Python – tensorflow.clip_by_norm()
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.
clip_by_norm()用于将张量值裁剪为最大 L2 范数。
Syntax: tensorflow.clip_by_norm(t, clip_norm, axes, name)
Parameters:
- t: It is the input tensor that need to be clipped.
- clip_norm: It is 0-D scalar tensor which defines the maximum clipping value.
- axes(optional): It’s 1-D vector tensor which defines the dimension to be used for calculating L2norm. If none is provided all dimensions will be used.
- name(optional): It defines the name for the operation.
Returns:
It returns a Tensor.
示例 1:
Python3
# Importing the library
import tensorflow as tf
# Initializing the input tensor
t = tf.constant([1, 2, 3, 4], dtype = tf.float64)
clip_norm = .8
# Printing the input tensor
print('t: ', t)
print('clip_norm: ', clip_norm)
# Calculating tangent
res = tf.clip_by_norm(t, clip_norm)
# Printing the result
print('Result: ', res)
Python3
# Importing the library
import tensorflow as tf
# Initializing the input tensor
t = tf.constant([1, 2, 3, 4], dtype = tf.float64)
clip_norm = 5.2
# Printing the input tensor
print('t: ', t)
print('clip_norm: ', clip_norm)
# Calculating tangent
res = tf.clip_by_norm(t, clip_norm)
# Printing the result
print('Result: ', res)
输出:
t: tf.Tensor([1. 2. 3. 4.], shape=(4, ), dtype=float64)
clip_norm: 0.8
Result: tf.Tensor([0.14605935 0.2921187 0.43817805 0.58423739], shape=(4, ), dtype=float64)
示例 2:
Python3
# Importing the library
import tensorflow as tf
# Initializing the input tensor
t = tf.constant([1, 2, 3, 4], dtype = tf.float64)
clip_norm = 5.2
# Printing the input tensor
print('t: ', t)
print('clip_norm: ', clip_norm)
# Calculating tangent
res = tf.clip_by_norm(t, clip_norm)
# Printing the result
print('Result: ', res)
输出:
t: tf.Tensor([1. 2. 3. 4.], shape=(4, ), dtype=float64)
clip_norm: 5.2
Result: tf.Tensor([0.94938577 1.89877153 2.8481573 3.79754307], shape=(4, ), dtype=float64)