Dear colleagues!
Size of np.float16(1) is 26
Size of np.float64(1) is 32
32 / 26 = 1.23
Since memory is limited I have a question after this code:
import numpy as np
import sys
a1 = np.ones(1, dtype='float16')
b1 = np.ones(1, dtype='float64')
div_1 = sys.getsizeof(b1) / sys.getsizeof(a1)
# div_1 = 1.06
a2 = np.ones(10, dtype='float16')
b2 = np.ones(10, dtype='float64')
div_2 = sys.getsizeof(b2) / sys.getsizeof(a2)
# div_2 = 1.51
a3 = np.ones(100, dtype='float16')
b3 = np.ones(100, dtype='float64')
div_3 = sys.getsizeof(b3) / sys.getsizeof(a3)
# div_3 = 3.0
Size of np.float64 numpy arrays is four times more than for np.float16.
Is it possible to minimize the difference close to 1.23?
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