation graph. (Link)

There is another use case that quite separated from the use case above, which is calling the layer with a KerasTensor. It is a class used when creating models using the functional API. It is a symbolic tensor without actual value, but only representing the shapes and types of intermediate output tensors between the layers. We will introduce more about it when we introduce the Model class.

The pseudo-code for the __call__() function is shown as follows.

class Layer(module.Module, ...):

  def __call__(self, inputs, **kwargs):

    if isinstance(inputs, keras_tensor.KerasTensor):
      inputs = convert_to_tf_tensor(inputs)
      outputs =
      return convert_to_keras_tensor(outputs)

    if isinstance(inputs, np.ndarray):
      inputs = tf.Tensor(inputs)

    # Check the inputs are compatible with the layer.
    input_spec.assert_input_compatibility(self.input_spec, inputs,

    if context.executing_eagerly():
      call_fn = convert_to_tf_function(
      return call_fn(inputs)

Input compatibility checking

Notably, in the code above, the inputs compatibility is being checked before the actual call. This is to ensure the bug is being caught early and throw out a meaningful error message. If you run the following code, you will get an error. It calls the Dense layer with vectors of length 5 first, which causes the layer to initialize the weights for these vectors. Then, it calls the same layer again with vectors of length 4, which are not compatible with the weights created just now.

layer = layers.Dense(3)
layer(np.random.rand(10, 5))
layer(np.random.rand(10, 4))

Error message:

ValueError: Input 0 of layer dense is incompatible with the layer: expected axis -1 of input shape to have value 5 but received input with shape (10, 4)

The inputs compatibility information is recorded in self.input_spec of a layer, which is a function with @property decorator. Now, we would like to see how are the input_spec being recorded and used to check new inputs.

The base Layer class doesn't record this input_spec. Each layer should record their own self.input_spec in build(). In build(), they usually need to create the weights of the layer using the input_shape. Meanwhile, they can define a self.input_spec for what inputs are compatible with these weights. For example, in, they set the input_spec for a fixed last dimension by creating an InputSpec instance (Source).

The signature of the InputSpec is as follows. There are many specifications to set, like the shape, data type, number of dimensions, upper and lower bound of dimensions.


class InputSpec(object):
  def __init__(self,

In __call__(), assert_input_compatibility() would check all the specifications of the recorded self.input_spec for the given inputs for compatibility.