NumPy和Tensor之间得转换

最新推荐文章于 2023-12-12 20:35:37 发布

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本文详细介绍了如何在TensorFlow的tf.Tensor和NumPy的ndarray之间进行数据转换。文章指出，这两种数据结构可以相互自动转换，且通常共享底层内存表示，这使得转换过程非常高效。然而，当数据位于不同的内存区域，如GPU和主机内存时，可能需要额外的复制操作。

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Converting between a TensorFlow tf.Tensors and a NumPy ndarray is easy:

1.TensorFlow operations automatically convert NumPy ndarrays to Tensors.
2，NumPy operations automatically convert Tensors to NumPy ndarrays.

Tensors are explicitly converted to NumPy ndarrays using their .numpy() method. These conversions are typically cheap since the array and tf.Tensor share the underlying memory representation, if possible. However, sharing the underlying representation isn’t always possible since the tf.Tensor may be hosted in GPU memory while NumPy arrays are always backed by host memory, and the conversion involves a copy from GPU to host memory.

import numpy as np

ndarray = np.ones([3, 3])

print("TensorFlow operations convert numpy arrays to Tensors automatically")
tensor = tf.multiply(ndarray, 42)
print(tensor)


print("And NumPy operations convert Tensors to numpy arrays automatically")
print(np.add(tensor, 1))

print("The .numpy() method explicitly converts a Tensor to a numpy array")
print(tensor.numpy())