Python – tensorflow.GradientTape.reset()

TensorFlow is open-source Python library designed by Google to develop Machine Learning models and deep learning  neural networks. 

reset() is used to clear all information that is stored by the Tape.

Syntax: reset()

Parameters: It doesn’t accept any parameters.

Returns: It returns none.

Example 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)

Output:

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)

Example 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)

Output:

res(y = x*x + x*x):  tf.Tensor(12.0, shape=(), dtype=float32)
res(y = x):  tf.Tensor(1.0, shape=(), dtype=float32)