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An example to start with
We'll consider a simple example of adding two vectors. The graph we want to
build is:
Chapter 2
The corresponding code to define the computational graph is:
v_1 = tf.constant([1,2,3,4])
v_2 = tf.constant([2,1,5,3])
v_add = tf.add(v_1,v_2) # You can also write v_1 + v_2 instead
Next, we execute the graph in the session:
or
with tf.Session() as sess:
print(sess.run(v_add))
sess = tf.Session()
print(sess.run(v_add))
sess.close()
This results in printing the sum of two vectors:
[3 3 8 7]
Remember, each session needs to be explicitly closed using close().
The building of a computational graph is very simple – you go on adding the
variables and operations and passing them through (flow the tensors). In this way
you build your neural network layer by layer. TensorFlow also allows you to use
specific devices (CPU/GPU) with different objects of the computational graph using
tf.device(). In our example, the computational graph consists of three nodes, v_1
and v_2 representing the two vectors, and v_add, the operation to be performed on
them. Now to bring this graph to life we first need to define a session object using
tf.Session(). We named our session object sess. Next, we run it using the run
method defined in the Session class as:
run (fetches, feed_dict=None, options=None, run_metadata)
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