TensorFlow – 如何将 rank-R 张量列表并行堆叠成一个 rank-(R+1) 张量

TensorFlow 是由 Google 设计的开源Python库，用于开发机器学习模型和深度学习神经网络。

TensorFlow 提供了内置方法，可以将 rank-R 张量列表并行堆叠到一个 rank-(R+1) 张量中。

Methods Used:

• parallel_stack: This method accepts a list of Tensors and returns a Tensor with all values stacked in parallel. This methods copies pieces of the input into the output as they become available.
• stack: This method accepts a list of Tensors, axis along which values should be stacked and returns a Tensor with all values stacked.

示例 1：此示例使用 stack 方法来堆叠张量。

Python3

# importing the library
import tensorflow as tf
  
# Initializing the Input
x = tf.constant([1, 2, 3])
y = tf.constant([4, 5, 6])
z = tf.constant([7, 8, 9])
  
# Printing the Input
print("x: ", x)
print("y: ", y)
print("z: ", z)
  
# Stacking Tensors
res = tf.stack(values =[x, y, z], axis = 0)
  
# Printing the resulting Tensor
print("Res: ", res )

输出：

x:  tf.Tensor([1 2 3], shape=(3, ), dtype=int32)
y:  tf.Tensor([4 5 6], shape=(3, ), dtype=int32)
z:  tf.Tensor([7 8 9], shape=(3, ), dtype=int32)
Res:  tf.Tensor(
[[1 2 3]
 [4 5 6]
 [7 8 9]], shape=(3, 3), dtype=int32)

示例 2：此示例使用 parallel_stack 方法来堆叠输入张量。

Python3

# importing the library
import tensorflow as tf
  
# Initializing the Input
x = tf.constant([1, 2, 3])
y = tf.constant([4, 5, 6])
z = tf.constant([7, 8, 9])
  
# Printing the Input
print("x: ", x)
print("y: ", y)
print("z: ", z)
  
# Stacking Tensors
res = tf.parallel_stack(values =[x, y, z])
  
# Printing the resulting Tensor
print("Res: ", res )

输出：

x:  tf.Tensor([1 2 3], shape=(3, ), dtype=int32)
y:  tf.Tensor([4 5 6], shape=(3, ), dtype=int32)
z:  tf.Tensor([7 8 9], shape=(3, ), dtype=int32)
Res:  tf.Tensor(
[[1 2 3]
 [4 5 6]
 [7 8 9]], shape=(3, 3), dtype=int32)