I want to make a custom layer which is supposed to fuse the output of a Dense Layer with a Convolution2D Layer.
The Idea came from this paper and here's the network:
the fusion layer tries to fuse the Convolution2D tensor (256x28x28) with the Dense tensor (256). here's the equation for it:
y_global => Dense layer output with shape 256
y_mid => Convolution2D layer output with shape 256x28x28
Here's the description of the paper about the Fusion process:
I ended up making a new custom layer like below:
class FusionLayer(Layer):
def __init__(self, output_dim, **kwargs):
self.output_dim = output_dim
super(FusionLayer, self).__init__(**kwargs)
def build(self, input_shape):
input_dim = input_shape[1][1]
initial_weight_value = np.random.random((input_dim, self.output_dim))
self.W = K.variable(initial_weight_value)
self.b = K.zeros((input_dim,))
self.trainable_weights = [self.W, self.b]
def call(self, inputs, mask=None):
y_global = inputs[0]
y_mid = inputs[1]
# the code below should be modified
output = K.dot(K.concatenate([y_global, y_mid]), self.W)
output += self.b
return self.activation(output)
def get_output_shape_for(self, input_shape):
assert input_shape and len(input_shape) == 2
return (input_shape[0], self.output_dim)
I think I got the __init__ and build methods right but I don't know how to concatenate y_global (256 dimesnions) with y-mid (256x28x28 dimensions) in the call layer so that the output would be the same as the equation mentioned above.
How can I implement this equation in the call method?
Thanks so much...
UPDATE: any other way to successfully integrate the data of these 2 layers is also acceptable for me... it doesn't exactly have to be the way mentioned in the paper but it needs to at least return an acceptable output...