本文整理汇总了Python中keras.models.Sequential.save_model方法的典型用法代码示例。如果您正苦于以下问题：Python Sequential.save_model方法的具体用法？Python Sequential.save_model怎么用？Python Sequential.save_model使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类keras.models.Sequential的用法示例。
在下文中一共展示了Sequential.save_model方法的1个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: Adadelta
# 需要导入模块: from keras.models import Sequential [as 别名]
# 或者: from keras.models.Sequential import save_model [as 别名]
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