CODEWITHSUNDEEP
pythonkeras
Spinkoo
Spinkoo

Reputation: 2080

Feed keras model input to the output layer

So I am building a keras sequential model in which the last output layer is an Upsampling2D layer & I need to feed the input image to that output layer to do a simple operation and return the output, any ideas?

EDIT :

The model mentioned before is the generator of a GAN model in which I need to add the input image to the output of the generator before feeding it to the discriminator

Upvotes: 0

Views: 290

Answers (2)

Spinkoo
Spinkoo

Reputation: 2080

So for the future reference, I solved it by using lambda layers as follow :

# z is the input I needed to use later on with the generator output to perform a certain function

generated_image = self.generator(z)        
generated_image_modified=tf.keras.layers.Concatenate()([generated_image,z])

# with x[...,variable_you_need_range] you can access the input we just concatenated in your train loop

lambd = tf.keras.layers.Lambda(lambda x: your_function(x[...,2:5],x[...,:2]))(generated_image_modified)

full_model = self.discriminator(lambd)
self.combined = Model(z,outputs = full_model)

Upvotes: 0

Mr. For Example
Mr. For Example

Reputation: 4313

1.You can define a backbone model using inputs of pre-trained model and the outputs of the last layer before the output layer of pre-trained model

2.Base on that backbone model, defined new model have that new skip connection and the output layer as same as pre-trained model

3.Set the weights of output layer in new model to equal to weights of output layer in pre-trained model, using: new_model.layers[-1].set_weights(pre_model.layers[-1].get_weights())

Here is one good article about Adding Layers to the middle of a pre-trained network whithout invalidating the weights

Upvotes: 1

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