• Last Updated : 11 Oct, 2021

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

gradients() is used to get symbolic derivatives of sum of ys w.r.t. x in xs. It doesn’t work when eager execution is enabled.

Parameters:

• ys: It is a Tensor or list of Tensors that need to be differentiated.
• xs: It is a Tensor or list of Tensors which is used for differentiation.
• grad_ys(optional): It is a Tensor or list of Tensors that is used to compute gradients for y.
• name(optional): It is used group gradient operation together. It’s default value is gradients.
• gate_gradients(optional): It used to avoid race condition. If true , it will add a tuple around the gradients returned for an operations.
• aggregation_method(optional): It’s value is a constant defined in AggregationMethod class.
• stop_gradients(optional): It’s a Tensor or list of tensors not to differentiate through.
• unconnected_gradients(optional): It specifies the gradient value returned when the given input tensors are unconnected. Accepted values are constants defined in the UnconnectedGradients class.

Returns: A list of Tensor of length len(xs) where each tensor is the sum(dy/dx) for y in ys and for x in xs.

Example 1:

Python3

 # Importing the libraryimport tensorflow as tf # Defining function@tf.functiondef gfg():  a = tf.ones([1, 2])  b = 5*a   # Calculating gradient  g1 = tf.gradients([b+a], [a])   # Printing result  print("res: ",g1) # Calling the  functiongfg()

Output:

Example 2:

Python3

 # Importing the libraryimport tensorflow as tf # Defining function@tf.functiondef gfg():  a = tf.ones([1, 2])  b = 5*a   # Calculating gradient  g1 = tf.gradients([b], [a])   # Printing result  print("res: ",g1) # Calling the  functiongfg()

Output: