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

- Binary Cross Entropy TensorFlow
- Weighted binary cross entropy TensorFlow
- Binary_cross_entropy_with_logits TensorFlow
- TensorFlow binary cross-entropy sigmoid
- Sparse binary cross-entropy TensorFlow
- Binary Cross Entropy loss function TensorFlow

## Binary Cross entropy TensorFlow

- In this section, we will discuss how to calculate a Binary Cross-Entropy loss in Python TensorFlow.
- To perform this particular task we are going to use the
**tf.Keras.losses.BinaryCrossentropy()**function and this method is used to generate the cross-entropy loss between predicted values and actual values. - In TensorFlow, the binary Cross-Entropy loss is used when there are only two label classes and it also comprises actual labels and predicted labels.

**Syntax:**

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

```
tf.keras.losses.BinaryCrossentropy(
from_logits=False,
label_smoothing=0.0,
axis=-1,
reduction=losses_utils.ReductionV2.AUTO,
name='binary_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 is binary_crossentropy value and it specifies the name of the operation.

**Example:**

```
import tensorflow as tf
new_values = [[1, 0, 1, 1, 0, 0], [1, 1, 0, 1, 0, 0], [1, 0, 1, 1, 0, 0]]
new_values2 = [[.2, .1, .8, .7, .1, .2],[.3, .4, .5, .6, .7, .7], [.1, .2, .3, .4, .5, .6] ]
new_bin_cross_entropy = tf.keras.losses.BinaryCrossentropy()
result = new_bin_cross_entropy(new_values, new_values2).numpy()
print(result)
```

In the following given code, we have imported the TensorFlow library and then created actual values and predicted values. After that, we have used the **tf.keras.losses.BinaryCrossentropy()** function and within this function, we have assigned the predicted and true values in it.

Here is the implementation of the following given code.

Read: TensorFlow Multiplication

## Weighted binary cross entropy TensorFlow

- In this section, we will discuss how to use the weight parameter in the BinaryCrossentropy function in Python TensorFlow.
- In this example, we have mentioned the weights in
**tf.Keras.losses.BinaryCrossentropy()**function and this function is used to generate the cross-entropy loss between predicted values and actual values.

**Syntax:**

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

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

**Example:**

Let’s take an example and check how to use the weight parameter in the BinaryCrossentropy function in Python TensorFlow.

**Source Code:**

```
import tensorflow as tf
new_act_val = [[1, 0], [1, 1]]
new_pred_val = [[-23, 56], [1.92, -24.6]]
weight=[0.8, 0.2]
new_result = tf.keras.losses.BinaryCrossentropy(from_logits=True)
new_result(new_act_val, new_pred_val).numpy()
new_result(new_act_val, new_pred_val, weight).numpy()
```

Here is the Screenshot of the following given code.

Read: TensorFlow Get Variable + Examples

## Binary_cross_entropy_with_logits TensorFlow

- In this Program, we will discuss how to use the binary cross-entropy with logits in Python TensorFlow.
- To do this task we are going to use the
**tf.nn.sigmoid_cross_entropy_with_logits()**function and this function is used to calculate the cross-entropy with given logits. - If you want to find the sigmoid cross-entropy between logits and labels. To do this task we are going to use the
**tf.nn.sigmoid_cross_entropy_with_logits()**function.

**Example:**

Let’s take an example of how to use the binary cross-entropy with logits in Python TensorFlow.

**Source Code:**

```
import tensorflow as tf
tf.compat.v1.disable_eager_execution()
new_logit = tf.constant([0, 0., 1., -1., 0., 1., 1.,0.])
new_label = tf.constant([1., 1., 1., 0., 0., 1., 0.,1.])
new_output=tf.nn.sigmoid_cross_entropy_with_logits(
labels=new_label, logits=new_logit)
with tf.compat.v1.Session() as val:
new_result=val.run(new_output)
print(new_result)
```

Here is the Output of the following given code.

Read: Module ‘tensorflow’ has no attribute ‘log’

## TensorFlow binary cross-entropy sigmoid

- In this section, we will discuss how to use the sigmoid in binary cross-entropy in Python TensorFlow.
- To perform this particular task we are going to use the
**tf.nn.sigmoid_cross_entropy_with_logits()**function and within this function, we have mentioned the sigmoid_logits values and this will calculate cross-entropy with labels.

**Syntax:**

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

