The real difference between float32 and float64
a = np.array([0.123456789121212,2,3], dtype=np.float16)print("16bit: ", a[0])a = np.array([0.123456789121212,2,3], dtype=np.float32)print("32bit: ", a[0])b = np.array([0.123456789121212121212,2,3], dtype=np.float64)print("64bit: ", b[0])- 16bit: 0.1235
- 32bit: 0.12345679
- 64bit: 0.12345678912121212
float32 is a 32 bit number - float64 uses 64 bits.
That means that float64’s take up twice as much memory - and doing operations on them may be a lot slower in some machine architectures.
However, float64’s can represent numbers much more accurately than 32 bit floats.
They also allow much larger numbers to be stored.
For your Python-Numpy project I'm sure you know the input variables and their nature.
To make a decision we as programmers need to ask ourselves
- What kind of precision does my output need?
- Is speed not an issue at all?
- what precision is needed in parts per million?
A naive example would be if I store weather data of my city as[12.3, 14.5, 11.1, 9.9, 12.2, 8.2]
Next day Predicted Output could be of 11.5 or 11.5164374
do your think storing float 32 or float 64 would be necessary?