make_and_run_model.py 文件源码

def make_model(batch_size, image_dim):
    model = Sequential()
    model.add(BatchNormalization(batch_input_shape=(batch_size,image_dim[1],image_dim[2],1)))
    model.add(Conv2D( 16 , [3,3],  activation='relu',padding='same'))
    #model.add(Dropout(0.2))
    model.add(Conv2D( 32 , [3,3],  activation='relu',padding='same'))
    #model.add(Dropout(0.2))
    model.add(Conv2D( 64 , [3,3],  activation='relu',padding='same'))
    model.add(Dropout(0.2))
    #model.add(Conv2D( 16 , [3,3],  activation='relu',padding='same'))
    #model.add(Dropout(0.2))
    #model.add(Conv2D( 16 , [3,3],  activation='relu',padding='same'))
    #model.add(Dropout(0.2))
    #model.add(Conv2D( 16 , [3,3],  activation='relu',padding='same'))
    #model.add(Conv2D(64, (3, 3), activation='relu',padding='same'))
    #model.add(Conv2D(64, (3, 3), activation='relu',padding='same'))
    #model.add(Conv2D(64, (3, 3), activation='relu',padding='same'))
    model.add(Conv2D(1, kernel_size=1,  padding='same', activation='sigmoid'))

    return(model)