Python – tensorflow.gather()
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。
collect()用于根据提供的索引对输入张量进行切片。
Syntax: tensorflow.gather( params, indices, validate_indices, axis, batch_dims, name)
Parameters:
- params: It is a Tensor with rank greater than or equal to axis+1.
- indices: It is a Tensor of dtype int32 or int64. It’s value should be in range [0, params.shape[axis]).
- axis: It is a Tensor of dtype int32 or int64. It defines the axis from which indices should be gathered. Default value is 0 and it must be greater than of equal to batch_dims.
- batch_dims: It is an integer representing the number o batch dimension. It must be less than or equal to rank(indices).
- name: It defines the name for the operation.
Returns:
It returns a Tensor having same dtype as param.
示例 1:
Python3
# Importing the library
import tensorflow as tf
# Initializing the input
data = tf.constant([1, 2, 3, 4, 5, 6])
indices = tf.constant([0, 1, 2, 1])
# Printing the input
print('data: ',data)
print('indices: ',indices)
# Calculating result
res = tf.gather(data, indices)
# Printing the result
print('res: ',res)
Python3
# Importing the library
import tensorflow as tf
# Initializing the input
data = tf.constant([[1, 2], [3, 4], [5, 6]])
indices = tf.constant([2, 0, 1])
# Printing the input
print('data: ',data)
print('indices: ',indices)
# Calculating result
res = tf.gather(data, indices)
# Printing the result
print('res: ',res)
输出:
data: tf.Tensor([1 2 3 4 5 6], shape=(6,), dtype=int32)
indices: tf.Tensor([0 1 2 1], shape=(4,), dtype=int32)
res: tf.Tensor([1 2 3 2], shape=(4,), dtype=int32)
示例 2:
Python3
# Importing the library
import tensorflow as tf
# Initializing the input
data = tf.constant([[1, 2], [3, 4], [5, 6]])
indices = tf.constant([2, 0, 1])
# Printing the input
print('data: ',data)
print('indices: ',indices)
# Calculating result
res = tf.gather(data, indices)
# Printing the result
print('res: ',res)
输出:
data: tf.Tensor(
[[1 2]
[3 4]
[5 6]], shape=(3, 2), dtype=int32)
indices: tf.Tensor([2 0 1], shape=(3,), dtype=int32)
res: tf.Tensor(
[[5 6]
[1 2]
[3 4]], shape=(3, 2), dtype=int32)