Keras - Merge Layer

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It is used to merge a list of inputs. It supports add(), subtract(), multiply(), average(), maximum(), minimum(), concatenate() and dot() functionalities.

Adding a layer

It is used to add two layers. Syntax is defined below −

keras.layers.add(inputs)

Simple example is shown below −

>>> a = input1 = keras.layers.Input(shape = (16,)) 
>>> x1 = keras.layers.Dense(8, activation = 'relu')(a) 
>>> a = keras.layers.Input(shape = (16,)) 
>>> x1 = keras.layers.Dense(8, activation='relu')(a) 
>>> b = keras.layers.Input(shape = (32,)) 
>>> x2 = keras.layers.Dense(8, activation = 'relu')(b) 
>>> summ = = keras.layers.add([x1, x2]) 
>>> summ = keras.layers.add([x1, x2]) 
>>> model = keras.models.Model(inputs = [a,b],outputs = summ)

subtract layer

It is used to subtract two layers. The syntax is defined below −

keras.layers.subtract(inputs)

In the above example, we have created two input sequence. If you want to apply subtract(), then use the below coding −

subtract_result = keras.layers.subtract([x1, x2]) 
result = keras.layers.Dense(4)(subtract_result) 
model = keras.models.Model(inputs = [a,b], outputs = result)

multiply layer

It is used to multiply two layers. Syntax is defined below −

keras.layers.multiply(inputs)

If you want to apply multiply two inputs, then you can use the below coding −

mul_result = keras.layers.multiply([x1, x2]) 
result = keras.layers.Dense(4)(mul_result) 
model = keras.models.Model(inputs = [a,b], outputs = result)

maximum()

It is used to find the maximum value from the two inputs. syntax is defined below −

keras.layers.maximum(inputs)

minimum()

It is used to find the minimum value from the two inputs. syntax is defined below −

keras.layers.minimum(inputs)

concatenate

It is used to concatenate two inputs. It is defined below −

keras.layers.concatenate(inputs, axis = -1)

Functional interface to the Concatenate layer.

Here, axis refers to Concatenation axis.

dot

It returns the dot product from two inputs. It is defined below −

keras.layers.dot(inputs, axes, normalize = False)

Here,

• axes refer axes to perform the dot product.

• normalize determines whether dot product is needed or not.