TensorFlow tf.gradients() function can return the gradient of a tensor. How to understand the result of it? We will use some examples to help tensorflow beginners to understand and use it in this tutorial.
Syntax
tf.gradients(
ys,
xs,
grad_ys=None,
name='gradients',
colocate_gradients_with_ops=False,
gate_gradients=False,
aggregation_method=None,
stop_gradients=None
)
Constructs symbolic derivatives of sum of ys w.r.t. x in xs
where ys and xs are each a tensor or a list of tensors
How to understand the result of tf.gradients()?
Suppose
y = [y1, y2], x = [x1, x2, x3]
y = f(x)
To compute the gradient of x based on y, we can do like this:
g = tf.gradients(y, x)
or
g = tf.gradients([y1, y2], [x1, x2, x3])
As to g = [g1, g2, g3]
where
Here is an example:
import tensorflow as tf
import numpy as np
x = tf.Variable(np.array([[1, 1.2, 1.3],[1, 1.5, 1.2]]), dtype = tf.float32)
z = tf.Variable(np.array([[1, 1],[1.2, 1.2], [2, 1.7]]), dtype = tf.float32)
y = tf.matmul(x, z)
g = tf.gradients(y, x)
init = tf.global_variables_initializer()
init_local = tf.local_variables_initializer()
with tf.Session() as sess:
sess.run([init, init_local])
print(sess.run([g]))
Run this code, you will get result:
[[array([[2. , 2.4, 3.7],
[2. , 2.4, 3.7]], dtype=float32)]]