Invoke the session only for the required computation

Invoke the session only for the required computation.
Constants
Constants are created using the constant() function.
constant(value, dtype=None, shape=None, name='Const', verify_shape=False)
value - Can take any assigned value
dtype - Data Type (float / integer)
shape - this is optional , it takes the dimensions
name - can assign any name to the constant
verify_shape - another optional parameter that verifies the dimension of the
constant.
Example:
x = tf.constant(42, name="a", dtype=tf.float32)
Variables
In TensorFlow all the variables are in-memory buffers that have tensors. These
tensors require initialization to be consumed in the data flow graph.
Variables become a part of the graph by calling the variable() construct.
They are used to keep and update the parameters used in the models.
Variables can be defined as shown below.
a = tf.Variable(tf.zeros([5]), name="a")
Another way of using variable is
z = tf.Variable(tf.add(x, y), trainable=False)
Placeholders
Placeholders are values that are initially unassigned but get initialized when
the session is invoked.
The main use of placeholders is they allow the creation of operations and the
computational graph sans needing to provide the data before that.

The required data can be added during the actual runtime from any external
sources.
Syntax:
placeholder(dtype, shape=None, name=None)
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HandsOn - TensorFlow Basic Hands_on
matrix1 = np.array([[1,3,4],[3,5,3],[4,5,3]])
matrix2 = np.array([[2,5,7],[3,6,8],[2,6,9]])
tf_mat1 = tf.convert_to_tensor(matrix1, dtype=tf.float64)
tf_mat2 = tf.convert_to_tensor(matrix2, dtype=tf.float64)
a = tf_mat1
b = tf_mat2
init = a * b
final_mat1 = tf.multiply(a, b)
b_inv = tf.transpose(b)
final_mat2 = tf.tensordot(a, b_inv, 1)
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Which one of the following would you use to pass data to placeholder during