In this Python tutorial, we will learn** how to use the TensorFlow one_hot **function in Python. Additionally, we will cover the following topics.

- TensorFlow one_hot example
- TensorFlow one_hot to index
- TensorFlow one_hot encoding example
- TensorFlow one_hot axis
- TensorFlow one hot categorical
- TensorFlow one hot encoding string
- TensorFlow one hot to dense
- TensorFlow text one hot encoding
- reverse one hot encoding tensorflow
- TensorFlow sparse one hot
- TensorFlow multilabel one hot

**Table of Contents**show

## Python TensorFlow one_hot

- In this section, we will discuss how to use the one_hot() function in TensorFlow Python.
- In python, one-hot encoding is a technique used a convert categorical data into numbers so that you can use it for machine learning algorithms.
- Suppose we have random variables that indicate the indices numbers and now we want to convert these numbers into numerical integer numbers
**(0,1)**. - To perform this particular task, we are going to use the
**tf.one_hot()**function. This function will help the user to returns a**one-hot**tensor.

**Syntax:**

Let’s have a look at the syntax and understand the working of the **tf.one_hot() **function in TensorFlow Python.

```
tf.one_hot
(
indices,
depth,
on_value=None,
off_value=None,
axis=None,
dtype=None,
name=None
)
```

- It consists of a few parameters
**indices:**This parameter indicates the index number which we want to operate and it is a tensor of indices.**depth:**This defines the dimension of a hot tensor no of rows and columns.**on_value:**By default it takes**1**value if it is not provided.**off_value:**By default it takes**0**value if it is not provided.**axis:**This parameter defines the axis to fill and by default its value is**-1**.**dtype:**The datatype of the output tensor.

Note:If the data type is not provided then by default the datatype ofon_value and off_valuewill betf.float32and it must be the same data type in both parameters. If it does not match then the type error will raise.

**Example:**

Let’s take an example and check how to use the **one_hot()** function in Python TensorFlow.

**Source Code:**

```
import tensorflow as tf
new_indi = [2, 3, 5]
new_val = 4
result=tf.one_hot(new_indi, new_val)
print(result)
```

In the above code we have imported the TensorFlow library and then initialize a list in which we have assigned the indices numbers. After that, we have used the** tf.one_hot()** function and within this function, we have passed the indices and depth as an argument.

Here is the implementation of the following given code.

Also, read: TensorFlow Tensor to NumPy

## TensorFlow one_hot example

- In this section, we will discuss the example of one_hot function in Python TensorFlow.
- To do this task, we are going to use the
**tf.one_hot()**function and it will convert the random number with binary integer numbers. - In this example we have create the session by importing the
**tf.compat.v1.disable_eager_execution()**function. - Next, we will declare the indices numbers in the list and then we are going to use the
**tf.one_hot()**function and assign the**indices, depth**axis as an argument.

**Example:**

```
import tensorflow as tf
tf.compat.v1.disable_eager_execution()
new_ind = [0,2,3,4,1]
tens = tf.constant(4)
result = tf.one_hot(new_ind, tens,on_value=1.0,off_value=0.0, axis =-1)
with tf.compat.v1.Session() as val:
new_output=val.run(result)
print(new_output)
```

Here is the execution of the following given code.

As you can see in the Screenshot the output displays the tensor one_hot.

Read: TensorFlow get shape

## TensorFlow one_hot to index

- In this Program, we will discuss how to convert the one_hot to index in Python TensorFlow.
- To do this task, first, we will display the tensor of
**one-hot**and then convert it into an index number. By using the**tf.argmax()**function we can easily convert the one-hot tensor into the index. - In Python, the
**tf.argmax()**function is used to return the indices of the given input tensor.

**Syntax:**

Let’s have a look at the syntax and understand the working of the **tf.argmax()** function.

```
tf.argmax
(
x,
axis
)
```

**Example:**

Let’s take an example and check how to convert the one-hot tensor to index in Python TensorFlow.

**Source Code:**

```
import tensorflow as tf
tf.compat.v1.disable_eager_execution()
new_ind = [0,2,3,4,1]
tens = tf.constant(4)
result = tf.one_hot(new_ind, tens,on_value=1.0,off_value=0.0, axis =-1)
new_result = tf.argmax(result, axis=1)
with tf.compat.v1.Session() as val:
new_output=val.run(new_result)
print(new_output)
```

Here is the implementation of the following given code.

As you can see in the Screenshot the output displays the indices numbers.

Read: Import error no module named TensorFlow

## TensorFlow one_hot encoding example

- In this section, we will discuss the example one_hot encoding in TensorFlow Python.
- By using the
**tf.one_hot()**function, we can easily perform this particular task and use all the parameters in function.

**Example:**

Let’s have a look at the example and understand the working of the **tf.one_hot()** function.

