Connecting Functional API models together

@fchollet I’m trying to connect 2 functional API models together. here’s the summary of the 2 models:

The First “Input” Model (It works as a single model just fine):

The Second Model that is supposed to be connected to the first model:

I’m trying to connect them together like this:

model = Model(input=generator.input, output=[discriminator.output[0], discriminator.output[1]])

But I get this error:

Graph disconnected: cannot obtain value for tensor discriminator_input at layer “discriminator_input”. The following previous layers were accessed without issue: []

I tried to make a model out of them like this: Model(input=[generator.input, discriminator.input], output=[discriminator.output[0], discriminator.output[1]])

But this code just resulted in the second Model (and not the 2 of them together), or at least this is what I think after getting a summary of the model and plotting it’s structure.

can we do this in Keras (connecting functional API models) or is there another way? Thanks

[dieuwkehupkes]

You should connect the output layer of the first network to the input layer of the second network. Something like:

Model(input=generator.input, output=discriminator(generator.output))

[krishhtof]

You should connect the output layer of the first network to the input layer of the second network. Something like:

Model(input=generator.input, output=discriminator(generator.output))

Hi @dieuwkehupkes,

I know it’s an old comment of yours, but it is still helpful. I’m using the same code as you’ve mentioned above to combine 2 separate Keras Models and it works correctly. EXCEPT for one small problem when I want to print out the summary of the newly combined model. When I print out the summary, I get the following output:

The main info from above to look out for is the last layer called model_1(Model). This is actually the model that I connected my first model to which you can see correctly above the model_1(Model) layer. What I want is the summary method from keras to print the entire model with all its layers and not just model_1(Model) as it shows above.

Do you or does anyone else know how one can achieve this in Keras?