Course Content
Introduction
TensorFlow is an open-source and free library for differentiable programming and data flow across a variety of tasks. TensorFlow is a math library and it is used for machine learning applications for example neural networks.
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TensorFlow for Beginners

### Creating a Tensor

Following is the way of creating a Tensor

Syntax:

`tf.constant(value, dtype, name = "")`

Arguments:

-`value`: Value of n-dimension to define the tensor.

Optional –

‘dtype’ – Define the type of data:

• `tf.string`: String variable
• `tf.int16`: Integer variable
• `tf.float32`: Flot variable
• “name”: Name of the tensor.

Optional:

By default, `Const_1:0`

### To create a tensor of dimension 0

```import tensorflow as tf
r1 = tf.constant(1, tf.int16)
print(r1)
r2 = tf.constant(1, tf.int16, name = "my_scalar")
print(r2)```

The output of the above code will be Tensor(“Const_1:0”, shape=(), dtype=int16) Tensor(“my_scalar:0”, shape=(), dtype=int16)

### To create a tensor with decimal or string values

```import tensorflow as tf
# Decimal
r1_decimal = tf.constant(1.12345, tf.float32)
print(r1_decimal)
# String
r1_string = tf.constant("infovistar", tf.string)
print(r1_string)```

The output of the above code will be Tensor(“Const_2:0”, shape=(), dtype=float32) Tensor(“Const_3:0”, shape=(), dtype=string)

### To create a tensor of dimension 1

```import tensorflow as tf
r2_boolean = tf.constant([True, True, False], tf.bool)
print(r2_boolean)
## Rank 2
r2_matrix = tf.constant([ [1, 2],
[3, 4] ], tf.int16)
print(r2_matrix)```

The output of the above code will be Tensor(“Const_4:0”, shape=(3,), dtype=bool) Tensor(“Const_5:0”, shape=(2, 2), dtype=int16)