float32 和 float64 的真正区别 [英] The real difference between float32 and float64

问题描述

我想了解 float16 和 float32 在结果精度方面的实际区别.例如，Numpy 允许您选择所需的数据类型范围(np.float16, np.float32, np.float64).我担心的是，如果我决定使用 float 16 来保留内存并避免可能的溢出，例如与 float32 相比，这会造成最终结果的损失吗?

I want to understand the actual difference between float16 and float32 in terms of the result precision. For instance, Numpy allows you to choose the range of the datatype you want (np.float16, np.float32, np.float64). My concern is that if I decide to go with float 16 to reserve memory and avoid possible overflow, would that create a loss of the final results comparing with float32 for instance?

谢谢

推荐答案

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])

• 16 位:0.1235
• 32 位:0.12345679
• 64 位:0.12345678912121212

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