Modification: 1. added L2 regularizer 2. removed word embedding normalization 3. conv layer switched to rectifier instead of tanh 4. different regularizer weight for different parameters(copied from Nal's code)
Modification:
1. added L2 regularizer
2. removed word embedding normalization
3. conv layer switched to rectifier instead of tanh
4. different regularizer weight for different parameters(copied from Nal's code)

Enhancement:
1. variable/debug info printing
2. parameters to change the training scheme(such as using fixed learning rate or not)

Possible todo:
1. word embedding layer initialized to normal distribution?
2. why the p_y_given_x become the same for all the instances after some iterations?
3. should I switch back to tanh for conv layer and even logreg layer?
4. should I add one more conv layer?
5. refer back to Nal's code?

sk413025/twitter-sent-dnn,sk413025/twitter-sent-dnn,xiaohan2012/twitter-sent-dnn,xiaohan2012/twitter-sent-dnn