DeepJet_models.py 文件源码

def Serious_gluon_model(Inputs,nclasses,dropoutRate=-1):
     x =   LocallyConnected2D(64, (8,8) ,stride= (4,4) , border_mode='same', activation='relu',kernel_initializer='lecun_uniform')(Inputs[1])
#     x = MaxPooling2D(pool_size=(2, 2))(x)
     x = Convolution2D(64, (4,4) , 1 , border_mode='same', activation='relu',kernel_initializer='lecun_uniform')(x)
    # x = MaxPooling2D(pool_size=(2, 2))(x)
     x = Convolution2D(64, (4,4) , 1 , border_mode='same', activation='relu',kernel_initializer='lecun_uniform')(x)
     x = MaxPooling2D(pool_size=(2, 2))(x)
     x = Flatten()(x)
     x = merge( [x, Inputs[0]] , mode='concat')
    # linear activation for regression and softmax for classification
     x = Dense(128, activation='relu',kernel_initializer='lecun_uniform')(x) 
     x = Dense(128, activation='relu',kernel_initializer='lecun_uniform')(x) 
     x = Dense(64, activation='relu',kernel_initializer='lecun_uniform')(x) 
     x = Dense(64, activation='relu',kernel_initializer='lecun_uniform')(x) 

     predictions = [Dense(2, activation='linear',init='normal')(x),Dense(nclasses, activation='softmax',kernel_initializer='lecun_uniform')(x)]
     model = Model(inputs=Inputs, outputs=predictions)
     return model