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Hi I am quite unexperienced with that topic.
I try to compare two images and interpret this difference. My Idea is to train a siamese CNN and a standard neural network (NN). The later one should analyse the cross correlation matrix of the two feature vectors. In order to produce this matrix the following function is used:

Python
```def NCC(a, b):
l=a.shape[1]
av_a=tf.math.reduce_mean(a)
av_b=tf.math.reduce_mean(b)
a=a-av_a
b=b-av_b
norm_a=tf.math.sqrt(tf.math.reduce_sum(a*a))
norm_b=tf.math.sqrt(tf.math.reduce_sum(b*b))
a=a/norm_a
b=b/norm_b
A=tf.reshape(tf.repeat(a, axis=0, repeats=l),(l,l))
B=tf.reshape(tf.repeat(b, axis=0, repeats=l),(l,l))
ncc=Flatten()(A*tf.transpose(B))
return ncc```

(This function produces a matrix with entries a_i*b_j for all combinations i and j)
I call NCC in the model between the CNN and the NN simply by using :
Python
```input_nn=NCC(feature_vectorA,feature_vectorB)
```

This function works well with batch size 1. However I get problems if I increase the batch size, due to
`tf.reshape(tf.repeat(a, axis=0, repeats=l),(l,l))`
because the input vector has the shape (batch_size,l). I need for the NN (after NCC) the output vector with shape (batch_size,l^2). Any hint how I can manage this, is appreciated.

What I have tried:

I tried also to use this function in a Lamda-Layer, e.g.
Python
```input_nn=Lambda(NCC)(feature_vectorA,feature_vectorB)
```

However this seems not to work
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