In this Python tutorial, we will learn** how to calculate a Cross-Entropy loss in Python TensorFlow**. Also, we will cover the following topics.

- TensorFlow cross-entropy loss with logits
- TensorFlow cross-entropy loss nan
- TensorFlow cross-entropy loss formula
- TensorFlow cross-entropy loss without softmax
- TensorFlow cross-entropy loss with mask
- TensorFlow binary cross-entropy loss
- TensorFlow Keras cross-entropy loss
- TensorFlow weighted cross-entropy loss
- sparse cross-entropy loss TensorFlow

**Table of Contents**show

## TensorFlow cross-entropy loss

- In this section, we will discuss how to generate the cross-entropy loss between the prediction and labels.
- To perform this particular task, we are going to use the
**tf.Keras.losses.CategoricalCrossentropy()**function and this method will help the user to get the cross-entropy loss between predicted values and label values.

**Syntax:**

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

```
tf.keras.losses.CategoricalCrossentropy(
from_logits=False,
label_smoothing=0.0,
axis=-1,
reduction=losses_utils.ReductionV2.AUTO,
name='categorical_crossentropy'
)
```

- It consists of a few parameters.
**from_logits:**This parameter indicates the logit values and it contains probabilities values that are [0,1].**label_smoothing:**By default, it takes 0.0 values and it will check the condition when it is greater than 0 and compute the loss between the true values.**axis:**By default, it takes a -1 value and the axis along which to generate cross-entropy.**name:**By default, it takes the**‘categorical_crossentropy’**value and defines the name of the operation.

**Example:**

Let’s take an example and check **how to generate the cross-entropy loss between the prediction and labels.**

**Source Code:**

```
import tensorflow as tf
tf.compat.v1.disable_eager_execution()
new_true = [[1.,0.],
[1.,1.]]
new_predict = [[0.9,1.0],
[0.3,1.0]]
new_binar_cross = tf.keras.losses.CategoricalCrossentropy()
result=new_binar_cross(new_true,new_predict)
with tf.compat.v1.Session() as val:
new_output=val.run(result)
print(new_output)
```

In the above code, we have used the **tf.keras.losses.CategoricalCrossentropy()** function and then assign the actual and predicted values to it.

Here is the Screenshot of the following given code.

Read: TensorFlow Multiplication

## TensorFlow cross-entropy loss with logits

- In this section, we are going to calculate the logits value with the help of cross-entropy in Python TensorFlow.
- To perform this particular task, we are going to use the
**tf.nn.softmax_cross_entropy_with_logits()**function, and this method calculates the softmax cross-entropy between labels and logits. - In this method, labels and logits have the same datatype and the axis parameter defines the class dimension.

**Syntax:**

Here is the Syntax of **tf.nn.softmax_cross_entropy_with_logits()** in Python TensorFlow.

```
tf.nn.softmax_cross_entropy_with_logits(
labels, logits, axis=-1, name=None
)
```

- It consists of a few parameters
**labels:**This parameter indicates the class dimension and it is a valid probability distribution.**logits:**These are typically linear output and unnormalized log probabilities.**axis:**By default, it takes a -1 value which specifies the last dimension.**name:**By default, it takes none value and defines the name of the operation.

**Example:**

Let’s take an example and check **how to calculate the logits value with the help of cross-entropy in Python TensorFlow.**

**Source Code:**

```
import tensorflow as tf
tf.compat.v1.disable_eager_execution()
new_logit_val = [[7.0, 8.0, 2.0], [1.0, 6.0, 8.0]]
new_label_val = [[3.0, 2.0, 1.0], [0.0, 1.0, 0.7]]
result=tf.nn.softmax_cross_entropy_with_logits(new_label_val,new_logit_val)
with tf.compat.v1.Session() as val:
new_output=val.run(result)
print(new_output)
```

In the following code, we have imported the TensorFlow library and then created the logit and label values. After that, we have used the **tf.nn.softmax_cross_entropy_with_logits()** function and within this function, we assigned the labels and logits.

Here is the implementation of the following given code.

Read: TensorFlow mean squared error

## TensorFlow cross-entropy loss nan

- In this section, we will discuss how to detect the nan in cross-entropy loss by using Python TensorFlow.
- To perform this particular task, we are going to use the nan values in the actual tensor and then we are going to use the
**tf.keras.losses.CategoricalCrossentropy()**function.

