This tutorial explains how to create tensors in **TensorFlow**.

What are tensors

Tensors are multi-dimensional arrays with a uniform type.

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)
```

` ````
print(scalar.ndim)
# Output
0
```

` ````
print(scalar.shape)
# Output
()
```

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)
```

` ````
print(vector.ndim)
# Output
1
```

` ````
print(vector.shape)
# Output
(3,)
```

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)
```

` ````
print(matrix.ndim)
# Output
2
```

` ````
print(matrix.shape)
# Output
(3, 3)
```

` ````
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)
```

` ````
print(rank_3_tensor.shape)
# Output
(3, 2, 6)
```

` ````
print(rank_3_tensor.ndim)
# Output
3
```

` ````
print(rank_3_tensor.dtype)
# Output
dtype: 'int32'
```

` ````
print(rank_3_tensor.shape[0])
# Output
3
```

` ````
print(rank_3_tensor.shape[-1])
# Output
6
```

Category: TensorFlow