极客教程Python – tensorflow.GradientTape.reset()
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。
reset()是用来清除所有被磁带存储的信息。
语法: reset()
参数：它不接受任何参数。
返回：它没有返回。
示例 1:
# 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
y+=x*x
# Computing gradient without reset
res = gfg.gradient(y, x)
# Printing result
print("res(y = x*x*x + x*x): ",res)
# Using GradientTape
with tf.GradientTape() as gfg:
gfg.watch(x)
y = x * x * x
# Resetting the Tape
gfg.reset()
gfg.watch(x)
y+=x*x
# Computing gradient with reset
res = gfg.gradient(y, x)
# Printing result
print("res(y = x*x): ",res)
Python
输出:
res(y = x*x*x + x*x): tf.Tensor(56.0, shape=(), dtype=float32)
res(y = x*x): tf.Tensor(8.0, shape=(), dtype=float32)
Python
示例 2:
# Importing the library
import tensorflow as tf
x = tf.constant(3.0)
# Using GradientTape
with tf.GradientTape() as gfg:
gfg.watch(x)
y = x * x
y+=x*x
# Computing gradient without reset
res = gfg.gradient(y, x)
# Printing result
print("res(y = x*x + x*x): ",res)
# Using GradientTape
with tf.GradientTape() as gfg:
gfg.watch(x)
y = x * x
# Resetting the Tape
gfg.reset()
gfg.watch(x)
y+=x
# Computing gradient with reset
res = gfg.gradient(y, x)
# Printing result
print("res(y = x): ",res)
Python
输出:
res(y = x*x + x*x): tf.Tensor(12.0, shape=(), dtype=float32)
res(y = x): tf.Tensor(1.0, shape=(), dtype=float32)
Python