tensorflow - Extracting activations from a specific layer of neural network

Question

I was working on an image recognition problem. After training the model, I saved the architecture as well as weights. Now I want to use the model for extracting features from other images and perform SVM on that. For this, I want to remove the last two layers of my model and get the values calculated by the CNN and fully connected layers till then. How can I do that in Keras?

question from:https://stackoverflow.com/questions/65857667/extracting-activations-from-a-specific-layer-of-neural-network

Answer 1

# a simple model
model = keras.models.Sequential([
    keras.layers.Input((32,32,3)),
    keras.layers.Conv2D(16, 3, activation='relu'),
    keras.layers.Flatten(),
    keras.layers.Dense(10, activation='softmax')
])

# after training
feature_only_model = keras.models.Model(model.inputs, model.layers[-2].output)

feature_only_model take a (32,32,3) for input and the output is the feature vector