How to extract features from layers in TensorFlow

Feature extraction in quite common while using transfer learning in ML.In this tutorial you will learn how to extract features from tf.keras.Sequential model using get_layer method.

In this post we will build a sequential model with multiple layers where each layer of model contains an input and output attribute, then we will use get_layer method to extract output of the layers.

Lets understand this with below code snippets.

Instantiate Sequential model with three layers


import tensorflow as tf

model = tf.keras.Sequential([
        tf.keras.Input(4,),
        tf.keras.layers.Dense(3, activation="tanh", name="layer1"),
        tf.keras.layers.Dense(4, activation="relu", name="layer2"),
        tf.keras.layers.Dense(2, activation="sigmoid",name="layer3"),
])
  
Build the model

input = tf.random.normal((1,4))
final_output = model(input)

print(input)
print(final_output)
  
  
Output

tf.Tensor([[-0.93511236 -1.5531812   0.03609114 -1.3348812 ]], shape=(1, 4), dtype=float32)
tf.Tensor([[0.5384151  0.33594143]], shape=(1, 2), dtype=float32)
  

Extract features of "layer1"

features_layer1 = tf.keras.models.Model(
    inputs=model.inputs,
    outputs=model.get_layer(name="layer1").output,
)

print(features_layer1)
  
Output

tf.Tensor([[-0.753606    0.9534933  -0.56991524]], shape=(1, 3), dtype=float32)
  
Extract features of "layer2"

features_layer2 = tf.keras.models.Model(
    inputs=model.inputs,
    outputs=model.get_layer(name="layer2").output,
)

print(features_layer2(input))
  

Output


tf.Tensor([[0.6845998  0.47238696 0.         0.04091616]], shape=(1, 4), dtype=float32)
  
Extract features of "layer3"

features_layer3 = tf.keras.models.Model(
    inputs=model.inputs,
    outputs=model.get_layer(name="layer3").output,
)

print(features_layer3(input))
  
  
Output

tf.Tensor([[0.5384151  0.33594143]], shape=(1, 2), dtype=float32)
  

Output for "layer3" and model output stored in "final_output" would be same


print(final_output)
print(features_layer3(input))
  
Output

tf.Tensor([[0.5384151  0.33594143]], shape=(1, 2), dtype=float32)
tf.Tensor([[0.5384151  0.33594143]], shape=(1, 2), dtype=float32)
  

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