multiple-loss 0.0.6

it's implimentation of multiple loss

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

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

pip install tf-multilabelloss