Summary  
This chapter introduces how to specify and convert data types for tensors by using the `dtype` argument in creation functions and `tf.cast` for transforming between types, while highlighting the effects on precision and value range.  

General domain of usage  
Deep learning model development

# Data Types

Welcome back, explorers of the TensorFlow realm! Today's quest involves navigating the world of **data types** in TensorFlow. Just as numbers and text have various types in programming languages (like integers, floats, or strings), tensors have their own set of types.

## Available Data Types in TensorFlow

TensorFlow supports a plethora of data types to cater to different kinds of data and operations. Some of the most commonly used types are:

- `tf.float16`, `tf.float32`, `tf.float64`: These are floating-point numbers where the numbers after the dot matter. The number in their name (like `16` in `tf.float16`) tells you about the **number of bits** used. `tf.float32` is commonly used because it offers a good balance between precision and computation speed;

- `tf.int8`, `tf.int16`, `tf.int32`, `tf.int64`: These are integer types, numbers without a decimal point. They can be both positive and negative;

- `tf.uint8`, `tf.uint16`, `tf.uint32`, `tf.uint64`: 'u' here stands for 'unsigned', meaning these integers are always non-negative;

- `tf.bool`: Represents boolean values (`True` or `False`);

- `tf.string`: For text data.

> **Note**
>
> For integer types, a larger number in the name only signifies a **wider range of values** it can store. But for floating-point types, a higher number in the name also indicates greater **computational accuracy**.

There are more data types available in TensorFlow, but for beginners, it's crucial to get familiarized with these primary types first. For a comprehensive list of data types supported by TensorFlow, consider checking out <a 
href="https://www.tensorflow.org/api_docs/python/tf/dtypes"
target="_blank"
style="color: #ff8a00; font-weight: 600; text-decoration: none; transition: 0.3s ease-in-out;"
onmouseover="this.style.color='#ff0000'"
onmouseout="this.style.color='#d27204'">this specific page</a> in the TensorFlow documentation.

## Setting the Data Type when Creating a Tensor

When you're initializing a tensor, you can specify its type using the `dtype` argument:

import tensorflow as tf

# Creating a tensor of type `float16`
tensor_float = tf.constant([1.2, 2.3, 3.4], dtype=tf.float16)

# Creating a tensor of type `int64`
tensor_int = tf.constant([1, 2, 3], dtype=tf.int64)

# Display tensors
print(tensor_float)
print('-' * 50)
print(tensor_int)

> **Note**
>
> Many tensor creation functions adopt this approach. It's applicable in methods such as `tf.Variable()`, `tf.zeros()`, `tf.ones()`, `tf.random.normal()`, `tf.random.uniform()`, and even in `tf.convert_to_tensor()`.

## Converting Between Data Types

But what if the function you're using doesn't allow for **direct data type specification**? Or perhaps you already possess a tensor of a certain type and **need to change it**. In these cases, you'll need to transform tensors **from one data type to another**. This is especially relevant as certain neural network operations or layers often demand inputs of a particular type, predominantly **floating point numbers**.

You can use `tf.cast()` to achieve this:

import tensorflow as tf

# Create a tensor
tensor_float = tf.constant([-1.2, 2.3, 3.8])

# Convert our `tensor_float` from `float32` to `int32`
tensor_int_converted = tf.cast(tensor_float, dtype=tf.int32)

# Display a tensor
print(tensor_float)
print('-' * 50)
print(tensor_int_converted)

Remember, while converting from a floating-point type to an integer type, TensorFlow will perform floor operation, essentially dropping the decimal part. So, `3.8` becomes `3`, and `-1.2` becomes `-1`.

> **Note**
>
> Be cautious while changing data types, especially when you move to a type with less precision. **You might lose information in the process**.

Dive deep into the world of TensorFlow with our course, designed to give you a robust understanding of its core components. Begin with an exploration of tensors and the basics of TensorFlow framework. By the course's end, you'll have honed the skills to build tensor-driven systems, including crafting a basic neural network. Equip yourself with the knowledge to harness TensorFlow's full potential and set the foundation for advanced deep learning pursuits.

You will gain a foundational understanding of TensorFlow's primary components - Tensors. You'll delve into the nature and applications of tensors, familiarize yourself with tensor properties, and acquire knowledge in essential mathematical operations.

You'll learn how TensorFlow operates and the ways to improve its performance. By the end of this module, you'll be well-equipped to implement basic neural networks or other tensor calculations, using only the TensorFlow library without any extras.

Data Types

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Data Types

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Awesome!