NumPy arrays and TensorFlow Tensors的区别和联系

最新推荐文章于 2022-08-11 15:51:18 发布

转载最新推荐文章于 2022-08-11 15:51:18 发布 · 693 阅读

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原文链接：http://www.cnblogs.com/augustone/p/10506893.html

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#python #人工智能

本文详细介绍了Tensor的主要特点，包括其在加速器内存如GPU、TPU上的支持以及不可变性。同时，深入探讨了Tensor与NumPy数组之间的双向转换机制，以及这种转换可能涉及的内存复制成本。此外，提供了在TensorFlow中检测和选择GPU的方法，以及如何显式指定运行设备的代码示例。

1，tensor的特点

Tensors can be backed by accelerator memory (like GPU, TPU).
Tensors are immutable

2，双向转换

TensorFlow operations automatically convert NumPy ndarrays to Tensors.
NumPy operations automatically convert Tensors to NumPy ndarrays

3，转换的代价

Tensors can be explicitly converted to NumPy ndarrays by invoking the .numpy() method on them. These conversions are typically cheap as the array and Tensor share the underlying memory representation if possible. However, sharing the underlying representation isn't always possible since the Tensor may be hosted in GPU memory while NumPy arrays are always backed by host memory, and the conversion will thus involve a copy from GPU to host memory.

4，使用tensor时如何测定和选择gpu

x = tf.random_uniform([3, 3])

print("Is there a GPU available: "),
print(tf.test.is_gpu_available())

print("Is the Tensor on GPU #0: "),
print(x.device.endswith('GPU:0'))

print(tf.test.is_built_with_cuda())

5，显式指定运行的xpu

import time

def time_matmul(x):
start = time.time()
for loop in range(10):
tf.matmul(x, x)

result = time.time()-start

print("10 loops: {:0.2f}ms".format(1000*result))

# Force execution on CPU
print("On CPU:")
with tf.device("CPU:0"):
x = tf.random_uniform([900, 900])
assert x.device.endswith("CPU:0")
time_matmul(x)

# Force execution on GPU #0 if available
if tf.test.is_gpu_available():
with tf.device("GPU:0"): # Or GPU:1 for the 2nd GPU, GPU:2 for the 3rd etc.
x = tf.random_uniform([1000, 1000])
assert x.device.endswith("GPU:0")
time_matmul(x)

转载于:https://www.cnblogs.com/augustone/p/10506893.html