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neural-networkdeep-learningkeras
Ninja_0203
Ninja_0203

Reputation: 45

How to add two output classification layers in keras?

I have a neural network whose job is to classify 10 classes. Further, I want these 10 classes to be classified into 2 classes (positive -> 3 , negative -> 7). How can I achieve this in keras?

Upvotes: 1

Views: 755

Answers (1)

Imran
Imran

Reputation: 13458

It sounds like you are trying to solve two different, but closely related problems. I recommend that you train your first model to predict 10 classes, and then you create a copy of the first model (including weights) except with a different output layer to support binary classification. At this point you can either:

  1. Train only your final dense layer and new output layer, or
  2. Train the entire model with a low learning rate

For more information you can read about Transfer Learning.

Example code:

model.save('model_1') # load this to retrieve your original model
model.pop() # pop output activation layer and associated params
model.pop() # pop final dense layer
model.add(Dense(1), kernel_initializer='normal', activation='sigmoid')
for layer in model.layers[:-2]:
    layer.trainable = False
model.compile(loss='binary_crossentropy', optimizer='nadam', metrics=['accuracy'])
model.fit(x_train, y_train, epochs=50, batch_size=32)

If you want to retrain the whole model then you can omit the loop setting all but the last two layers to untrainable, and choose an optimizer such as SGD with a low learning rate.

Upvotes: 2

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