Metadata-Version: 2.1
Name: tf-multilabelloss
Version: 0.0.5
Summary: it's implimentation of preceptron
Home-page: https://github.com/omkarsingh1008/multi_label_loss_pypi
Author: omkarsingh1008
Author-email: omkarsinghrajpurohit363@gmail.com
License: UNKNOWN
Project-URL: Bug Tracker, https://github.com/omkarsingh1008/multi_label_loss_pypi/issues
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.7
Description-Content-Type: text/markdown
License-File: LICENSE

# tf-multilabelloss
Create a multilabelloss which can help as when we working on multilabel classification model. 
meaning of multilabel classification is that:- 

* develop a single model that will provide binary classification predictions for each of the num_class

* In other words it will predict 'positive' or 'negative' for all class.


how to use tf-multilabelloss

```python
from multi_label_loss.multilabelloss import MultilabelLoss
predictions = Dense(len(num_class), activation="sigmoid")(x)
model = Model(inputs=base_model.input, outputs=predictions)
model.compile(optimizer='adam', loss=MultilabelLoss(num_class),metrics=['binary_accuracy'])
```
## installation
```bash
pip install tf-multilabelloss
```


