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生成的层有什么办法可以使上一层的神经元总是比下一层多?我有示例代码:

from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Flatten, InputLayer
from kerastuner.tuners import RandomSearch
from tensorflow.keras.optimizers import Adam

def build_model(hp):
    model = Sequential()
    model.add(InputLayer(input_shape=e_net_out_shape))
    model.add(Flatten())

    for i in range(hp.Int('num_layers', 0, 2)):
        model.add(Dense(units=hp.Int('units_' + str(i), min_value=32, max_value=928,  step=64), activation='relu'))

    model.add(Dense(39, activation='softmax'))
    model.compile(optimizer=Adam(learning_rate=0.01), loss='categorical_crossentropy', metrics=['acc'])
    return model

并在使用随机搜索算法搜索下一层 ex。units_2比前一个 ex 有更多的神经元。units_1. 所以我想将max_value下一层限制为前一层的当前值。

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