I created two convolutional neural networks (CNN), and I want to make these networks work in parallel. Each network takes different type of images and they join in the last fully connected layer.
How to do this?
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You essentially need a multi-input model. This can only be done through keras' functional api and can work with the pretrained nets in
keras.applications. To create one you can do this:
from keras.layers import Input, Conv2D, Dense, concatenate from keras.models import Model
1) Define your first model:
in1 = Input(...) # input of the first model x = Conv2D(...)(in1) # rest of the model out1 = Dense(...)(x) # output for the first model
2) Define your second model:
in2 = Input(...) # input of the first model x = Conv2D(...)(in2) # rest of the model out2 = Dense(...)(x) # output for the first model
3) Merge the two models and conclude the network:
x = concatenate([out1, out2]) # merge the outputs of the two models out = Dense(...)(x) # final layer of the network
4) Create the Model:
model = Model(inputs=[in1, in2], outputs=[out])