python–tensorlow。梯度胶带()
哎哎哎:# t0]https://www . geeksforgeeks . org/python-tensorlow-gradienttape/
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。
GradientTape() 用于记录自动微分的操作。
语法:张量流。GradientTape(持久，watch _ accessed _ variables)
参数:
- 持久(可选):可以是真，也可以是假，默认值为假。它定义是否创建持久渐变带。
- watch _ access _ variables:它是一个布尔值，定义了磁带是否会自动观察磁带活动时访问的任何(可训练的)变量。
例 1:
Python 3
# 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)
例 2:
Python 3
# 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)
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