# How to create tensors in TensorFlow

This tutorial explains how to create tensors in TensorFlow.

What are tensors

Tensors are multi-dimensional arrays with a uniform type.

#### Create Scalar in TensorFlow

Scalar is a `rank-0` tensor. A scalar doesn't have any axis and contains single value. Below is the example for creating a scalar in TensorFlow.

``` ```
import tensorflow as tf

scalar = tf.constant(10)
print(scalar)

# Output
tf.Tensor(10, shape=(), dtype=int32)
``` ```
###### Check number of axes in Scalar
``` ```
print(scalar.ndim)

# Output
0
``` ```
###### Check shape of Scalar
``` ```
print(scalar.shape)

# Output
()
``` ```

#### Create Vector in TensorFlow

A vector is a `rank-1` tensor. A vector contains one axis. Below is the example of creating a Vector in TensorFlow.

``` ```
import tensorflow as tf

vector = tf.constant([6.0, 9.0, 11.0])
print(vector)

# Output
tf.Tensor([ 6.  9. 11.], shape=(3,), dtype=float32)
``` ```
###### Check number of axes in Vector
``` ```
print(vector.ndim)

# Output
1
``` ```
###### Check shape of Vector
``` ```
print(vector.shape)

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

#### Create Matrix in TensorFlow

A matrix is a `rank-2` tensor. Matrix have 2 axes. Below is the example of creating matrix in TensorFlow.

``` ```
import tensorflow as tf

matrix = tf.constant([[4, 4, 2],
[3, 4, 3],
[5, 6, 1]])
print(matrix)

# Output
tf.Tensor(
[[4 4 2]
[3 4 3]
[5 6 1]], shape=(3, 3), dtype=int32)
``` ```
###### Check number of axes in Matrix
``` ```
print(matrix.ndim)

# Output
2
``` ```
###### Check shape of Matrix
``` ```
print(matrix.shape)

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

#### Create tensor with more than 2 axes

``` ```
import tensorflow as tf

rank_3_tensor = tf.constant([
[[2 ,1, 1, 2, 3, 4],
[4, 5, 6, 7, 8, 9]],
[[6, 10, 11, 12, 13, 14],
[7, 15, 16, 17, 18, 19]],
[[11, 20, 21, 22, 23, 24],
[12, 25, 26, 27, 28, 29]],])

print(rank_3_tensor)

# Output
tf.Tensor(
[[[ 2  1  1  2  3  4]
[ 4  5  6  7  8  9]]

[[ 6 10 11 12 13 14]
[ 7 15 16 17 18 19]]

[[11 20 21 22 23 24]
[12 25 26 27 28 29]]], shape=(3, 2, 6), dtype=int32)
``` ```
###### Check shape of tensor
``` ```
print(rank_3_tensor.shape)

# Output
(3, 2, 6)
``` ```
###### Check number of axes in tensor
``` ```
print(rank_3_tensor.ndim)

# Output
3
``` ```
###### Check type of element in tensor
``` ```
print(rank_3_tensor.dtype)

# Output
dtype: 'int32'
``` ```
###### Check number of elements along axis 0 of tensor
``` ```
print(rank_3_tensor.shape[0])

# Output
3
``` ```
###### Check number of elements along last axis of tensor
``` ```
print(rank_3_tensor.shape[-1])

# Output
6
``` ```

Category: TensorFlow

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