我正在使用LSTM网络进行时间序列预测。我正在使用keras tunerforhyperparameters tuning但我总是得到一个空精度，如下面的代码所示。任何帮助都会很棒。
tuner_H = Hyperband(
build_model,
max_epochs=20,
objective='val_accuracy',
seed=1,
executions_per_trial=10,
directory='hyperband',
project_name='cifar10'
)
tuner_H.search(X_train, Y_train, epochs= 20, validation_split=0.1, verbose = 1)
Epoch 1/3
283/283 [==============================] - 2s 5ms/step - loss: 0.1571 - accuracy: 0.0000e+00 - val_loss: 0.2332 - val_accuracy: 0.0000e+00
Epoch 2/3
283/283 [==============================] - 1s 4ms/step - loss: 0.1502 - accuracy: 0.0000e+00 - val_loss: 0.2236 - val_accuracy: 0.0000e+00
Epoch 3/3
283/283 [==============================] - 1s 4ms/step - loss: 0.1439 - accuracy: 0.0000e+00 - val_loss: 0.2142 - val_accuracy: 0.0000e+00
Epoch 1/3
283/283 [==============================] - 2s 5ms/step - loss: 0.1497 - accuracy: 0.0000e+00 - val_loss: 0.2217 - val_accuracy: 0.0000e+00
Epoch 2/3
283/283 [==============================] - 1s 4ms/step - loss: 0.1427 - accuracy: 0.0000e+00 - val_loss: 0.2116 - val_accuracy: 0.0000e+00
Epoch 3/3
283/283 [==============================] - 1s 4ms/step - loss: 0.1356 - accuracy: 0.0000e+00 - val_loss: 0.2010 - val_accuracy: 0.0000e+00
Epoch 1/3
283/283 [==============================] - 2s 5ms/step - loss: 0.1490 - accuracy: 0.0000e+00 - val_loss: 0.2201 - val_accuracy: 0.0000e+00
Epoch 2/3
283/283 [==============================] - 1s 4ms/step - loss: 0.1418 - accuracy: 1.1058e-04 - val_loss: 0.2098 - val_accuracy: 0.0000e+00
Epoch 3/3
283/283 [==============================] - 1s 4ms/step - loss: 0.1352 - accuracy: 0.0000e+00 - val_loss: 0.1996 - val_accuracy: 0.0000e+00
Epoch 1/3
283/283 [==============================] - 2s 5ms/step - loss: 0.1472 - accuracy: 0.0000e+00 - val_loss: 0.2172 - val_accuracy: 0.0000e+00
Epoch 2/3
283/283 [==============================] - 1s 4ms/step - loss: 0.1402 - accuracy: 0.0000e+00 - val_loss: 0.2066 - val_accuracy: 0.0000e+00
Epoch 3/3
283/283 [==============================] - 1s 4ms/step - loss: 0.1332 - accuracy: 0.0000e+00 - val_loss: 0.1960 - val_accuracy: 0.0000e+00.........................