CODEWITHSUNDEEP
pythontensorflowkerasdeep-learning
Odgiiv
Odgiiv

Reputation: 713

How to run keras model twice before loss?

I want to compute a loss function which uses output of the network twice on different inputs. For example as hypothetically,

first_output = model(first_input)
second_output = model(second_input)
loss = mean_absolute_error(first_output, second_output)

How to achieve this in tensorflow or keras?

Update: Thank you guys for replies. I want to reimplement this paper in keras or tensorflow. As explained in it, "critic" network which is discriminator in GAN has two inputs and run through them one by one and compute loss function depending on outputs and compute gradient. Main problem is how to make possible in tensorflow or keras?

Upvotes: 2

Views: 546

Answers (1)

Engineero
Engineero

Reputation: 12938

You could try using keras.layers.merge. I have used this before to make Siamese networks with something like:

first_output = model(first_input)
second_output = model(second_input)
mae = lambda x: mean_absolute_error(x[0], x[1])
distance = merge(inputs=[first_output, second_output],
                 mode=mae,
                 output_shape=lambda x: x[0],
                 name='mean_absolute_error')

For the Siamese network example, you'd then typically make some prediction on this distance measure with something like:

prediction = Dense(2, activation='softmax', name='prediction')(distance)
model = Model([first_input, second_input], prediction, name='siamese_net')
model.compile(optimizer=Adam(),
              loss=some_loss_function)

using keras.models.Model and keras.layers.Dense for that example.

Note that keras.layers.merge is (I believe) deprecated in the latest versions of Keras, which is really a shame. I think to do something similar with the most modern Keras you would need to use keras.layers.Concatenate to combine the two results followed by keras.layers.Lambda to apply the function.

Upvotes: 1

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