categorical_crossentropy's output is non-zero when y_true and y_pred are 1
### Multi class error

The issue with this code is that it is using **categorical_crossentropy** loss function for a binary classification problem.**categorical_crossentropy**is meant for multi-class classification problems where each example can belong to one of several classes.

For a binary classification problem with only two classes (0 or 1), binary_crossentropy is the appropriate loss function to use.

```
import tensorflow as tf

y_true = tf.constant([[1.]])
y_pred = tf.constant([[1.]])

loss = tf.keras.losses.binary_crossentropy(y_true, y_pred)
print(loss)  # 0.0
``` @pat749 i think it because limitations of floating-point arithmetic used by computers.

Obtaining a very small non-zero loss value even when the actual and predicted values are the same is not a bug, it is a normal behavior and expected in most cases. @neel-jotaniya That is correct. In many cases, such as in machine learning models, the goal is to minimize the difference between the predicted and actual values, which is usually measured using a loss function. However, it is often impossible to achieve a perfect match between the predicted and actual values, so a very small non-zero loss value is expected and acceptable. This is particularly true for regression problems where the output is a continuous variable. This value is due to the epsilon value added here https://github.com/keras-team/keras/blob/bcb0e8bc686df15dbbc905503ec9fc337bbabbb9/keras/backend.py#L7290 
The small epsilon value is added usually during numerical computation for example when division operation taking place, it's often added to the denominator to prevent a divide by zero error or to  avoid overflow/underflow issues.
Epsilon value is very small which is `1e-07 ` and does not create any large impact and can be ignored. Thanks! This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you. Closing due to lack of recent activity. Please update the issue when new information becomes available, and we will reopen the issue. Thanks! Are you satisfied with the resolution of your issue?
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<details><summary>Click to expand!</summary> 
 
 ### Issue Type

Bug

### Have you reproduced the bug with TF nightly?

Yes

### Source

source

### Tensorflow Version

2.11.0

### Custom Code

Yes

### OS Platform and Distribution

_No response_

### Mobile device

_No response_

### Python version

_No response_

### Bazel version

_No response_

### GCC/Compiler version

_No response_

### CUDA/cuDNN version

_No response_

### GPU model and memory

_No response_

### Current Behaviour?

```shell
When `y_true` and `y_pred` are `1.`, the categorical crossentropy outputted is a non-zero value. But it should be **zero** following the design of categorical crossentropy.
```


### Standalone code to reproduce the issue

```shell
import tensorflow as tf

y_true = tf.constant([[1.]])
y_pred = tf.constant([[1.]])

loss = tf.keras.metrics.categorical_crossentropy(y_true, y_pred)
print(loss)  # 1.1920929e-07 but should be 0
```
```


### Relevant log output

```shell
1.1920929e-07
```
</details>