**Example:**

```
import tensorflow as tf
import numpy as np
tf.compat.v1.disable_eager_execution()
new_true = [[1.0,np.nan],
[np.nan,1.]]
new_predict = [[0.9,1.0],
[0.3,1.0]]
new_binar_cross = tf.keras.losses.CategoricalCrossentropy()
result=new_binar_cross(new_true,new_predict)
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 nan value.

Read: Python TensorFlow Placeholder

## TensorFlow cross-entropy loss formula

- In TensorFlow, the loss function is used to optimize the input model during training and the main purpose of this function is to minimize the loss function.
- Cross entropy loss is a cost function to optimize the model and it also takes the output probabilities and calculates the distance from the binary values.

**Example:**

Let’s take an example and check **how to use the cross-entropy in Python TensorFlow**.

**Source Code:**

```
import tensorflow as tf
tf.compat.v1.disable_eager_execution()
new_true = [[1.,0.],
[1.,1.]]
new_predict = [[0.9,1.0],
[0.3,1.0]]
new_binar_cross = tf.keras.losses.CategoricalCrossentropy()
result=new_binar_cross(new_true,new_predict)
with tf.compat.v1.Session() as val:
new_output=val.run(result)
print(new_output)
```

By using the **tf.Keras.losses.CategoricalCrossentropy()** function and within this function we have set the new_true and new_predict values to it.

Here is the Screenshot of the following given code.

Read: Tensorflow iterate over tensor

## TensorFlow cross-entropy loss without softmax

- In this section, we will discuss how to use the loss cross-entropy without softmax in Python TensorFlow.
- To perform this particular task, we are going to use the
**tf.Keras.losses.CategoricalCrossentropy()**function and this method will help the user to get the cross-entropy loss between predicted values and label values.

**Syntax:**

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

```
tf.keras.losses.CategoricalCrossentropy(
from_logits=False,
label_smoothing=0.0,
axis=-1,
reduction=losses_utils.ReductionV2.AUTO,
name='categorical_crossentropy'
)
```

**Example:**

Let’s take an example and check** how to use the loss cross-entropy without softmax in Python TensorFlow.**

**Source Code:**

```
import tensorflow as tf
y_true = [1, 0, 1, 1]
y_pred = [-15.6, 2.27, 8.94, -13.8]
new_result = tf.keras.losses.BinaryCrossentropy(from_logits=True)
new_result(y_true, y_pred).numpy()
```

In the above code, we have imported the TensorFlow library and then use the **tf.Keras.losses.BinaryCrossentropy()** function and within this function we have set the **logits=True**.

After that, we have assigned the **y_pred, y_true()** values, and once you will execute this code the output displays the random value.

Here is the Screenshot of the following given code.

Read: Python TensorFlow truncated normal

## TensorFlow cross-entropy loss with mask

- In this section, we will discuss
**how to find the cross-entropy with mask in Python TensorFlow.** - To perform this particular task, we are going to use the
**tf.equal()**and which is used to return the tensor of boolean values for the given tensor values, and to convert the mask values we are going to use the**tf.compat.v1.to_float()**. - By using the
**tf.compat.v1.losses.softmax_cross_entropy()**and this is used to create a cross entropy loss.

**Syntax:**

Here is the Syntax of **tf.compat.v1.losses.softmax_cross_entropy()** function in Python TensorFlow.

```
tf.compat.v1.losses.softmax_cross_entropy(
onehot_labels,
logits,
weights=1.0,
label_smoothing=0,
scope=None,
loss_collection=ops.GraphKeys.LOSSES,
reduction=Reduction.SUM_BY_NONZERO_WEIGHTS
)
```

- It consists of a few parameters.
**onehot_labels:**This parameter indicates the one-hot encoded labels.**logits:**This parameter specifies the logits values.**weights:**By default, it takes a 1.0 value that is transmit to loss and it is an optional tensor.- label_smoothing: By default, it takes a 0 value and it will check the condition if it is more than 0 value then it will smooth the label.
**scope:**It specifies the scope of the operation and it performs the compute to loss.**loss_collection:**This parameter indicates the collection in which the loss will be added,

**Example:**

Let’s take an example and check **how to find the cross-entropy with mask in Python TensorFlow.**

**Source Code:**

