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是否有可能创建一个提供多个输出的深度学习网络？这样做的原因也是为了尝试捕捉输出之间的关系。在给出的示例中，我只能创建一个输出。

library(h2o)
localH2O = h2o.init()
irisPath = system.file("extdata", "iris.csv", package = "h2o")
iris.hex = h2o.importFile(localH2O, path = irisPath)
h2o.deeplearning(x = 1:4, y = 5, data = iris.hex, activation = "Tanh", 
             hidden = c(10, 10), epochs = 5)
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H2O（ H2O FAQ和H2O Google Group 主题）目前似乎不支持多响应列。他们的建议是为每个响应训练一个新模型。

（无意义的）示例：

library(h2o)
localH2O <- h2o.init()
irisPath <- system.file("extdata", "iris.csv", package = "h2o")
iris.hex <- h2o.importFile(localH2O, path = irisPath)

m1 <- h2o.deeplearning(x = 1:2, y = 3, data = iris.hex, activation = "Tanh", 
         hidden = c(10, 10), epochs = 5, classification = FALSE)
m2 <- h2o.deeplearning(x = 1:2, y = 4, data = iris.hex, activation = "Tanh", 
         hidden = c(10, 10), epochs = 5, classification = FALSE)

但是，似乎可以通过deepnet包获得多个响应（检查library(sos); findFn("deep learning")）。

library(deepnet)
x <- as.matrix(iris[,1:2])
y <- as.matrix(iris[,3:4])
m3 <- dbn.dnn.train(x = x, y = y, hidden = c(5,5))
于 2015-04-13T14:15:20.887 回答