I am trying to use an LSTM to predict  daily usage for users. I have data for 30 days.
Based on business knowledge I know users divide roughly into different categories. E.g. daily users would have a non-zero usage almost every day, weekly users have one or two days of non-zero usage every 7 days and monthly users might have a couple of days with non-zero usage per 30 days.
Samples where every column is one day and each row is access usage for one user.
User 1: 50,  80, 33,  19, 30, 15, ...
User 2:  0,  21,  13, 30, 0, 5,  0,  0,  55, 28,  0,  19,  0, ...
User 3: 11,  2,  11,  56, .....

From above, User1,3 maybe a daily user, User2 maybe a weekly user.
If I only can get info such as user name and access usage.
Can a single lstm model capture this different types of patterns of users?
The goal is to predict the daily usage for next 10 days of each users.
Why I ask is because I tried 100 epochs with learning rate 0.0001, but error still failed.
The prediction result always look like the same even give another input of user name.