1，TensorFlow-cpu优化
当你使用cpu版TensorFlow时（比如pip安装），你可能会遇到警告，说你cpu支持AVX/AVX2指令集，那么在以下网址下载对应版本。
https://github.com/fo40225/tensorflow-windows-wheel
具体使用github上有说明。
根据测试，安装AVX指令集后相应数学计算（矩阵乘法、分解等）速度是原来的3倍左右。
2，numpy优化
一般现在的numpy默认都是支持openblas的，但是我发现支持mkl的更快。下载地址
https://www.lfd.uci.edu/~gohlke/pythonlibs/#numpy
查看numpy支持的优化：np.__config__.show()
以下附上测试代码及结果，你可以在自己电脑上测试。
'''default numpy(openblas):---------
Dotted two 4096x4096 matrices in 1.99 s.
Dotted two vectors of length 524288 in 0.40 ms.
SVD of a 2048x1024 matrix in 1.75 s.
Cholesky decomposition of a 2048x2048 matrix in 0.21 s.
Eigendecomposition of a 2048x2048 matrix in 10.31 s.
------------------------------------------------------numpy+mkl:----------
Dotted two 4096x4096 matrices in 1.56 s.
Dotted two vectors of length 524288 in 0.33 ms.
SVD of a 2048x1024 matrix in 1.07 s.
Cholesky decomposition of a 2048x2048 matrix in 0.24 s.
Eigendecomposition of a 2048x2048 matrix in 6.94 s.
'''
import numpy as np
from time import time
# Let's take the randomness out of random numbers (for reproducibility)
np.random.seed(0)
size = 4096
A, B = np.random.random((size, size)), np.random.random((size, size))
C, D = np.random.random((size * 128, )), np.random.random((size * 128, ))
E = np.random.random((int(size / 2), int(size / 4)))
F = np.random.random((int(size / 2), int(size / 2)))
F = np.dot(F, F.T)
G = np.random.random((int(size / 2), int(size / 2)))
# Matrix multiplication
N = 20
t = time()
for i in range(N):
np.dot(A, B)
delta = time() - t
print('Dotted two %dx%d matrices in %0.2f s.' % (size, size, delta / N))
del A, B
# Vector multiplication
N = 5000
t = time()
for i in range(N):
np.dot(C, D)
delta = time() - t
print('Dotted two vectors of length %d in %0.2f ms.' %
(size * 128, 1e3 * delta / N))
del C, D
# Singular Value Decomposition (SVD)
N = 3
t = time()
for i in range(N):
np.linalg.svd(E, full_matrices=False)
delta = time() - t
print("SVD of a %dx%d matrix in %0.2f s." % (size / 2, size / 4, delta / N))
del E
# Cholesky Decomposition
N = 3
t = time()
for i in range(N):
np.linalg.cholesky(F)
delta = time() - t
print("Cholesky decomposition of a %dx%d matrix in %0.2f s." %
(size / 2, size / 2, delta / N))
# Eigendecomposition
t = time()
for i in range(N):
np.linalg.eig(G)
delta = time() - t
print("Eigendecomposition of a %dx%d matrix in %0.2f s." %
(size / 2, size / 2, delta / N))