kaggleQQDistRMS_CL.py 文件源码

def createBaseNetworkSmall(inputDim, inputLength):
    baseNetwork = Sequential()
    baseNetwork.add(Embedding(input_dim=inputDim,
                              output_dim=inputDim, input_length=inputLength))
    baseNetwork.add(Conv1D(256, 7, strides=1, padding='valid', activation='relu', kernel_initializer=RandomNormal(
        mean=0.0, stddev=0.05), bias_initializer=RandomNormal(mean=0.0, stddev=0.05)))
    baseNetwork.add(MaxPooling1D(pool_size=3, strides=3))
    baseNetwork.add(Conv1D(256, 7, strides=1, padding='valid', activation='relu', kernel_initializer=RandomNormal(
        mean=0.0, stddev=0.05), bias_initializer=RandomNormal(mean=0.0, stddev=0.05)))
    baseNetwork.add(MaxPooling1D(pool_size=3, strides=3))
    baseNetwork.add(Conv1D(256, 3, strides=1, padding='valid', activation='relu', kernel_initializer=RandomNormal(
        mean=0.0, stddev=0.05), bias_initializer=RandomNormal(mean=0.0, stddev=0.05)))
    baseNetwork.add(Conv1D(256, 3, strides=1, padding='valid', activation='relu', kernel_initializer=RandomNormal(
        mean=0.0, stddev=0.05), bias_initializer=RandomNormal(mean=0.0, stddev=0.05)))
    baseNetwork.add(Conv1D(256, 3, strides=1, padding='valid', activation='relu', kernel_initializer=RandomNormal(
        mean=0.0, stddev=0.05), bias_initializer=RandomNormal(mean=0.0, stddev=0.05)))
    baseNetwork.add(Conv1D(256, 3, strides=1, padding='valid', activation='relu', kernel_initializer=RandomNormal(
        mean=0.0, stddev=0.05), bias_initializer=RandomNormal(mean=0.0, stddev=0.05)))
    baseNetwork.add(MaxPooling1D(pool_size=3, strides=3))
    baseNetwork.add(Flatten())
    baseNetwork.add(Dense(1024, activation='relu'))
    baseNetwork.add(Dropout(0.5))
    baseNetwork.add(Dense(1024, activation='relu'))
    baseNetwork.add(Dropout(0.5))
    return baseNetwork