TensorFlow是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。
reset()用于清除磁带存储的所有信息。
用法：reset()
参数：它不接受任何参数。
返回：它不返回任何内容。
范例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
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)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)
范例2：
Python3
# 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)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)
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注：本文由纯净天空筛选整理自aman neekhara大神的英文原创作品 Python – tensorflow.GradientTape.reset()。非经特殊声明，原始代码版权归原作者所有，本译文未经允许或授权，请勿转载或复制。