首页 \ 问答 \ 我怎样才能告诉h2o深度学习网格有AUC而不是剩余偏差(How can I tell h2o deep learning grid to have AUC instead of residual deviance)

我怎样才能告诉h2o深度学习网格有AUC而不是剩余偏差(How can I tell h2o deep learning grid to have AUC instead of residual deviance)

 我想通过寻找AUC或准确度来测量模型性能。 在网格搜索中，我得到residual deviance结果，我怎样才能告诉h2o深度学习网格有AUC而不是剩余偏差，并将结果显示为如下所示？  
train <- read.table(text = "target birds    wolfs     snakes
                              0        9         7 a
                              0        8         4 b
                              1        2         8 c
                              1        2         3 a
                              1        8         3 a
                              0        1         2 a
                              0        7         1 b
                              0        1         5 c
                              1        9         7 c
                              1        8         7 c
                              0        2         7 b
                              1        2         3 b
                              1        6         3 c
                              0        1         1 a
                              0        3         9 a
                              1        1         1 b ",header = TRUE)
trainHex <- as.h2o(train)

g <- h2o.grid("deeplearning",
              hyper_params = list(
                  seed = c(123456789,12345678,1234567),
                  activation = c("Rectifier", "Tanh", "TanhWithDropout", "RectifierWithDropout", "Maxout", "MaxoutWithDropout")
              ),
              reproducible = TRUE,
              x = 2:4,
              y = 1,
              training_frame = trainHex,
              validation_frame = trainHex,
              epochs = 50,
              )
g
model_ids <- g@summary_table
model_ids<-as.data.frame(model_ids)
 
 我得到的结果表：  
     Hyper-Parameter Search Summary: ordered by increasing residual_deviance
             activation      seed                                                  model_ids   residual_deviance
1                Maxout  12345678 Grid_DeepLearning_train_model_R_1483217086840_112_model_10 0.07243775676256235
2                Maxout   1234567 Grid_DeepLearning_train_model_R_1483217086840_112_model_16 0.10060885040861599
3     MaxoutWithDropout 123456789  Grid_DeepLearning_train_model_R_1483217086840_112_model_5  0.1706496158406441
4                Maxout 123456789  Grid_DeepLearning_train_model_R_1483217086840_112_model_4 0.17243125875659948
5                  Tanh 123456789  Grid_DeepLearning_train_model_R_1483217086840_112_model_1 0.18326527198894926
6                  Tanh  12345678  Grid_DeepLearning_train_model_R_1483217086840_112_model_7 0.18763395264761593
7                  Tanh   1234567 Grid_DeepLearning_train_model_R_1483217086840_112_model_13 0.18791531211136187
8       TanhWithDropout 123456789  Grid_DeepLearning_train_model_R_1483217086840_112_model_2 0.19808063817007837
9       TanhWithDropout  12345678  Grid_DeepLearning_train_model_R_1483217086840_112_model_8 0.19815190962052193
10      TanhWithDropout   1234567 Grid_DeepLearning_train_model_R_1483217086840_112_model_14 0.19832946889767458
11            Rectifier 123456789  Grid_DeepLearning_train_model_R_1483217086840_112_model_0 0.20679125165086842
12    MaxoutWithDropout   1234567 Grid_DeepLearning_train_model_R_1483217086840_112_model_17 0.21971759565380736
13 RectifierWithDropout 123456789  Grid_DeepLearning_train_model_R_1483217086840_112_model_3 0.22337599298253263
14    MaxoutWithDropout  12345678 Grid_DeepLearning_train_model_R_1483217086840_112_model_11 0.22440661112729862
15 RectifierWithDropout   1234567 Grid_DeepLearning_train_model_R_1483217086840_112_model_15  0.2284671685474275
16 RectifierWithDropout  12345678  Grid_DeepLearning_train_model_R_1483217086840_112_model_9 0.23163744415703522
17            Rectifier   1234567 Grid_DeepLearning_train_model_R_1483217086840_112_model_12  0.2516917276707789
18            Rectifier  12345678  Grid_DeepLearning_train_model_R_1483217086840_112_model_6  0.2642221616447725

