Which loss function and metrics to use for multi-label classification with very high ratio of negatives to positives?
Actually you should use tf.nn.weighted_cross_entropy_with_logits.
It not only for multi label classification and also has a pos_weight can pay much attention at the positive classes as you would expected.
I am training a multi-label classification model for detecting attributes of clothes. I am using transfer learning in Keras, retraining the last few layers of the vgg-19 model.
The total number of attributes is 1000 and about 99% of them are 0s. Metrics like accuracy, precision, recall, etc., all fail, as the model can predict all zeroes and still achieve a very high score. Binary cross-entropy, hamming loss, etc., haven't worked in the case of loss functions.
I am using the deep fashion dataset. 
So, which metrics and loss functions can I use to measure my model correctly?