```
tf.nn.sigmoid_cross_entropy_with_logits(
labels=None, logits=None, name=None
)
```

- It consists of a few parameters
**labels:**This parameter indicates the tensor of the same type and the values are between 0 and 1.**logits:**By default, it takes none value and specifies the real number.**name:**This parameter indicates the name of the operation.

**Example:**

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

**Source Code:**

```
import tensorflow as tf
tf.compat.v1.disable_eager_execution()
sigmoid_logits = tf.constant([0., -1., 1.,0])
soft_binary_labels = tf.constant([0., 1., 1.,0.])
new_logit = tf.constant([0, 0., 1., -1., 0., 1., 1.,0.])
new_label = tf.constant([1., 1., 1., 0., 0., 1., 0.,1.])
new_output=tf.nn.sigmoid_cross_entropy_with_logits(
labels=soft_binary_labels, logits=sigmoid_logits)
with tf.compat.v1.Session() as val:
new_result=val.run(new_output)
print(new_result)
```

In the following given code, we have imported the TensorFlow library and then created a sigmoid value by using the** tf.constant()** function and within this function, we have assigned the** 0 and 1** values.

After that, we have used the **tf.nn.sigmoid_cross_entropy_with_logits()** function and within this function we assigned the labels and logits values.

Here is the Screenshot of the following given code.

Read: Tensorflow iterate over tensor

## Sparse binary cross entropy TensorFlow

- In this section, we will discuss how to sparse the binary cross-entropy in Python TensorFlow.
- To perform this particular task we are going to use the
**tf.Keras.losses.SparseCategoricalCrossentropy()**function and this function will calculate 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 logit values and it contains probabilities values that are [0,1].**name:**By default, it takes sparse_categorical_crossentropy value and it specifies the name of the operation.

**Example:**

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

**Source Code:**

```
import tensorflow as tf
new_true_value = [0,1]
new_pred_value = [[1, 0.32, 1], [0, 1.0, 0.23]]
result = tf.keras.losses.SparseCategoricalCrossentropy()
new_output=result(new_true_value, new_pred_value)
print(new_output)
```

You can refer to the below Screenshot.

Read: Convert list to tensor TensorFlow

## Binary cross entropy loss function TensorFlow

- In this section, we will discuss how to use the loss function in binary cross entropy by using Python TensorFlow.
- By using the t
**f.keras.losses.BinaryCrossentropy()**function we are going to assign the actual and predicted values in it.

**Example:**

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

**Source Code:**

```
import tensorflow as tf
new_true = [[1.,0.],
[1.,1.]]
new_predict = [[0.9,1.0],
[0.3,1.0]]
new_binar_cross = tf.keras.losses.BinaryCrossentropy()
result=new_binar_cross(new_true,new_predict)
print(result)
```

Here is the implementation of the following given code.

You may also like to read the following TensorFlow tutorials.

- Python TensorFlow expand_dims
- TensorFlow global average pooling
- Batch Normalization TensorFlow
- Python TensorFlow truncated normal
- Python TensorFlow random uniform
- Python TensorFlow reduce_sum
- PyTorch Numpy to Tensor

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

- Binary Cross entropy TensorFlow
- Weighted binary cross entropy TensorFlow
- Binary_cross_entropy_with_logits TensorFlow
- TensorFlow binary cross-entropy sigmoid
- Sparse binary cross-entropy TensorFlow
- Binary cross-entropy loss function TensorFlow

I am Bijay Kumar, a Microsoft MVP in SharePoint. Apart from SharePoint, I started working on Python, Machine learning, and artificial intelligence for the last 5 years. During this time I got expertise in various Python libraries also like Tkinter, Pandas, NumPy, Turtle, Django, Matplotlib, Tensorflow, Scipy, Scikit-Learn, etc… for various clients in the United States, Canada, the United Kingdom, Australia, New Zealand, etc. Check out my profile.