如何从R中知道完成了多少个深度学习时代? [英] How do know how many deep learning epochs were done, from R?

问题描述

默认情况下，h2o.deeplearning()的提前停止处于打开状态.但是，从R中，我如何确定它是否停止得早，以及它发生了多少个时期?我已经尝试过了:

Early stopping is turned on by default for h2o.deeplearning(). But, from R, how do I find out if it did stop early, and how many epochs it did? I've tried this:

model = h2o.deeplearning(...) print(model)

model = h2o.deeplearning(...) print(model)

它告诉我有关层，MSE，R2等的信息，但与运行多少纪元无关.

which tells me information on the layers, the MSE, R2, etc. but nothing about how many epochs were run.

在Flow上，我可以看到信息(例如，"Scoring History-Deviance"图表或"Scoring History"表中x轴的位置).

Over on Flow I can see the information (e.g. where the x-axis stops in the "Scoring History - Deviance" chart, or in the Scoring History table).

推荐答案

如果您的模型称为m，则只需获取训练的纪元数:last(m@model$scoring_history$epochs)

If your model is called m, then to get just the number of epochs trained: last(m@model$scoring_history$epochs)

要查看还有哪些其他信息(实际上是您可以在Flow界面中看到的所有信息)以及如何访问它，请使用str(m)

To see what other information is available (which is literally everything you can see in the Flow interface) and how to access it, use str(m)

还请注意以下命令:summary(m)除了print(m)显示的内容外，它还添加了此部分(用于深度学习模型):

Also be aware of this command: summary(m) In addition to what is shown with print(m) it adds this section (for a deeplearning model):

Scoring History: 
            timestamp   duration training_speed    epochs iterations       samples training_MSE training_deviance training_r2
1 2016-04-14 11:35:46  0.000 sec                  0.00000          0      0.000000
2 2016-04-14 11:35:52  5.218 sec 15139 rows/sec  10.00000          1  77150.000000      0.00000           0.00000     0.07884
...
7 2016-04-14 11:36:18 31.346 sec 25056 rows/sec 100.00000         10 771500.000000      0.00000           0.00000     0.72245

即您可以通过查看最后一行来查看纪元总数.

I.e. You can see total number of epochs by looking at the last row.

顺便说一句，当应用于数据 frame 时，这与h2o的summary()命令不同；在这种情况下，它的行为类似于R的内置摘要功能，并显示数据框中每一列的统计信息.

BTW, this is different to h2o's summary() command when applied to a data frame; in that case it behaves like R's built-in summary function, and shows statistics on each column in the data frame.

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