In [1]:

    

import tensorflow as tf


    

In [2]:

    

a = tf.constant(1.5)
b = tf.constant(2, dtype=tf.float32)


    

In [3]:

    

a


    

    
Out[3]:





<tf.Tensor 'Const:0' shape=() dtype=float32>

In [4]:

    

b


    

    
Out[4]:





<tf.Tensor 'Const_1:0' shape=() dtype=float32>

In [5]:

    

c = a + b


    

In [6]:

    

c


    

    
Out[6]:





<tf.Tensor 'add:0' shape=() dtype=float32>

In [8]:

    

with tf.Session() as session:
    res = session.run(c)
    res2 = a.eval()
    print(res)
    print(res2)


    

    




3.5
1.5

In [9]:

    

state = tf.Variable(0)
one = tf.constant(1)

update = tf.assign(state, state + one)


    

In [10]:

    

with tf.Session() as session:
    # It is mandatory to initialize variables before we start running the graph
    session.run(tf.global_variables_initializer())
    print(session.run(state))
    for ind in range(5):
        session.run(update)
        print(session.run(state))


    

    




0
1
2
3
4
5

In [11]:

    

input_data = tf.placeholder(tf.float32)


    

In [12]:

    

op = tf.matmul(input_data, tf.transpose(input_data))


    

In [13]:

    

with tf.Session() as session:
    res = session.run(op, feed_dict={input_data: [[0, 1, 2, 3]]})
    print(res)


    

    




[[ 14.]]

In [ ]: