Constants

The constant valued tensors are created using the tf.constant() function that has the following signature:

tf.constant(
  value,
  dtype=None,
  shape=None,
  name='Const',
  verify_shape=False
)

Let's look at the example code provided in the Jupyter Notebook with this book:

c1=tf.constant(5,name='x')
c2=tf.constant(6.0,name='y')
c3=tf.constant(7.0,tf.float32,name='z')

Let's look into the code in detail:

• The first line defines a constant tensor c1, gives it value 5, and names it x.  
• The second line defines a constant tensor c2, stores value 6.0, and names it y.
• When we print these tensors, we see that the data types of c1 and c2 are automatically deduced by TensorFlow.
• To specifically define a data type, we can use the dtype parameter or place the data type as the second argument. In the preceding code example, we define the data type as tf.float32 for c3.

Let's print the constants c1, c2, and c3:

print('c1 (x): ',c1)
print('c2 (y): ',c2)
print('c3 (z): ',c3)

When we print these constants, we get the following output:

c1 (x):  Tensor("x:0", shape=(), dtype=int32)
c2 (y):  Tensor("y:0", shape=(), dtype=float32)
c3 (z):  Tensor("z:0", shape=(), dtype=float32)

In order to print the values of these constants, we have to execute them in a TensorFlow session with the tfs.run() command:

print('run([c1,c2,c3]) : ',tfs.run([c1,c2,c3]))

We see the following output:

run([c1,c2,c3]) :  [5, 6.0, 7.0]