A model in Keras is composed of layers. There are in-built layers present in Keras which you can directly import like Conv2D, Pool, Flatten, Reshape, etc. But sometimes you need to add your own custom layer. In this blog, we will learn how to add a custom layer in Keras.
There are basically two types of custom layers that you can add in Keras.
Lambda Layer
Lambda layer is useful whenever you need to do some operation on previous layer and do not want to add any trainable weights to it.
Let say you want to add your own activation function (which is not built-in Keras) to a layer. Then you first need to define a function which will take the output from the previous layer as input and apply custom activation function to it. We then pass this function to lambda layer.
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from keras.layers import Lambda from keras import backend as K # defining a custom non linear function def activation_relu(inputs): return K.maximum(0.,inputs) # call function using lambda layer squashed_output = Lambda(activation_relu)(inputs) # where inputs are output from previous layer |
Custom Class Layer
Sometimes you want to create your own layer with trainable weights which is not in-built in Keras. In that case you need to create a custom class layer where you need to define following methods.
- __init__ method to initialize class variable and super class variables
- build method to define weights.
- call method where you will perform all your operations.
- compute_output_shape method to define output shape of this custom layer
Lets see an example of a custom layer class. Here you only need to focus on the architecture of the class.
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class MyLayer(Layer): def __init__(self, output_dim, **kwargs): self.output_dim = output_dim super(MyLayer, self).__init__(**kwargs) def build(self, input_shape): # Create a trainable weight variable for this layer. self.W= self.add_weight(name='kernel', shape=(input_shape[1], self.output_dim), initializer='uniform', trainable=True) self.built = True def call(self, x): return K.dot(x, self.W) def compute_output_shape(self, input_shape): return (input_shape[0], self.output_dim) |
In the build method defining self.built = True is necessary. Also, you can see that all logic is written inside call(self, inputs) method. comput_output_shape will define the output shape of the layer.
You can also pass multiple input tensor to this custom layer. The only thing you need to do is, pass multiple inputs using a list.
Hope you enjoy reading.
If you have any doubt/suggestion please feel free to ask and I will do my best to help or improve myself. Good-bye until next time.