R深度学习，多输出(R deep learning, multiple outputs)

是否有可能创建一个提供多个输出的深度学习网络？这样做的原因还在于尝试捕获输出之间的关系。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)

Is it possible to create a deep learning net that gives multiple outputs? The reason for doing this is to also try to capture the relationships between outputs. In the examples given I can only create one output.

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)

最满意答案

H2O目前不支持多个响应列（ H2O常见问题解答和H2O Google Group主题）。他们的建议是为每个响应训练一个新模型。

（荒谬的）例子：

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，似乎通过deepnet包可以获得多个响应（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))

It doesn't look like multiple response columns are currently supported in H2O (H2O FAQ and H2O Google Group topic). Their suggestion is to train a new model for each response.

(Nonsensical) example:

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)

However, it appears that multiple responses are available through the deepnet package (check 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))

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