import tensorflow as tf 

w1 = tf.Variable([[1,2]]) 
w2 = tf.Variable([[3,4]]) 

res = tf.matmul(w1, [[2],[1]]) 

grads = tf.gradients(res,[w1]) 

with tf.Session() as sess: 
 tf.global_variables_initializer().run()
 print sess.run(res)
 print sess.run(grads) 

[[4]]
[array([[2, 1]], dtype=int32)]

import tensorflow as tf 
def gradient_clip(gradients, max_gradient_norm):
 """Clipping gradients of a model."""
 clipped_gradients, gradient_norm = tf.clip_by_global_norm(
   gradients, max_gradient_norm)
 gradient_norm_summary = [tf.summary.scalar("grad_norm", gradient_norm)]
 gradient_norm_summary.append(
  tf.summary.scalar("clipped_gradient", tf.global_norm(clipped_gradients)))

 return clipped_gradients
w1 = tf.Variable([[3.0,2.0]]) 
# w2 = tf.Variable([[3,4]]) 
params = tf.trainable_variables()
res = tf.matmul(w1, [[3.0],[1.]]) 
opt = tf.train.GradientDescentOptimizer(1.0)
grads = tf.gradients(res,[w1]) 
clipped_gradients = gradient_clip(grads,2.0)
global_step = tf.Variable(0, name='global_step', trainable=False)
#update = opt.apply_gradients(zip(clipped_gradients,params), global_step=global_step)
with tf.Session() as sess: 
 tf.global_variables_initializer().run()
 print sess.run(res)
 print sess.run(grads) 
 print sess.run(clipped_gradients)