Convolutional2D Siamese Network in Keras

Question

I'm trying to use Keras's Siamese layer in conjunction with a shared Convolution2D layer. I don't need the input to pass through any other layers before the Siamese layer but the Siamese layer requires that input layers be specified. I can't figure out how to create the input layers to match the input of the conv layer. The only concrete example of the Siamese layer being used I could find is in the tests where Dense layers (with vector inputs) are used as input. Basically, I want an input layer that allows me to specify the image dimensions as input so they can be passed on to the shared conv layer.

In code I have something like the following:

img_rows = 28
img_cols = 28
img_input_shape = (1, img_rows, img_cols)

shared = Sequential()

shared.add(Convolution2D(nb_filters, nb_conv, nb_conv,
                        border_mode='valid',
                        input_shape=img_input_shape))
shared.add(Activation('relu'))
# .... more layers, etc.

right_input_layer = SomeInputLayer(input_shape=img_input_shape) # what should SomeInputLayer be?
left_input_layer = SomeInputLayer(input_shape=img_input_shape)
siamese = Siamese(shared, [left_input_layer, right_input_layer], merge_mode='concat')

model = Sequential()
model.add(siamese)
# ...
model.compile(loss=contrastive_loss, optimizer='rmsprop')

What should SomeInputLayer be? Or is my appraoch in general incorrect?

Answer 1

Okay, I figured it out. The "abstract" Layer class is basically a pass through layer which is just what I need. I was also making a mistake where I thought Siamese could take an entire model (i.e. multiple layers) but it in fact only takes a single layer. To make the creation of these Siamese layers less painful there is a add_shared_layer helper function.

I should also point out this pull request that would allow a shared layer to the first layer in a model, exactly what I am trying to do.