How to concatenate two layers in keras?

Concatenating Layers in Keras

In Keras, concatenating layers allows you to combine the outputs of multiple layers into a single output. This is particularly useful for:

• Merging features from different branches of a network.
• Combining outputs from convolutional layers with fully connected layers.
• Implementing skip connections in residual networks.

Methods for Concatenating Layers

1. Using the `concatenate()` function

The concatenate() function from Keras’s functional API is the most common way to concatenate layers. It takes a list of input tensors and combines them along a specified axis.

from tensorflow.keras.layers import Input, Dense, Concatenate
from tensorflow.keras.models import Model

# Input layers
input_a = Input(shape=(10,))
input_b = Input(shape=(5,))

# Define layers
dense_a = Dense(8, activation='relu')(input_a)
dense_b = Dense(4, activation='relu')(input_b)

# Concatenate layers
merged = Concatenate(axis=1)([dense_a, dense_b])

# Output layer
output = Dense(1, activation='sigmoid')(merged)

# Define model
model = Model(inputs=[input_a, input_b], outputs=output)

In this example, two input layers input_a and input_b are defined with different shapes. Two dense layers, dense_a and dense_b, process the inputs respectively. The Concatenate() function combines the outputs of these layers along axis 1 (features), creating a merged layer. Finally, a dense layer with a sigmoid activation function is used as the output layer.

2. Using the `Lambda` layer

The Lambda layer can also be used to concatenate layers. It applies a custom function to the input tensors.

from tensorflow.keras.layers import Input, Dense, Lambda
from tensorflow.keras.models import Model

# Input layers
input_a = Input(shape=(10,))
input_b = Input(shape=(5,))

# Define layers
dense_a = Dense(8, activation='relu')(input_a)
dense_b = Dense(4, activation='relu')(input_b)

# Concatenate using Lambda layer
merged = Lambda(lambda x: tf.concat(x, axis=1))([dense_a, dense_b])

# Output layer
output = Dense(1, activation='sigmoid')(merged)

# Define model
model = Model(inputs=[input_a, input_b], outputs=output)

This code defines a lambda function that uses tf.concat to concatenate the input tensors along axis 1. The Lambda layer applies this function to the outputs of dense_a and dense_b, achieving the concatenation.

Axis for Concatenation

The axis parameter in the concatenate() function defines the dimension along which the tensors are concatenated:

Choosing the appropriate axis depends on the specific architecture and desired output.