如何与张量流交叉验证？

Question

如何与张量流交叉验证？以及如何优化以改进多变量回归模型）），但是交叉验证中的主要问题是

# Создаем последовательную модель
model = Sequential()
# Добавляем уровни сети
model.add(Dense(2048, input_dim=trainInput.shape[1], activation="relu"))
model.add(Dense(2048, activation="relu"))
model.add(Dense(1024, activation="relu"))
model.add(Dense(1024, activation="relu"))
model.add(layers.Dropout(0.1))
model.add(Dense(1))
# Компилируем сеть и задаем оптимизатор
optimizer = tf.keras.optimizers.Adam(learning_rate=0.0008)
model.compile(loss='mse', optimizer=optimizer, metrics=["mse", "mae"])
#print(model.summary())


class PrintDot(keras.callbacks.Callback):
  def on_epoch_end(self, epoch, logs):
    if epoch % 10 == 0: print('')
    print('.', end='')
EPOCHS = 50
BATCH = 30
early_stop = keras.callbacks.EarlyStopping(monitor='val_loss', patience = 3)
history = model.fit(
  trainInput, trainTarget, batch_size = BATCH,
  epochs=EPOCHS,  validation_data=(validationInput,validationTarget), verbose = 0,
 callbacks=[early_stop, PrintDot()])
# Вывод истории обучения
hist = pd.DataFrame(history.history)
hist['epoch'] = history.epoch
hist