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neuralnet, ваттность и перекрестная проверка

2016-04-11 3 views
0

У меня очень большой набор данных с 36 функциями, который включает в себя 6 выходных столбцов. Я пытаюсь выполнить MLP backpropagation neural network learning (Regression) в этом наборе данных, и я использую neuralnet и caret. Я хочу два скрытых слоя с 6 и 5 узлами в каждом слое. Я также хочу, чтобы добавить перекрестную проверку к кратной моей модели NNneuralnet, ваттность и перекрестная проверка

control <- trainControl(method="repeatedcv", number=5, repeats=1) 
    # train the model 
    model <- train(X,Y, method="neuralnet", 
       algorithm = "backprop", learningrate = 0.25,act.fct = 'tanh', 
       tuneGrid = data.frame(layer1 = 2:6, layer2 = 2:6, layer3 = 0),threshold = 0.1, trControl=control) 
warnings()

, где X и Y является функция и предсказатель кадром данных соответственно

но дает ошибку и предупреждение

Error in train.default(X, Y, method = "neuralnet", algorithm = "backprop", : 
    wrong model type for classification 
> warnings() 
Warning messages: 
1: In eval(expr, envir, enclos) : 
    model fit failed for Resample01: layer1=4, layer2=1, layer3=1 Error in if (reached.threshold < min.reached.threshold) { : 
    missing value where TRUE/FALSE needed 

2: In eval(expr, envir, enclos) : 
    model fit failed for Resample02: layer1=4, layer2=1, layer3=1 Error in if (reached.threshold < min.reached.threshold) { : 
    missing value where TRUE/FALSE needed 

3: In eval(expr, envir, enclos) : 
    model fit failed for Resample03: layer1=4, layer2=1, layer3=1 Error in if (reached.threshold < min.reached.threshold) { : 
    missing value where TRUE/FALSE needed 

4: In eval(expr, envir, enclos) : 
    model fit failed for Resample04: layer1=4, layer2=1, layer3=1 Error in if (reached.threshold < min.reached.threshold) { : 
    missing value where TRUE/FALSE needed 

5: In eval(expr, envir, enclos) : 
    model fit failed for Resample05: layer1=4, layer2=1, layer3=1 Error in if (reached.threshold < min.reached.threshold) { : 
    missing value where TRUE/FALSE needed 

6: In eval(expr, envir, enclos) : 
    model fit failed for Resample06: layer1=4, layer2=1, layer3=1 Error in if (reached.threshold < min.reached.threshold) { : 
    missing value where TRUE/FALSE needed 

7: In eval(expr, envir, enclos) : 
    model fit failed for Resample07: layer1=4, layer2=1, layer3=1 Error in if (reached.threshold < min.reached.threshold) { : 
    missing value where TRUE/FALSE needed 

8: In eval(expr, envir, enclos) : 
    model fit failed for Resample08: layer1=4, layer2=1, layer3=1 Error in if (reached.threshold < min.reached.threshold) { : 
    missing value where TRUE/FALSE needed 

9: In eval(expr, envir, enclos) : 
    model fit failed for Resample09: layer1=4, layer2=1, layer3=1 Error in if (reached.threshold < min.reached.threshold) { : 
    missing value where TRUE/FALSE needed 

10: In eval(expr, envir, enclos) : 
    model fit failed for Resample10: layer1=4, layer2=1, layer3=1 Error in if (reached.threshold < min.reached.threshold) { : 
    missing value where TRUE/FALSE needed 

11: In eval(expr, envir, enclos) : 
    model fit failed for Resample11: layer1=4, layer2=1, layer3=1 Error in if (reached.threshold < min.reached.threshold) { : 
    missing value where TRUE/FALSE needed 

12: In eval(expr, envir, enclos) : 
    model fit failed for Resample12: layer1=4, layer2=1, layer3=1 Error in if (reached.threshold < min.reached.threshold) { : 
    missing value where TRUE/FALSE needed 

13: In eval(expr, envir, enclos) : 
    model fit failed for Resample13: layer1=4, layer2=1, layer3=1 Error in if (reached.threshold < min.reached.threshold) { : 
    missing value where TRUE/FALSE needed 

14: In eval(expr, envir, enclos) : 
    model fit failed for Resample14: layer1=4, layer2=1, layer3=1 Error in if (reached.threshold < min.reached.threshold) { : 
    missing value where TRUE/FALSE needed 

15: In eval(expr, envir, enclos) : 
    model fit failed for Resample15: layer1=4, layer2=1, layer3=1 Error in if (reached.threshold < min.reached.threshold) { : 
    missing value where TRUE/FALSE needed 

16: In eval(expr, envir, enclos) : 
    model fit failed for Resample16: layer1=4, layer2=1, layer3=1 Error in if (reached.threshold < min.reached.threshold) { : 
    missing value where TRUE/FALSE needed 

17: In eval(expr, envir, enclos) : 
    model fit failed for Resample17: layer1=4, layer2=1, layer3=1 Error in if (reached.threshold < min.reached.threshold) { : 
    missing value where TRUE/FALSE needed 

18: In eval(expr, envir, enclos) : 
    model fit failed for Resample18: layer1=4, layer2=1, layer3=1 Error in if (reached.threshold < min.reached.threshold) { : 
    missing value where TRUE/FALSE needed 

19: In eval(expr, envir, enclos) : 
    model fit failed for Resample19: layer1=4, layer2=1, layer3=1 Error in if (reached.threshold < min.reached.threshold) { : 
    missing value where TRUE/FALSE needed 

20: In eval(expr, envir, enclos) : 
    model fit failed for Resample20: layer1=4, layer2=1, layer3=1 Error in if (reached.threshold < min.reached.threshold) { : 
    missing value where TRUE/FALSE needed 

21: In eval(expr, envir, enclos) : 
    model fit failed for Resample21: layer1=4, layer2=1, layer3=1 Error in if (reached.threshold < min.reached.threshold) { : 
    missing value where TRUE/FALSE needed 

22: In eval(expr, envir, enclos) : 
    model fit failed for Resample22: layer1=4, layer2=1, layer3=1 Error in if (reached.threshold < min.reached.threshold) { : 
    missing value where TRUE/FALSE needed 

23: In eval(expr, envir, enclos) : 
    model fit failed for Resample23: layer1=4, layer2=1, layer3=1 Error in if (reached.threshold < min.reached.threshold) { : 
    missing value where TRUE/FALSE needed 

24: In eval(expr, envir, enclos) : 
    model fit failed for Resample24: layer1=4, layer2=1, layer3=1 Error in if (reached.threshold < min.reached.threshold) { : 
    missing value where TRUE/FALSE needed 

25: In eval(expr, envir, enclos) : 
    model fit failed for Resample25: layer1=4, layer2=1, layer3=1 Error in if (reached.threshold < min.reached.threshold) { : 
    missing value where TRUE/FALSE needed 

26: In nominalTrainWorkflow(x = x, y = y, wts = weights, info = trainInfo, ... : 
    There were missing values in resampled performance measures.

источник

2016-04-11 Eka

+0

Ваше использование команды поезда неверно. вы получаете ошибки, если здесь указаны параметры настройки. В этом случае скрытые слои. – phiver

+0

Я отредактировал код с правильным параметром слоя, но все же его давая ошибку – Eka

+0

Каковы ваши фактические данные? У вас есть только 1 предиктор или x матрица с несколькими столбцами? Является ли ваш фактор Y? – Alos

ответ

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