[keras, custom metric] group images by k, calculate mean for these k and then mse from results.py

# tensorflow inside keras metric, with debugging tf variables

def grouped_mse(k=3):
    def f(y_true, y_pred):
        group_by = tf.constant(k)
        real_size = tf.size(y_pred)

        remainder = tf.truncatemod(real_size, group_by)
        remainder = K.print_tensor(remainder, message="remainder is: ")

        # ignore the rest, this could apply for few in the last minibatch
        y_true = y_true[0:real_size - remainder]
        y_pred = y_pred[0:real_size - remainder]

        real_size = tf.size(y_pred) + 0*remainder
        real_size = K.print_tensor(real_size, message="real_size is: ")
        n = real_size / group_by
        n = K.print_tensor(n, message="n is: ")


        # from 1..n create a vector with [111 222 333 ... nnn] where the number of repeats is group_by
        idx = tf.range(n)
        idx = tf.reshape(idx, [-1, 1])  # Convert to a len(yp) x 1 matrix.
        idx = tf.tile(idx, [1, group_by])  # Create multiple columns.
        idx = tf.reshape(idx, [-1])  # Convert back to a vector.


        y_pred_byK = tf.segment_mean(y_pred, idx)
        y_true_byK = tf.segment_mean(y_true, idx)
        tmp = K.mean(K.square(y_pred_byK - y_true_byK), axis=-1)
        return tmp
        """
        # this can be a metric, but not a loss function - we would have to explode it again to its original size
        a = tf.size(y_pred_byK)
        b = tf.size(y_true_byK)

        a = tf.cast(a, tf.float32)
        b = tf.cast(b, tf.float32)

        a = K.print_tensor(a, message="a is: ")
        b = K.print_tensor(b, message="b is: ")

        tmp = y_pred_byK - y_true_byK
        tmp = K.print_tensor(tmp, message="tmp1 is: ")
        tmp = K.square(tmp)
        tmp = K.print_tensor(tmp, message="tmp2 is: ")
        tmp = K.mean(tmp, axis=-1)
        tmp = K.print_tensor(tmp, message="tmp3 is: ")

        tmp = tf.scalar_mul(1+0*a+0*b,tmp)
        """
        return tmp
    return f