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Python – tensorflow.GradientTape.batch_jacobian()

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

batch_jacobian() is used to compute and stack the per example jacobian.

Syntax: batch_jacobian( target, source, unconnected_gradients, parallel_iterations, experimental_use_pfor )

Parameters:

  • target: It is a Tensor having minimum rank 2.
  • source: It is a Tensor having minimum rank 2.
  • unconnected_gradients(optional): It’s value can either be zero or None. Default value is None.
  • parallel_iterations(optional): It is used to control parallel iterations and memory usage.
  • experimental_use_pfor(optional): It is a boolean with default value True. It uses pfor to calculate jacobian when set to true otherwise tf.while_loop is used.

Returns: It returns a Tensor. 

Example 1:

Python3

# Importing the library
import tensorflow as tf
  
x = tf.constant([[4, 2],[1, 3]], dtype=tf.dtypes.float32)
  
# Using GradientTape
with tf.GradientTape() as gfg:
  gfg.watch(x)
  y = x * x * x
  
# Computing jacobian
res  = gfg.batch_jacobian(y, x) 
  
# Printing result
print("res: ",res)

                    

Output:

res:  tf.Tensor(
[[[48.  0.]
  [ 0. 12.]]

 [[ 3.  0.]
  [ 0. 27.]]], shape=(2, 2, 2), dtype=float32)

Example 2:

Python3

# Importing the library
import tensorflow as tf
  
x = tf.constant([[4, 2],[1, 3]], dtype=tf.dtypes.float32)
  
# Using GradientTape
with tf.GradientTape() as gfg:
  gfg.watch(x)
  
  # Using nested GradientTape for calculating higher order jacobian
  with tf.GradientTape() as gg:
    gg.watch(x)
    y = x * x * x
  # Computing first order jacobian
  first_order = gg.batch_jacobian(y, x)
  
# Computing Second order jacobian
second_order  = gfg.batch_jacobian(first_order, x) 
  
# Printing result
print("first_order: ",first_order)
print("second_order: ",second_order)

                    

Output:

first_order:  tf.Tensor(
[[[48.  0.]
  [ 0. 12.]]

 [[ 3.  0.]
  [ 0. 27.]]], shape=(2, 2, 2), dtype=float32)
second_order:  tf.Tensor(
[[[[24.  0.]
   [ 0.  0.]]

  [[ 0.  0.]
   [ 0. 12.]]]


 [[[ 6.  0.]
   [ 0.  0.]]

  [[ 0.  0.]
   [ 0. 18.]]]], shape=(2, 2, 2, 2), dtype=float32)




Last Updated : 10 Jul, 2020
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