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I think group CNN is close. It has rotation and symmetry


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The number of parameters in a convolutional layer can be calculated using the following formula: (filter_width * filter_height * n_filters_in) + 1) * n_filters_out. Filling in the numbers for your example we get ((1 * 1 * 32) + 1) * 32 = 33 * 32 = 1056, which aligns with the number you get from model.summary(). Of these 1056 parameters, 1024 (1 * 1 * 32 * 32)...


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