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我使用Tensorflow進行一些非DL計算，並且我遇到了一個我不明白的行爲。我本身測試方陣的乘法：tf.matmul(a,a)：Tensorflow乘法的常數性能比tf.random低
- 時tf.constant
- 創建矩陣在矩陣是在每次運行隨機初始化
我的期望是第一種情況應該有一些開銷來傳輸初始數據，100 MB（使用float32的5000x5000矩陣），但是由於每次運行時進行隨機初始化，第二種情況的執行速度應稍慢。
但是，我看到的是，即使在同一會話中連續運行，常數的乘法速度也要慢很多。
代碼
import tensorflow as tf
import numpy as np
from timeit import timeit
import os
os.environ["TF_CPP_MIN_LOG_LEVEL"]="2" # nospam
SIZE = 5000
NUM_RUNS = 10
a = np.random.random((SIZE, SIZE))
_const_a = tf.constant(a, dtype=tf.float32, name="Const_A")
_mul_const_a = tf.matmul(_const_a, _const_a, name="Mul_Const")
_random_a = tf.random_uniform((SIZE, SIZE), dtype=tf.float32, name="Random_A")
_mul_random_a = tf.matmul(_random_a, _random_a, name="Mul_Random")
with tf.Session(config=tf.ConfigProto(log_device_placement=True)) as s:
# Run once to make sure everything is initialised
s.run((_const_a, _mul_const_a, _random_a, _mul_random_a))
# timeit
print("TF with const\t", timeit(lambda: s.run((_mul_const_a.op)), number=NUM_RUNS))
print("TF with random\t", timeit(lambda: s.run((_mul_random_a.op)), number=NUM_RUNS))
輸出
Device mapping:
/job:localhost/replica:0/task:0/device:GPU:0 -> device: 0, name: GeForce GTX 1070, pci bus id: 0000:01:00.0, compute capability: 6.1
Random_A/sub: (Sub): /job:localhost/replica:0/task:0/device:GPU:0
Random_A/RandomUniform: (RandomUniform): /job:localhost/replica:0/task:0/device:GPU:0
Random_A/mul: (Mul): /job:localhost/replica:0/task:0/device:GPU:0
Random_A: (Add): /job:localhost/replica:0/task:0/device:GPU:0
Mul_Random: (MatMul): /job:localhost/replica:0/task:0/device:GPU:0
Mul_Const: (MatMul): /job:localhost/replica:0/task:0/device:GPU:0
Random_A/max: (Const): /job:localhost/replica:0/task:0/device:GPU:0
Random_A/min: (Const): /job:localhost/replica:0/task:0/device:GPU:0
Random_A/shape: (Const): /job:localhost/replica:0/task:0/device:GPU:0
Const_A: (Const): /job:localhost/replica:0/task:0/device:GPU:0
TF with const 2.9953213009994215
TF with random 0.513827863998813