I’m facing some (RAM) memory issues to train a neural network.
I have an input array consisting of grayscale images encoded as the type numpy.uint8 (therefore the whole range 0-255 can be covered). When feeding the data to the network, I’m supposed to normalize the values into the 0-1 range, making them now floating points (numpy.float64 or 32). However, this conversion makes my data 8x(or 4x) bigger, which my RAM memory can’t handle.
Would there be a way I could overcome this issue?