CODEWITHSUNDEEP
tensorflowkerasconv-neural-network
man zet
man zet

Reputation: 901

Two parallel conv2d layers (keras)

I want two build a neural network that takes two separate matrices with same dimensions (for example grey-scale images) as input, and outputs a value between -1 and 1 (probably tanh).

I would like to build the network so that there are two seperate convolutional layers as inputs. Each one takes one matrix(or image). An then that these one are combined in a following layer. So i want it to look something like that:

enter image description here

My first question is can i do this in keras (or if not in tensorflow)? The second Question is? Does it make sense? Because I could also very easy composite the two matrices together, and only use one conv2d layer. So something like this:

enter image description here

what I want to do exactly would go too far. But can you imagine a situation where the first version would make more sense?

Upvotes: 3

Views: 2152

Answers (1)

ixeption
ixeption

Reputation: 2050

You can do that in Keras and is makes sense, if the inputs are different. To do so in keras first you need a multiple input model and you have to concatenate the outputs of the convolutional layer together.

input_1= Input(shape=(x,y), name='input_1')
input_2= Input(shape=(x,y), name='input_1')
c1 = Conv2D(filter_size, kernel_size))(input_1)
p1 = MaxPooling2D(pool_size=(2, 2)(input_1)
f1 = Flatten()(p1)
c2 = Conv2D(filter_size, kernel_size))(input_2)
p2 = MaxPooling2D(pool_size=(2, 2)(c2)
f2 = Flatten()(p2)

x = concatenate([f1, f2])
x = Dense(num_classes, activation='sigmoid')(x)

model = Model(inputs=[input_1, input_2], outputs=[x])
model.compile('adam', 'binary_crossentropy', metrics=['accuracy'])

Depending on you data it could also be possible to share the convolution layers, therefore you can just define dem once and reuse them. Weights are shared in this case.

conv = Conv2D(filter_size, kernel_size))
pooling = MaxPooling2D(pool_size=(2, 2)
flatten = Flatten()

f1 = flatten(pooling(conv(input_1)))
f2 = flatten(pooling(conv(input_2)))

Upvotes: 5

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