python-kerasHow do I use Python and Keras to concatenate layers?

Using Python and Keras to concatenate layers is a common task in deep learning. Here is an example of how to do it:

from keras.layers import Concatenate
from keras.layers import Input
from keras.layers import Dense

# define two sets of inputs
inputA = Input(shape=(32,))
inputB = Input(shape=(32,))

# first branch operates on the first input
x = Dense(8, activation="relu")(inputA)

# second branch opreates on the second input
y = Dense(4, activation="relu")(inputB)

# combine the output of the two branches
z = Concatenate()([x, y])

The code above defines two inputs (inputA and inputB) and two branches (x and y). The output of the two branches is then combined using the Concatenate layer. The output of the Concatenate layer will have a shape of (None, 12).

Code explanation

• from keras.layers import Concatenate: imports the Concatenate layer from Keras.
• from keras.layers import Input: imports the Input layer from Keras.
• from keras.layers import Dense: imports the Dense layer from Keras.
• inputA = Input(shape=(32,)): defines an input layer with a shape of (32,).
• inputB = Input(shape=(32,)): defines an input layer with a shape of (32,).
• x = Dense(8, activation="relu")(inputA): defines a Dense layer with 8 neurons and a ReLU activation on the inputA layer.
• y = Dense(4, activation="relu")(inputB): defines a Dense layer with 4 neurons and a ReLU activation on the inputB layer.
• z = Concatenate()([x, y]): combines the output of the two branches using the Concatenate layer.

Here are some ## Helpful links

• Keras Documentation - Concatenate
• TensorFlow - Concatenate
• Keras Documentation - Input
• Keras Documentation - Dense

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