Python – tensorflow.GradientTape()

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

GradientTape() is used to record operations for automatic differentiation.

Syntax: tensorflow.GradientTape( persistent, watch_accessed_variables)

Parameters:

• persistent(optional): It can either be True or False with default value False. It defines whether persistent gradient tape is created or not.
• watch_accessed_variables:  It is a boolean defining whether the tape will automatically watch any (trainable) variables accessed while the tape is active or not.

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

# Computing gradient
res  = gfg.gradient(y, x)

# Printing result
print("res: ",res)

Output:

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

Example 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)

Output:

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