## What is Tensor

In simple language we can call as multi dimensional array. Let us try to understand different dimensions

Zero dimension : It is a single point or a scalar value

One dimension : It is a line , it consist of zero dimension points

Two dimensions : It is a matrix ,

N dimensions : Tensor

## What is graph and what is session

Tensors flow across a graph consist of operations. Here tensors flows through each node or operation under a session. The graph consist of nodes or operations and tensors are our inputs or resultants out of the nodes.

Each session is a process flow across the graph using nodes and tensors.

Having said all these complex words to understand let us first start with some examples.

Importing library
`import tensorflow as tf`

Creating graph
```
import tensorflow as tf
my_graph = tf.Graph()
with my_graph.as_default():
a = tf.constant([50], name = 'my_const_a')
```

Here we have added one constant. Now we will create session and see the result.
```
import tensorflow as tf
my_graph = tf.Graph()
with my_graph.as_default():
a = tf.constant([50], name = 'my_const_a')
my_sess = tf.Session(graph = my_graph)
result = my_sess.run(a)
print(result)
sess.close()
```

Output
`[50]`

## Adding

```
import tensorflow as tf
my_graph = tf.Graph()
with my_graph.as_default():
a = tf.constant([12], name = 'my_const_a')
b = tf.constant([13], name = 'my_const_b')
c=tf.add(a,b)
sess = tf.Session(graph = my_graph)
result = sess.run(c)
print(result)
sess.close()
```

Output
`[25]`

We will get shape of different dimensions
```
import tensorflow as tf
my_graph = tf.Graph()
with my_graph.as_default():
my_scalar = tf.constant(5)
my_vector = tf.constant([1,2,3])
my_matrix = tf.constant([[1,2,3],[4,5,6],[7,8,9]])
my_tensor = tf.constant( [ [[1,2,3],[4,5,6],[7,8,9]] ,
[[4,5,6],[1,2,3],[7,8,9]] , [[7,8,9],[4,5,6],[1,2,3]] ] )
print(my_scalar.shape)
print(my_vector.shape)
print(my_matrix.shape)
print(my_tensor.shape)
```

Output
```
()
(3,)
(3, 3)
(3, 3, 3)
```

Now let us multiply two matrix by using *matmul()*
```
import tensorflow as tf
my_graph = tf.Graph()
with my_graph.as_default():
my_matrix1=tf.constant([[1,2,3],[4,5,6],[7,8,9]])
my_matrix2=tf.constant([[11,12,13],[14,15,16],[17,18,19]])
my_result=tf.matmul(my_matrix1,my_matrix2)
with tf.Session(graph = my_graph) as my_sess:
result=my_sess.run(my_result)
print(result)
```

Output
```
[[ 90 96 102]
[216 231 246]
[342 366 390]]
```

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