tensorflow_demo.py

import tensorflow as tf

node1 = tf.constant(3.0, tf.float32)
node2 = tf.constant(4.0) # also tf.float32 implicitly

with tf.Session() as sess:
    W = tf.Variable([.3], tf.float32, name='W')
    b = tf.Variable([-.3], tf.float32, name='b')
    x = tf.placeholder(tf.float32, name='x')
    with tf.name_scope("LinearModel"):
       y = W * x + b
    f = tf.sin(y,name='f')

    init = tf.global_variables_initializer()
    sess.run(init)

    print sess.run(y, {x:[1,2,3,4]})

    with tf.name_scope("Updates"):
        fixW = tf.assign(W, [-1.])
        fixb = tf.assign(b, [1.])
    sess.run([fixW, fixb])
    
    print "After transform"
    print sess.run(y, {x:[1,2,3,4]})
    

    grad_f = tf.gradients(f,x,name='dfdx')
    grad_y = tf.gradients(y,x,name='dydx')

    print "Gradients"
    ret =  sess.run([grad_f,grad_y], {x:[1,2,3,4]})
    print "df/dx: ",ret[0][0]
    print "dy/dx: ",ret[1][0]
    summary_writer = tf.summary.FileWriter('logs',
                                      sess.graph)