Which loss function should I use in my LSTM and why?
When it comes to regression problem in deep learning mean square error MSE is the most preferred loss function but when it comes to categorical problem where you want your output to be 1 or 0, true or false the cross binary entropy is preferable
I try to understand Keras and LSTMs step by step. Right now I build an LSTM there the input is a sentence and the output is an array of five values which can each be 0 or 1.
Example:
Input sentence: 'I hate cookies'
Output example: [0,0,1,0,1]
For this, I am using keras library.
Now I am not sure which loss function I should use. Right now I just know two predefined loss functions a little bit better and both seem not to be good for my example:
Binary cross entropy: Good if I have a output of just 0 or 1
Categorical cross entropy: Good if I have an output of an array with one 1 and all other values being 0.
Both functions would not make any sense for my example. What would you use and why?
Edit
Another Question: Which Activation function would you use in Keras?