Keras Customized Layer

Keras is the most popular Python Library. Keras provides you an environment to work with a model. The models contain various layers. The neural network is a combination of a number of layers. Each of these layers has an arrangement in order. These layers receive input, process it accordingly, and produce the desired output. This output is fed as the input to the next layer. Let us learn about Keras Customized Layer.

Customized Layers of Keras

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Keras Customized Layer

Keras also offers you an opportunity to design your own layer. This layer is known as Customized Layer. This is the most useful opportunity that Keras offers. Sometimes, the layer that Keras provides you do not satisfy your requirements. So, you have to build your own layer. Here, it allows you to apply the necessary algorithms for the input data.

Adding a Custom Layer in Keras

There are two ways to include the Custom Layer in the Keras. The methods are:

  • Custom Class Layer
  • Lambda Layer

Custom Class Layer

This method allows you to create a Custom Layer. To build the custom layer, this method provides you four methods:

  • _init_: This method helps you to initialize the class variables. It contains arguments that allow you to give the dimension of the output.
  • Build(input_shape): This function allows you to define the raining weights.
  • Call(x): The Call Method performs the exact working of the layer. It is done during the training process.
  • Compute_output_shape(input_shape): It helps you to compute the shape of the output.

Steps to create Custom Layers using Custom Class Layer Method

It is very easy to create a custom layer in Keras.

Step 1: Importing the useful modules

The very first is to import the necessary modules. You need to import the backend module and layer module. Here, both modules have a unique and specific task. The Backend Module helps you to access the dot function. The Layer Module enables you to create the layer. It is a base class. It allows you to sub-class the layers to design your own layer.

To import these modules, refer the code below:
from keras import backend as k
from keras.layers import Layers

Step 2: Definition of a layer

The layer is a class that is further sub-classed. Here, you need to create the layer and name it. For instance, take the name of the layer as TechVidvan.

class TechVidvan (Layer):

Here, with the code above, you are creating a new layer TechVidvan by sub-classing the Layer Class.

Step 3: Initializing the Layer Class

Here, the output_dim enables you to set the dimension of the output. Further, in the next line, it calls the _init_ function of the base class.

def _init_(self, output_dim, **kwargs):
self.output_dim = output_dim
super(TechVidvan, self)._init_(**kwargs)

Step 4: Implement a build method

Here, you have to use the Build Method. It is the main method that enables you to build the layer properly. It enables you to change the inner working of the layer. You can customize the functionalities. You can now call the base class Build function.

Here, the function is defined with one argument – input_shape. It specifies the shape of the input. Further, you need to create the weight according to the input shape. And it is set in the kernel. Now, it creates it in normal initializer mode. Further, the base class Build Method is called.

def build(self, input shape):
self.kernel = self.add_weight(name = ‘kernel’, shape = (input_shape[1], self.output_dim), initializer = ‘normal’, trainable = True)
super(TechVidvan, self).build(input_shape)

Step 5: Implementation of the class method

It is during the training process. The Call Method performs the exact working of the layer. To create a custom class method, follow the code below:

Here, you need to define the call function passing one argument – input_data. This will be the input data for your custom layer. Further, you need to return the dot product of the input data.

def call(self, input_data):
return, self.kernel)

Step 6: Implementation of custom_output_shape method

Here, you need to define the custom_output_shape method while passing one argument input_shape to it. Now, you have to compute the shape of the output. This is using the shape of input data and the dimensions of the output.

def compute _output_shape( self, input_shape): return (input_shape[0], self.output_dim)

Once you implement the Build Method, Call Method, and comput_output_shape Method, it completes the creation of a custom layer.


Keras is a proficient library that provides you a user-friendly environment. It focuses on the idea of models. It is the stack of layers. Sometimes, the layers that Keras provides do not satisfy all your requirements. So, you need to create a layer of your choice. Keras allows you to build layers according to your requirements. This concept is the Keras Customized Layer, where you can design the functionality of the layer on your own.

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