Python – tensorflow.GradientTape.gradient()

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。

gradient()用于使用记录在该磁带上下文中的操作来计算梯度。

Syntax: gradient(target, sources, output_gradients, unconnected_gradients)

Parameters:

• target: It is Tensor or list of Tensor to be differentiated.
• sources: It is Tensor or list of Tensor. Target values are differentiated against the source.
• output_gradients: It is a list of gradients with default value None.
• unconnected_gradients: It’s value can be either none or zero with default value none.

Returns: It returns a list or nested structure of Tensor.

示例 1：

Python3

# Importing the library
import tensorflow as tf
  
x = tf.constant(4.0)
  
# Using GradientTape
with tf.GradientTape() as gfg:
  gfg.watch(x)
  y = x * x * x
  
# Computing gradient
res  = gfg.gradient(y, x) 
  
# Printing result
print("res: ",res)

输出：

res:  tf.Tensor(48.0, shape=(), dtype=float32)

示例 2：

Python3

# Importing the library
import tensorflow as tf
  
x = tf.constant(4.0)
  
# Using GradientTape
with tf.GradientTape() as gfg:
  gfg.watch(x)
  
  # Using nested GradientTape for 
  # calculating higher order derivative
  with tf.GradientTape() as gg:
    gg.watch(x)
    y = x * x * x
  
  # Computing first order gradient
  first_order = gg.gradient(y, x)
  
# Computing Second order gradient
second_order  = gfg.gradient(first_order, x) 
  
# Printing result
print("first_order: ",first_order)
print("second_order: ",second_order)

输出：

first_order:  tf.Tensor(48.0, shape=(), dtype=float32)
second_order:  tf.Tensor(24.0, shape=(), dtype=float32)