Keras allows to create our own customized layer. Once a new layer is created, it can be used in any model without any restriction. Let us learn how to create new layer in this blog.

Keras provides a base layer class, Layer which can sub-classed to create our own customized layer. Let us create a simple layer which will find weight based on normal distribution and then do the basic computation of finding the summation of the product of input and its weight during training.

Step 1: Import the necessary module
First, let us import the necessary modules −
from keras import backend as K
from keras.layers import Layer
Here,
backend is used to access the dot function.
Layer is the base class and we will be sub-classing it to create our layer
Step 2: Define a layer class
Let us create a new class, MyCustomLayer by sub-classing Layer class −
class MyCustomLayer(Layer):
...
Step 3: Initialize the layer class
Let us initialize our new class as specified below −
def __init__(self, output_dim, **kwargs):
self.output_dim = output_dim
super(MyCustomLayer, self).__init__(**kwargs)
Here,
Line 2 sets the output dimension.
Line 3 calls the base or super layer’s init function.
Step 4: Implement build method
build is the main method and its only purpose is to build the layer properly. It can do anything related to the inner working of the layer. Once the custom functionality is done, we can call the base class build function. Our custom build function is as follows −
def build(self, input_shape):
self.kernel = self.add_weight(name = 'kernel',
shape = (input_shape[1], self.output_dim),
initializer = 'normal', trainable = True)
super(MyCustomLayer, self).build(input_shape)
Here,
Line 1 defines the build method with one argument, input_shape. Shape of the input data is referred by input_shape.
Line 2 creates the weight corresponding to input shape and set it in the kernel. It is our custom functionality of the layer. It creates the weight using ‘normal’ initializer.
Line 6 calls the base class, build method.
Step 5: Implement call method
call method does the exact working of the layer during training process.
Our custom call method is as follows
def call(self, input_data):
return K.dot(input_data, self.kernel)
Here,
Line 1 defines the call method with one argument, input_data. input_data is the input data for our layer.
Line 2 return the dot product of the input data, input_data and our layer’s kernel, self.kernel
Step 6: Implement compute_output_shape method
def compute_output_shape(self, input_shape): return (input_shape[0], self.output_dim)
Here,
Line 1 defines compute_output_shape method with one argument input_shape
Line 2 computes the output shape using shape of input data and output dimension set while initializing the layer.
Implementing the build, call and compute_output_shape completes the creating a customized layer. The final and complete code is as follows
from keras import backend as K from keras.layers import Layer
class MyCustomLayer(Layer):
def __init__(self, output_dim, **kwargs):
self.output_dim = output_dim
super(MyCustomLayer, self).__init__(**kwargs)
def build(self, input_shape): self.kernel =
self.add_weight(name = 'kernel',
shape = (input_shape[1], self.output_dim),
initializer = 'normal', trainable = True)
super(MyCustomLayer, self).build(input_shape) #
Be sure to call this at the end
def call(self, input_data): return K.dot(input_data, self.kernel)
def compute_output_shape(self, input_shape): return (input_shape[0], self.output_dim)
Using our customized layer
Let us create a simple model using our customized layer as specified below −
from keras.models import Sequential
from keras.layers import Dense
model = Sequential()
model.add(MyCustomLayer(32, input_shape = (16,)))
model.add(Dense(8, activation = 'softmax')) model.summary()
Here,
Our MyCustomLayer is added to the model using 32 units and (16,) as input shape
Running the application will print the model summary as below −
Model: "sequential_1"
_________________________________________________________________
Layer (type) Output Shape Param
#================================================================
my_custom_layer_1 (MyCustomL (None, 32) 512
_________________________________________________________________
dense_1 (Dense) (None, 8) 264
=================================================================
Total params: 776
Trainable params: 776
Non-trainable params: 0

So, this brings us to the end of blog. This Tecklearn ‘How to create our own Customized Layer in Keras Library’ blog helps you with commonly asked questions if you are looking out for a job in Artificial Intelligence. If you wish to learn Artificial Intelligence and build a career in AI or Machine Learning domain, then check out our interactive, Artificial Intelligence and Deep Learning with TensorFlow Training, that comes with 24*7 support to guide you throughout your learning period. Please find the link for course details:

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Introduction to Deep Learning and AI

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Advantage of Deep Learning over Machine learning
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Python Programming Language
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Python for Data Science and AI
Python Language Essentials
Python Libraries – Numpy and Pandas
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More Prerequisites for Deep Learning and AI

Pandas for Data Analysis
Machine Learning Basic Concepts
Normalization
Data Set
Machine Learning Concepts
Regression
Logistic Regression
SVM – Support Vector Machines
Decision Trees
Python Libraries for Data Science and AI

Introduction to Neural Networks

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Epoch, Forward & backword propagation
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Multi-layered Neural Networks

Error Back propagation issues
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Regularization techniques in Deep Learning

Deep Learning Libraries

Tensorflow
Keras
OpenCV
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Building of Simple Neural Network from Scratch from Simple Equation

Training the model

Dual Equation Neural Network

TensorFlow
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Introduction to Keras API

Define Keras
How to compose Models in Keras
Sequential Composition
Functional Composition
Predefined Neural Network Layers
What is Batch Normalization
Saving and loading a model with Keras
Customizing the Training Process
Using TensorBoard with Keras
Use-Case Implementation with Keras

GPU in Deep Learning

Introduction to GPUs and how they differ from CPUs
Importance of GPUs in training Deep Learning Networks
The GPU constituent with simpler core and concurrent hardware
Keras Model Saving and Reusing
Deploying Keras with TensorBoard

Keras Cat Vs Dog Modelling

Activation Functions in Neural Network

Optimization Techniques

Some Examples for Neural Network

Convolutional Neural Networks (CNN)

Introduction to CNNs
CNNs Application
Architecture of a CNN
Convolution and Pooling layers in a CNN
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Introduction to RNN Model
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