support_model.add(PReLU(1024))
support_model.add(Dropout(0.4))

support_model.add(Dense(1024,48,activation='softmax'))

trainer = Adadelta(lr = 4.0 , rho = 0.97 , epsilon = 1e-8 )
support_model.compile(loss = 'categorical_crossentropy' , optimizer = trainer)

try:
  for i in range(epoch):
    support_model.fit(new_X_cv[0] , Y_cv[0] , batch_size = 256,nb_epoch=1,shuffle=True,validation_split=0.0,show_accuracy=False)
    support_model.evaluate(new_X_cv[1],Y_cv[1] , show_accuracy=True)
except KeyboardInterrupt:
  print('Stop')

"""
xg_train = xgb.DMatrix( new_X_cv[0], label=[y.index(1) for y in Y_cv[0].tolist()])
xg_cv = xgb.DMatrix(new_X_cv[1] , label=[y.index(1) for y in Y_cv[1].tolist()])

param = {}
# use softmax multi-class classification
param['objective'] = 'multi:softmax'
# scale weight of positive examples
param['eta'] = 0.15
param['max_depth'] = 5
param['silent'] = 1
param['nthread'] = 6
param['num_class'] = 48
param['subsample'] = 0.7
num_round = 20