In [1]:

    

# Import the tensorflow library, and reference it as `tf`
import tensorflow as tf

# Build our graph nodes, starting from the inputs
a = tf.constant(5, name="input_a")
b = tf.constant(3, name="input_b")
c = tf.mul(a,b, name="mul_c")
d = tf.add(a,b, name="add_d")
e = tf.add(c,d, name="add_e")

# Open up a TensorFlow Session
sess = tf.Session()

# Execute our output node, using our Session
output = sess.run(e)

# Open a TensorFlow SummaryWriter to write our graph to disk
writer = tf.train.SummaryWriter('./my_graph', sess.graph)

# Close our SummaryWriter and Session objects
writer.close()
sess.close()

# To start TensorBoard after running this file, execute the following command:
# $ tensorboard --logdir='./my_graph'


    

In [2]:

    

# Import the tensorflow library, and reference it as `tf`
import tensorflow as tf


    

In [3]:

    

# Build our graph nodes, starting from the inputs
a = tf.constant(5, name="input_a")
b = tf.constant(3, name="input_b")
c = tf.mul(a,b, name="mul_c")
d = tf.add(a,b, name="add_d")
e = tf.add(c,d, name="add_e")


    

In [4]:

    

# Open up a TensorFlow Session
sess = tf.Session()


    

In [5]:

    

# Execute our output node, using our Session
output = sess.run(e)


    

In [6]:

    

# Open a TensorFlow SummaryWriter to write our graph to disk
writer = tf.train.SummaryWriter('./my_graph', sess.graph)


    

In [7]:

    

# Close our SummaryWriter and Session objects
writer.close()
sess.close()


    

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