小编Tzi*_*kos的帖子

我正在尝试第一次获得一个 keras-tuner 调整的深度学习模型。我的调整代码如下：

def build_model_test(hp):
    model = models.Sequential()
    model.add(layers.InputLayer(input_shape=(100,28)))
    model.add(layers.Dense(28,activation = 'relu'))
    model.add(BatchNormalization(momentum = 0.99))
    model.add(Dropout(hp.Float('dropout', 0, 0.5, step=0.1, default=0.5)))
    model.add(layers.Conv1D(filters=hp.Int(
    'num_filters',
    16, 128,
    step=16
),kernel_size=3,strides=1,padding='same',activation='relu'))
    model.add(BatchNormalization(momentum = 0.99))
    model.add(Dropout(hp.Float('dropout', 0, 0.5, step=0.1, default=0.5)))
    model.add(layers.Conv1D(filters=hp.Int(
    'num_filters',
    16, 128,
    step=16
),kernel_size=3,strides=1,padding='same',activation='relu'))
    model.add(BatchNormalization(momentum = 0.99))
    model.add(Dropout(hp.Float('dropout', 0, 0.5, step=0.1, default=0.5)))
    model.add(layers.Conv1D(filters=hp.Int(
    'num_filters',
    16, 128,
    step=16
),kernel_size=3,strides=1,padding='same',activation='relu'))
    model.add(BatchNormalization(momentum = 0.99))
    model.add(Dropout(hp.Float('dropout', 0, 0.5, step=0.1, default=0.5)))
    model.add(layers.Dense(units=hp.Int('units',min_value=16,max_value=512,step=32,default=128),activation = 'relu'))
    model.add(Dropout(hp.Float('dropout', 0, 0.5, step=0.1, default=0.5)))
    model.add(layers.Dense(1, activation = 'linear'))

    model.compile(
        optimizer='adam',
        loss=['mean_squared_error'],
        metrics=[tf.keras.metrics.RootMeanSquaredError()]
    )
    return …