def build(self,input_dims):
#句子特征
model=Sequential()
model.add(Convolution2D(100,1,input_dims[0],input_shape=(self.num_channel,100,input_dims[0]),activation='relu'))
model.add(Dropout(0.5))
model.add(MaxPooling2D(pool_size=(50,1)))
model.add(Flatten())
model.add(Dropout(0.5))
model.add(Dense(100,activation='tanh'))
model.add(Dropout(0.5))
#用户整体特征
model2=Sequential()
model2.add(Dense(100,input_dim=input_dims[1],activation='tanh'))
model2.add(Dropout(0.5))
#时间地域特征
model3=Sequential()
model3.add(Dense(output_dim=800,input_dim=input_dims[2],activation='tanh'))
model3.add(Dropout(0.5))
model3.add(Dense(output_dim=300,activation='tanh'))
model3.add(Dropout(0.5))
merged_model= Sequential()
merged_model.add(Merge([model, model2,model3], mode='concat', concat_axis=1))
merged_model.add(Dense(self.num_class))
merged_model.add(Activation('softmax'))
merged_model.compile(loss='categorical_crossentropy',optimizer='adadelta',metrics=['accuracy'],)
self.model=merged_model
self.earlyStopping=EarlyStopping(monitor='val_loss', patience=25, verbose=0, mode='auto')
self.checkpoint=ModelCheckpointPlus(filepath='weights.hdf5',monitor='val_loss',verbose_show=20)
def build(self, vector_dim):
#句子特征
model = Sequential()
model.add(
Convolution2D(100,
1,
vector_dim,
input_shape=(2, 100, vector_dim),
activation='relu'))
model.add(Dropout(0.5))
model.add(MaxPooling2D(pool_size=(50, 1)))
model.add(Flatten())
model.add(Dropout(0.5))
model.add(Dense(100, activation='tanh'))
model.add(Dropout(0.5))
model.add(Dense(3, activation='softmax'))
model.compile(
loss='categorical_crossentropy',
optimizer='adadelta',
metrics=['accuracy'],
)
self.model = model
self.earlyStopping = EarlyStopping(monitor='val_loss',
patience=25,
verbose=0,
mode='auto')
self.checkpoint = ModelCheckpointPlus(filepath='weights.hdf5',
monitor='val_loss',
verbose_show=20)
def build(self, input_dim):
model = Sequential()
model.add(Dense(output_dim=800, input_dim=input_dim,
activation='tanh'))
model.add(Dropout(0.5))
model.add(Dense(output_dim=300, activation='tanh'))
model.add(Dropout(0.5))
model.add(Dense(output_dim=8))
model.add(Dropout(0.3))
model.add(Activation('softmax'))
model.compile(optimizer='adadelta',
loss='categorical_crossentropy',
metrics=['accuracy'])
self.model = model
self.earlyStopping = EarlyStopping(monitor='val_loss',
patience=100,
verbose=0,
mode='auto')
self.checkpoint = ModelCheckpointPlus(filepath='weights.hdf5',
monitor='val_loss',
verbose_show=20)