kerasCompileResult | R Documentation |
Compile a matrix with training, validation, and test results
kerasCompileResult(y, kerasConf)
y |
(1x6)-dim matrix with the following entries: |
kerasConf |
keras configuration generated with |
All values should be minimized: accuracies will be negative. The (1x7)-dim result matrix has the following entries
returnValue:
Metric used for optimization. Default: "validationLoss"
.
trainingLoss:
training loss.
negTrainingAccuracy:
negative training accuracy.
validationLoss:
validation loss.
negValidationAccuracy:
negative validation accuracy.
testLoss:
test loss.
negTestAccuracy:
negative test accuracy.
result matrix
evalKerasMnist
funKerasMnist
x <- 1 testLoss <- x negTestAccuracy <- 1-x validationLoss <- x/2 negValidationAccuracy <- 1-x/2 trainingLoss <- x/3 negTrainingAccuracy <- 1-x/3 y <- matrix(c(trainingLoss, negTrainingAccuracy, validationLoss, negValidationAccuracy, testLoss, negTestAccuracy), 1,6) kerasConf <- list() kerasConf$returnValue <- "testLoss" sum(kerasCompileResult(y, kerasConf)) == 4 kerasConf$returnValue <- "negTestAccuracy" sum(kerasCompileResult(y, kerasConf)) == 3 kerasConf$returnValue <- "validationLoss" sum(kerasCompileResult(y, kerasConf))*2 == 7 kerasConf$returnValue <- "negValidationAccuracy" sum(kerasCompileResult(y, kerasConf))*2 == 7 kerasConf$returnValue <- "trainingLoss" sum(kerasCompileResult(y, kerasConf))*3 == 10 kerasConf$returnValue <- "negTrainingAccuracy" sum(kerasCompileResult(y, kerasConf))*3 == 11
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