test.py 文件源码

def createBaseNetworkLarge(inputDim, inputLength):
        baseNetwork = Sequential()
        baseNetwork.add(Embedding(input_dim=inputDim, output_dim=inputDim, input_length=inputLength))
        baseNetwork.add(Conv1D(1024, 7, strides=1, padding='valid', activation='relu', kernel_initializer=RandomNormal(mean=0.0, stddev=0.02), bias_initializer=RandomNormal(mean=0.0, stddev=0.02)))
        baseNetwork.add(MaxPooling1D(pool_size=3, strides=3))
        baseNetwork.add(Conv1D(1024, 7, strides=1, padding='valid', activation='relu', kernel_initializer=RandomNormal(mean=0.0, stddev=0.02), bias_initializer=RandomNormal(mean=0.0, stddev=0.02)))
        baseNetwork.add(MaxPooling1D(pool_size=3, strides=3))
        baseNetwork.add(Conv1D(1024, 3, strides=1, padding='valid', activation='relu', kernel_initializer=RandomNormal(mean=0.0, stddev=0.02), bias_initializer=RandomNormal(mean=0.0, stddev=0.02)))
        baseNetwork.add(Conv1D(1024, 3, strides=1, padding='valid', activation='relu', kernel_initializer=RandomNormal(mean=0.0, stddev=0.02), bias_initializer=RandomNormal(mean=0.0, stddev=0.02)))
        baseNetwork.add(Conv1D(1024, 3, strides=1, padding='valid', activation='relu', kernel_initializer=RandomNormal(mean=0.0, stddev=0.02), bias_initializer=RandomNormal(mean=0.0, stddev=0.02)))
        baseNetwork.add(Conv1D(1024, 3, strides=1, padding='valid', activation='relu', kernel_initializer=RandomNormal(mean=0.0, stddev=0.02), bias_initializer=RandomNormal(mean=0.0, stddev=0.02)))
        baseNetwork.add(MaxPooling1D(pool_size=3, strides=3))
        baseNetwork.add(Flatten())
        baseNetwork.add(Dense(2048, activation='relu'))
        baseNetwork.add(Dropout(0.5))
        baseNetwork.add(Dense(2048, activation='relu'))
        baseNetwork.add(Dropout(0.5))
        return baseNetwork