```
import tensorflow as tf
new_logit_val = [[0.0,1.0], [1.0,0.0]]
new_label_val = [[3.0, 2.0], [0.0, 1.0]]
mask = tf.equal(new_logit_val, 0)
weights = tf.compat.v1.to_float(mask)
loss = tf.compat.v1.losses.softmax_cross_entropy(new_label_val, new_logit_val, weights)
print(loss)
```

In the following given code, we have used the tf.equal() function and within this function, we assigned the logit value and convert them into masks by using the **tf.compat.v1.to_float()**.

Here is the Screenshot of the following given code.

## TensorFlow binary cross-entropy loss

- In this section, we will discuss how to calculate a Binary Cross-Entropy loss in Python TensorFlow. For this, you can refer to our detailed article Binary Cross Entropy TensorFlow.
- You will get all the information regarding
**binary cross-entropy loss**.

## TensorFlow Keras cross-entropy loss

- In this section, we will discuss how to measure the cross-entropy loss in Keras.
- To perform this particular task, we are going to use the
**tf.Keras.losses.CategoricalCrossentropy()**function and this method will help the user to get the cross-entropy loss between predicted values and label values.

**Syntax:**

Here is the Syntax of **tf.Keras.losses.CategoricalCrossentropy() in Python TensorFlow**.

```
tf.keras.losses.CategoricalCrossentropy(
from_logits=False,
label_smoothing=0.0,
axis=-1,
reduction=losses_utils.ReductionV2.AUTO,
name='categorical_crossentropy'
)
```

**Example:**

Let’s take an example and check **how to measure the cross-entropy loss in Keras**.

**Source Code:**

```
import tensorflow as tf
tf.compat.v1.disable_eager_execution()
new_true_val = [[1.,1.],
[0.,1.]]
new_predict_val = [[1.0,0.0],
[1.0,0.0]]
new_binar_cross = tf.keras.losses.CategoricalCrossentropy()
result=new_binar_cross(new_true_val,new_predict_val)
with tf.compat.v1.Session() as val:
new_output=val.run(result)
print(new_output)
```

Here is the implementation of the following given code

Read: Python TensorFlow expand_dims

## TensorFlow weighted cross-entropy loss

- In this section, we will discuss how to use the weights in cross-entropy loss by using Python TensorFlow.
- To perform this particular task, we are going to use the tf.nn.weighted_cross_entropy_with_logits() function and this function will help the user to find a weighted cross-entropy.

**Example:**

Let’s take an example and check** how to use the weights in cross-entropy loss by using Python TensorFlow**.

**Source Code:**

```
import tensorflow as tf
new_label = tf.constant([0.1, 1., 1.])
new_logit = tf.constant([1., -0, -9.])
result=tf.nn.weighted_cross_entropy_with_logits(
labels=new_label, logits=new_logit, pos_weight=tf.constant(1.5))
print(result)
```

Here is the implementation of the following given code.

Read: Python TensorFlow reduce_mean

## Sparse cross-entropy loss TensorFlow

- In this Program, we will discuss how to sparse a cross-entropy loss in Python TensorFlow.
- To perform this particular task, we are going to use the tf.keras.losses.SparseCategoricalCrossentropy() function and this method is used to find the cross-entropy loss between the prediction and labels.

**Syntax:**

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

```
tf.keras.losses.SparseCategoricalCrossentropy(
from_logits=False,
reduction=losses_utils.ReductionV2.AUTO,
name='sparse_categorical_crossentropy'
)
```

- It consists of a few parameters.
**from_logits:**This parameter indicates the y_prediction encodes a probability distribution and by default, it takes a false value.- name: By default, it takes ‘sparse_categorical_crossentropy’ and specifies the name of the operation.

**Example:**

Let’s take an example and check** how to sparse a cross-entropy loss in Python TensorFlow.**

**Source Code:**

```
import tensorflow as tf
new_true = [2, 1]
new_prediction = [[0.1, 1.2, 1.2], [1.0, 2.1, 0.95]]
new_result = tf.keras.losses.SparseCategoricalCrossentropy()
new_result(new_true, new_prediction)
```

Here is the implementation of the following given code

Also, take a look at some more TensorFlow tutorials.

In this Python tutorial, we have learned** how to calculate a Cross-Entropy loss in Python TensorFlow**. Also, we have covered the following topics.

- TensorFlow cross-entropy loss with logits
- TensorFlow cross-entropy loss nan
- TensorFlow cross-entropy loss formula
- TensorFlow cross-entropy loss without softmax
- TensorFlow cross-entropy loss with mask
- TensorFlow binary cross-entropy loss
- TensorFlow Keras cross-entropy loss
- TensorFlow weighted cross-entropy loss
- sparse cross-entropy loss TensorFlow

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