this script takes in lightsvm formated data
but with some modification in data_generator it might fit to other data format 

pypy sgd_fm.py

sgd = SGD(lr=0.002,momentum=0.9,adam=True,nesterov=True,dropout=0.3,l2=0.0,l2_fm=0.0,task='r',n_components=4,nb_epoch=63,interaction=True,no_norm=False)# local 513834
sgd.preload(path+'X_cat_oh_high_order.svm',path+'X_t_cat_oh_high_order.svm')
# sgd.load_weights()
sgd.train(path+'X_train_cat_oh_high_order.svm',path+'X_test_cat_oh_high_order.svm',in_memory=False)
sgd.predict(path+'X_test_cat_oh_high_order.svm',out='valid.csv')
sgd.predict(path+'X_t_cat_oh_high_order.svm',out='out.csv')

If 'adam' is set to True, parameter 'momentum' and 'nesterov' are ignored. 
It is recommended to left 'lr' set to default value.
If using validation, it will automatically save the best weights during training process.

lr: learning rate
momentum: momentum of sgd
nesterov: using nesterov momentum or not
adam: using adam as optimizer
dropout: dropout rate
l2: l2 norm for linear weights
l2_fm: l2 norm for latent weights
l2_bias: l2 norm for bias 
task: 'r' for regression, 'c' for classification
n_components: dimension of latent
nb_epoch: rounds to train
interaction: is set to False, it becomes a normal glm
no_norm: normalize inputs or not,default True

preload: preload datas and create initial weights matrix
train: train the model, can set validation data to save the best model
predict: predict results
load_weights: load saved weights
save_weights: save weights