I'm trying to convert a dataset of dimensions (32, 32, 3, 10000) dimension dataset to a grayscale dataset, where I would have (32, 32, 1, 10000) dimensions, but I need to have that 1 channel because I will input this to a Neural Network. I tried using numpy.average, but the shape becomes (32, 32, 10000) which the TensorFlow unit is not taking as an input. I even tried to manually average it, but it had the same result. Could you guys help me with this?
It is possible to add that extra dimension you need after getting (32, 32, 1000) as shape.
You could try np.expand_dims with the axis parameter to define where you want that extra "1" to appear.