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## Tensor Dimensions

A tensor's rank indicates the number of axes it contains, and the size of each dimension represents the length along that axis.

 

### 0-D Tensor (Scalar)

This is a single number or a lone value.

```python title="Example of a 0-D Tensor (Scalar)"
import tensorflow as tf
scalar = tf.constant(3.14)
print(scalar)
```

 
 
### 1-D Tensor (Vector)

An array of numbers, similar to a list in Python.

```python title="Example of a 1-D Tensor (Vector)"
vector = tf.constant([1, 2, 3])
print(vector)
```

 
 
### 2-D Tensor (Matrix)

A structured dataset made up of rows and columns.

```python title="Example of a 2-D Tensor (Matrix)"
matrix = tf.constant([[1, 2], [3, 4], [5, 6]])
print(matrix)
```

 
 
### 3-D and Higher-Dimensional Tensors

Used to represent more complex structures, such as images and video data.

```python title="Example of a 3-D Tensor"
tensor_3d = tf.constant([[[1, 2], [3, 4]], [[5, 6], [7, 8]]])
print(tensor_3d)
```

 

## Properties of a Tensor

Tensors have several properties:

- `shape`: A tuple representing the dimensions of the tensor.

- `dtype`: The data type stored in the tensor (e.g., float32, int32).

- `device`: Information about the device (CPU/GPU) where the tensor is executed.

```python title="Checking Tensor Properties"
example_tensor = tf.constant([[1, 2, 3], [4, 5, 6]], dtype=tf.float32)

print(f"Shape: {example_tensor.shape}")
# Shape: (2, 3)

print(f"Data Type: {example_tensor.dtype}")
# Data Type: <dtype: 'float32'>
```

By examining the properties of a tensor, you can easily determine its structure and data type.

In the next lesson, we'll go over a brief quiz to review what we have learned so far.

A 2D tensor is a data structure composed of rows and columns, commonly referred to as a matrix. It is used to represent data in a tabular format.

### Questions about the Dimensionality of Tensors