I would like to measure models performance by looking for AUC or Accuracy. In the grid search I get results with residual deviance,how can I tell h2o deep learning grid to have AUC instead of residual deviance and present the results as atable like the one attached below ? 
train <- read.table(text = "target birds    wolfs     snakes
                              0        9         7 a
                              0        8         4 b
                              1        2         8 c
                              1        2         3 a
                              1        8         3 a
                              0        1         2 a
                              0        7         1 b
                              0        1         5 c
                              1        9         7 c
                              1        8         7 c
                              0        2         7 b
                              1        2         3 b
                              1        6         3 c
                              0        1         1 a
                              0        3         9 a
                              1        1         1 b ",header = TRUE)
trainHex <- as.h2o(train)

g <- h2o.grid("deeplearning",
              hyper_params = list(
                  seed = c(123456789,12345678,1234567),
                  activation = c("Rectifier", "Tanh", "TanhWithDropout", "RectifierWithDropout", "Maxout", "MaxoutWithDropout")
              ),
              reproducible = TRUE,
              x = 2:4,
              y = 1,
              training_frame = trainHex,
              validation_frame = trainHex,
              epochs = 50,
              )
g
model_ids <- g@summary_table
model_ids<-as.data.frame(model_ids)
 
The results table that I got: 
     Hyper-Parameter Search Summary: ordered by increasing residual_deviance
             activation      seed                                                  model_ids   residual_deviance
1                Maxout  12345678 Grid_DeepLearning_train_model_R_1483217086840_112_model_10 0.07243775676256235
2                Maxout   1234567 Grid_DeepLearning_train_model_R_1483217086840_112_model_16 0.10060885040861599
3     MaxoutWithDropout 123456789  Grid_DeepLearning_train_model_R_1483217086840_112_model_5  0.1706496158406441
4                Maxout 123456789  Grid_DeepLearning_train_model_R_1483217086840_112_model_4 0.17243125875659948
5                  Tanh 123456789  Grid_DeepLearning_train_model_R_1483217086840_112_model_1 0.18326527198894926
6                  Tanh  12345678  Grid_DeepLearning_train_model_R_1483217086840_112_model_7 0.18763395264761593
7                  Tanh   1234567 Grid_DeepLearning_train_model_R_1483217086840_112_model_13 0.18791531211136187
8       TanhWithDropout 123456789  Grid_DeepLearning_train_model_R_1483217086840_112_model_2 0.19808063817007837
9       TanhWithDropout  12345678  Grid_DeepLearning_train_model_R_1483217086840_112_model_8 0.19815190962052193
10      TanhWithDropout   1234567 Grid_DeepLearning_train_model_R_1483217086840_112_model_14 0.19832946889767458
11            Rectifier 123456789  Grid_DeepLearning_train_model_R_1483217086840_112_model_0 0.20679125165086842
12    MaxoutWithDropout   1234567 Grid_DeepLearning_train_model_R_1483217086840_112_model_17 0.21971759565380736
13 RectifierWithDropout 123456789  Grid_DeepLearning_train_model_R_1483217086840_112_model_3 0.22337599298253263
14    MaxoutWithDropout  12345678 Grid_DeepLearning_train_model_R_1483217086840_112_model_11 0.22440661112729862
15 RectifierWithDropout   1234567 Grid_DeepLearning_train_model_R_1483217086840_112_model_15  0.2284671685474275
16 RectifierWithDropout  12345678  Grid_DeepLearning_train_model_R_1483217086840_112_model_9 0.23163744415703522
17            Rectifier   1234567 Grid_DeepLearning_train_model_R_1483217086840_112_model_12  0.2516917276707789
18            Rectifier  12345678  Grid_DeepLearning_train_model_R_1483217086840_112_model_6  0.2642221616447725

原文：https://stackoverflow.com/questions/41439307

更新时间：2023-06-25 17:06

最满意答案

 如果您发出HEAD请求而不是GET请求，您可能会得到您所追求的内容：  
NSMutableURLRequest *request = [[NSURLRequest requestWithURL:url] mutableCopy];
[request setHTTPMethod:@"HEAD"];
[request autorelease];

If you make a HEAD request instead of a GET request, you'll probably get what you're after: 
NSMutableURLRequest *request = [[NSURLRequest requestWithURL:url] mutableCopy];
[request setHTTPMethod:@"HEAD"];
[request autorelease];

我怎样才能告诉h2o深度学习网格有AUC而不是剩余偏差(How can I tell h2o deep learning grid to have AUC instead of residual deviance)

最满意答案

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