Autodifferentiation of Rosenbrock function using Tensorflow

rosen-tf.py

x = tf.Variable([1., 3.], tf.float32)
# Rosenbrock function
y = tf.add(tf.pow(tf.subtract(1.0, x[0]), 2.0), 
           tf.multiply(100.0, tf.pow(tf.subtract(x[1],tf.pow(x[0], 2.0)), 2.0)), 'y')

dx = tf.gradients(y, x)[0]
val = np.array([1, 3], dtype=np.float32)

with tf.Session() as sess:
    sess.run(tf.global_variables_initializer())
    print (sess.run(y, {x: val}), sess.run(dx, {x: val}))