TensorFlow is open-source Python library designed by Google to develop Machine Learning models and deep learning neural networks.
gradient() is used to computes the gradient using operations recorded in context of this tape.
Syntax: gradient(target, sources, output_gradients, unconnected_gradients)
Parameters:
- target: It is Tensor or list of Tensor to be differentiated.
- sources: It is Tensor or list of Tensor. Target values are differentiated against the source.
- output_gradients: It is a list of gradients with default value None.
- unconnected_gradients: It’s value can be either none or zero with default value none.
Returns: It returns a list or nested structure of Tensor.
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)
