Tensorflow: Combining Loss Functions in LSTM Model for Domain Adaptation
From an implementation perspective, the short answer would be yes. However, I believe your question could be more specific, maybe what you mean is whether you could do it with tf.estimator?
Can any one please help me out?
I am working on my thesis work. Its about Predicting Parkinson disease, Since i want to build an LSTM model to adapt independent of patients. Currently i have implemented it using TensorFlow with my own loss function.
Since i am planning to introduce both labeled train and unlabeled train data in every batch of data to train the model. I want to apply my own loss function on this both labeled and unlabeled train data and also want to apply cross entropy loss only on labeled train data. Can i do this in tensorflow?
So my question is, Can i have combination of loss functions in a single model training on different set of train data?