Related Articles
• Last Updated : 10 Jul, 2020

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.

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 libraryimport tensorflow as tf  x = tf.constant(4.0)  # Using GradientTapewith tf.GradientTape() as gfg:  gfg.watch(x)  y = x * x * x  # Computing gradientres  = gfg.gradient(y, x)  # Printing resultprint("res: ",res)

Output:

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

Example 2:

## Python3

 # Importing the libraryimport tensorflow as tf  x = tf.constant(4.0)  # Using GradientTapewith 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 gradientsecond_order  = gfg.gradient(first_order, x)   # Printing resultprint("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)

Attention geek! Strengthen your foundations with the Python Programming Foundation Course and learn the basics.

To begin with, your interview preparations Enhance your Data Structures concepts with the Python DS Course.

My Personal Notes arrow_drop_up