Python – tensorflow.math.segment\_mean()

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

segment\_mean()用于查找张量中各段元素的平均值。

语法:
tensorflow.math.segment_mean( data, segment_ids, name )

参数 :

• data: 它是一个张量。允许的 dtypes 是float32, float64, int32, uint8, int16, int8, int64, bfloat16, uint16, half, uint32, uint64。
• segment\_ids : 它是带有排序值的一维张量。它的大小应该等于数据的第一维的大小。允许的d类型是int32和int64。
• name（可选）:它定义了该操作的名称。

返回：它返回一个 dtype 的张量为x。

示例 1:

# importing the library
import tensorflow as tf

# Initializing the input tensor
data = tf.constant([1, 2, 3])
segment_ids = tf.constant([2, 2, 2])

# Printing the input tensor
print('data: ', data)
print('segment_ids: ', segment_ids)

# Calculating result
res = tf.math.segment_mean(data, segment_ids)

# Printing the result
print('Result: ', res)

输出:

data: tf.Tensor([1 2 3], shape=(3, ), dtype=int32)
segment_ids: tf.Tensor([2 2 2], shape=(3, ), dtype=int32)
Result: tf.Tensor([0 0 2], shape=(3, ), dtype=int32)

示例 2:

# importing the library
import tensorflow as tf

# Initializing the input tensor
data = tf.constant([[1, 2, 3], [4, 5, 6], [7, 8, 9]], dtype = float64)
segment_ids = tf.constant([0, 0, 2])

# Printing the input tensor
print('data: ', data)
print('segment_ids: ', segment_ids)

# Calculating result
res = tf.math.segment_mean(data, segment_ids)

# Printing the result
print('Result: ', res)

输出:

data: tf.Tensor(
[[1. 2. 3.]
[4. 5. 6.]
[7. 8. 9.]], shape=(3, 3), dtype=float64)
segment_ids: tf.Tensor([0 0 2], shape=(3, ), dtype=int32)
Result: tf.Tensor(
[[2.5 3.5 4.5]
[0. 0. 0. ]
[7. 8. 9. ]], shape=(3, 3), dtype=float64)