**Source code:**

```
import tensorflow as tf
tens=[1,3,4,5]
result= tf.one_hot(tens, depth=4, on_value="True", off_value="False")
tf.print(result)
```

In the following given code we have imported the TensorFlow library and then initialize a list that indicates the indices number. After that, we have used the** tf.one_hot()** function and within this function, we have assigned the depth, on_value, and off_value as an argument.

In this example we have set the **on_value=”True” and off_value=”False”**. Once you will execute this code the output displays the one-hot tensor in the order of boolean values.

Here is the Screenshot of the following given code.

Read: Python Copy NumPy Array

## TensorFlow one_hot axis

- In this section, we will discuss we are going to use the axis parameter in one_hot() function in TensorFlow Python.
- To do this task, we will use the
**tf.one_hot()**function and inside this function, we have set the**axis=-1**that indicates the innermost axis.

**Syntax:**

Let’s have a look at the Syntax and understand the working of the TensorFlow **one_hot() **function in Python.

```
tf.one_hot
(
indices,
depth,
on_value=None,
off_value=None,
axis=None,
dtype=None,
name=None
)
```

**Example:**

```
import tensorflow as tf
tf.compat.v1.disable_eager_execution()
result = tf.one_hot([1,3,5,2], 6,axis =-1)
with tf.compat.v1.Session() as val:
new_output=val.run(result)
print(new_output)
```

In the following given code we have created a session by importing the **tf.compat.v1.disable_eager_execution()** function and then specified the **axis=-1** in **tf.onehot()** function.

Here is the Screenshot of the following given code.

Read: Python TensorFlow expand_dims

## TensorFlow one hot categorical

- Here we are going to discuss how to use the one_hot categorical() function in Python TensorFlow.
- In this example, we are going to use the tfp.distribution.OneHotCategorical() function is parameterized by the log probabilities and then we will create a class distribution.

**Syntax:**

Here is the Syntax of **tfp.distributions.OneHotCategorical()** function.

```
tfp.distributions.Categorical
(
logits=None,
probs=None,
dtype=tf.int32,
validate_args=False,
allow_nan_stats=True,
name='Categorical'
)
```

**Example:**

Let’s take an example and understand the working of **tfp.distributions.OneHotCategorical()** function.

```
import tensorflow as tf
import tensorflow_probability as tfp
tens=tfp.distributions.OneHotCategorical(probs=[0.7,0.4,0.5])
print(tens)
result=tens.sample()
print(result)
```

Here is the implementation of the following given code.

As you can see in the Screenshot the output displays the event shape as 3 which means the random variable is now a vector.

Read: Python NumPy Savetxt + Examples

## TensorFlow one hot encoding string

- In this section, we will discuss how to get the string value in output by creating a tensor in Python.
- In this example we have set the parameter
**on_value=’x’ and off_value=’y’**in**tf.one_hot()**function. once you will execute this code the output displays the string value in a one-hot tensor.

**Example:**

```
import tensorflow as tf
tens=[1,3,6,5,4,7]
result= tf.one_hot(tens, depth=6, on_value='x', off_value='y')
tf.print(result)
```

Here is the Screenshot of the following given code.

Read: Python TensorFlow truncated normal

## TensorFlow one hot to dense

- In this section, we will discuss how to convert one hot tensor to dense in Python TensorFlow.
- By using the slicing and
**tf.where()**function we can easily convert the one_hot tensor to dense. - To do this task first we will import the
**tf.compat.v1.disable_eager_execution()**module for creating a session and then we are going to use the**tf.constant()**function for creating tensor indices.

**Syntax:**

Here is the Syntax of **tf.where()** function

```
tf.where
(
condition,
x=None,
y=None,
name=None
)
```

**Example:**

Let’s take an example and check how to convert the **one_hot** tensor to dense in Python.

**Source Code:**

```
import tensorflow as tf
tf.compat.v1.disable_eager_execution()
tens = tf.constant([[1, 0, 0], [0, 1, 0], [0, 0, 1]])
dense = tf.where(tf.equal(tens, 1))
indices = dense[:,1]
with tf.compat.v1.Session() as val:
new_output=val.run(indices)
print(new_output)
```

Here is the Screenshot of the following given code

Read: Convert list to tensor TensorFlow

## TensorFlow text one hot encoding

- In this section, we will discuss how to encode a text into a list of words in Python.
- To perform this particular task we are going to use the
**tf.keras.preprocessing.text.one_hot()**function and this function is used to convert the text into a list of words. In this example we will take a string as a input and it will return a list of encoded integers.

**Syntax:**

Let’s have a look at the Syntax and understand the working of **tf.Keras.preprocessing.text.one_hot() **function.

```
tf.keras.preprocessing.text.one_hot
(
input_text,
n,
filters='!"#$%&,
lower=True,
split=' ',
)
```

- It consists of a few parameters
**input_text:**This parameter indicates the input text that will be string.**n**: This defines the size of input text**split:**This parameter is used for word splitting.

