NumPy operations profiling.

broadcast.py

import timeit
import numpy as np


def broadcast():
    a = np.random.random(256)
    b = np.random.random((2048, 256))
    return timeit.timeit(lambda: a - b, number=1000)


def tile():
    a = np.random.random(256)
    b = np.random.random((2048, 256))
    n = b.shape[0]
    return timeit.timeit(lambda: np.tile(a, (n, 1)) - b, number=1000)


# 0.3977937699999998
broadcast()

# 0.575944809000001
tile()

relu.py

import time
import numpy as np

def profile(f, n=2000):
    arr = np.random.random((1000, 100)) - 0.5
    start = time.time()
    for _ in range(n):
        f(arr)
    elapsed = time.time() - start
    return elapsed

def maximum(a):
    a = np.maximum(0, a)
    return a

def indexing(a):
    a[a > 0] = 0
    return a

def where(a):
    a = np.where(a > 0, a, 0)
    return a

print(profile(maximum))  # 0.2757070064544678
print(profile(indexing)) # 0.06246805191040039
print(profile(where))    # 0.8790371417999268