Python tensorflow.identity_n()用法及代码示例

TensorFlow是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。

identity_n()用于获取具有与输入Tensor相同的形状和内容的Tensor列表。

用法：tensorflow.identity_n( input, name)

范例1：

# Importing the library 
import tensorflow as tf 
  
# Initializing the input 
data = tf.constant([[1, 2, 3], [3, 4, 5], [5, 6, 7]]) 
  
# Printing the input 
print('data:', data) 
  
# Calculating result 
res = tf.identity_n(data) 
  
# Printing the result 
print('res:', res)

输出：

data: tf.Tensor(
[[1 2 3]
 [3 4 5]
 [5 6 7]], shape=(3, 3), dtype=int32)
res: [<tf.Tensor:shape=(3, ), dtype=int32, numpy=array([1, 2, 3], dtype=int32)>,
     <tf.Tensor:shape=(3, ), dtype=int32, numpy=array([3, 4, 5], dtype=int32)>, 
     <tf.Tensor:shape=(3, ), dtype=int32, numpy=array([5, 6, 7], dtype=int32)>]

范例2：

# Importing the library 
import tensorflow as tf 
  
# Initializing the input 
data = tf.constant([1, 2, 3]) 
  
# Printing the input 
print('data:', data) 
  
# Calculating result 
res = tf.identity_n(data) 
  
# Printing the result 
print('res:', res)

输出：

data: tf.Tensor([1 2 3], shape=(3, ), dtype=int32)
res: [<tf.Tensor:shape=(), dtype=int32, numpy=1>,
     <tf.Tensor:shape=(), dtype=int32, numpy=2>, 
     <tf.Tensor:shape=(), dtype=int32, numpy=3>]