**Example:**

Let’s take an example and check how to encode a text into a list of words in Python.

**Source Code:**

```
import tensorflow as tf
tens_text = "Python Programming"
new_output = tf.keras.preprocessing.text.one_hot(tens_text,n=3)
print(new_output)
```

Here is the implementation of the following given code

Read: Tensorflow custom loss function

## reverse one hot encoding tensorflow

- In this section, we will discuss how to reverse the one-hot encoding Tensor in Python TensorFlow.
- To perform this particular task we are going to create one_hot tensor by using the
**tf.one_hot()**function and then we are going to reverse the**one-hot**tensor elements by applying the**tf.reverse()**function in Python. - This function is used to reverse a tensor based on axis and it is available in TensorFlow package.

**Syntax:**

Let’s have a look at the Syntax and understand the working of **tf.reverse()** function.

```
tf.reverse
(
tensor,
axis,
name=None
)
```

- It consists of a few parameters
**tensor:**This parameter indicates the tensor and it must be a integer datatype.**axis:**The indices of the dimension to reverse.**name:**By default it takes none value and it indicates the name of the operation.

**Example:**

Let’s take an example and check how to reverse the one-hot encoding Tensor in Python.

**Source Code:**

```
import tensorflow as tf
new_indi = [3, 1, 2]
new_val = 4
result=tf.one_hot(new_indi, new_val)
print(result)
b_reversed = tf.reverse(result, axis=[0, 1])
print(b_reversed)
```

You can refer to the below Screenshot.

Read: TensorFlow next_batch

## TensorFlow sparse one hot

- In this Program, we will discuss how to use the
**tf.sparse.to_dense()**function in Python TensorFlow. - By using the
**tf.sparse.to_dense()**function is used to convert the sparse tensor to dense tensor.

**Syntax:**

Here is the Syntax of tf.sparse.to_dense() function.

```
tf.sparse.to_dense
(
sp_input,
default_value=None,
validate_indices=True,
name=None
)
```

- It consists of a few parameters
**sp_input:**This parameter indicates the input spare tensor which we want to operate.**default_value**:By default it takes none value and it is used to set for indices.**validate_indices:**This parameter specifies there is no reptition and it will check the condition if the value is true then they are sorted in lexicographic order.

**Example:**

Let’s have a look at the example and understand the working of **tf.sparse.to_dense()** function.

**Source Code:**

```
import tensorflow as tf
tens_input = tf.SparseTensor(
dense_shape=[3, 6],
values=[3, 4,5],
indices =[[0, 1],
[0, 3],
[2, 0]])
result=tf.sparse.to_dense(tens_input).numpy()
print(result)
```

In** **the following given code we** **have imported the TensorFlow library and then use the **tf.SparseTensor()** function and within this function we have assigned the dense **shape, values,** and **indices** as an argument.

Here is the implementation of the following given code.

As you can see in the Screenshot the output displays the dense tensor.

Read: TensorFlow Sparse Tensor

## TensorFlow multilabel one hot

- Here we are going to discuss how to use multi labels in one_hot() function in Python TensorFlow.
- To do this task we are going to use the tf.raggged.constant() function and this function is used when we have a nested list in Tensor.
- Next, we will declare a variable and assign the tf.one_hot() function and within this function, we will assign the depth as an argument.

**Syntax:**

Here is the Syntax of** tf.raggged.constant()** function.

```
tf.ragged.constant
(
pylist,
dtype=None,
ragged_rank=None,
inner_shape=None,
name=None,
row_splits_dtype=tf.dypes.int64
)
```

**Example:**

```
import tensorflow as tf
new_indices = tf.ragged.constant([[2, 3], [2], [1, 2]])
one_hot_tensor = tf.one_hot(new_indices, 5)
multi_labels = tf.reduce_max(one_hot_tensor, axis=1)
print(multi_labels)
```

Here is the Screenshot of the following given code.

You may also like to read the following Python TensorFlow tutorials.

- TensorFlow cross-entropy loss
- Binary Cross Entropy TensorFlow
- Tensorflow embedding_lookup
- TensorFlow Graph – Detailed Guide

In this Python tutorial, we have learned** how to use the TensorFlow one_hot **function in Python. Also, we have covered the following topics.

- TensorFlow one_hot example
- TensorFlow one_hot to index
- TensorFlow one_hot encoding example
- TensorFlow one_hot axis
- TensorFlow one hot categorical
- TensorFlow one hot encoding string
- TensorFlow one hot to dense
- Tensorflow dataset onehot encode
- TensorFlow text one hot encoding
- reverse one hot encoding tensorflow
- TensorFlow sparse one hot
- TensorFlow multilabel one hot

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