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.
Syntax: tensorflow.gradients( ys, xs, grad_ys, name, gate_gradients, aggregation_method, stop_gradients, unconnected_gradients)
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:
res: [<tf.Tensor 'gradients/AddN:0' shape=(1, 2) dtype=float32>]
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:
res: [<tf.Tensor 'gradients/mul_grad/Mul_1:0' shape=(1, 2) dtype=float32>]
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