{ "id": 6734, "title": "Logistic regression layer cost function?", "url": "/competitions/galaxy-zoo-the-galaxy-challenge/discussion/6734", "author_name": "", "post_date": "2014-01-01T02:31:02.847Z", "votes": null, "comment_count": 5, "views": 2246, "content": "

I've been using this competition to work through the deep learning tutorial using Theano. The tutorial using the MNIST dataset, which of course has discrete classes.

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In this case, we're learning and predicting the probabilities themselves. The tutorial uses negative log likelihood for a cost function, defined as

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-T.mean(T.log(self.p_y_given_x)[T.arange(y.shape[0]), y])

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Since we're trying to minimize the difference between the output of p_y_given_x and y (predict the correct likelihood interval), I tried this cost function:

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return -T.mean(T.log(T.abs_(self.p_y_given_x - y)))

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But that failed to converge.

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Am I thinking about this wrong? I'm having trouble coming up with another cost function.

", "messages": [ { "id": "36887", "postDate": "01/01/2014 02:31:02", "content": "

I've been using this competition to work through the deep learning tutorial using Theano. The tutorial using the MNIST dataset, which of course has discrete classes.

\n

In this case, we're learning and predicting the probabilities themselves. The tutorial uses negative log likelihood for a cost function, defined as

\n

-T.mean(T.log(self.p_y_given_x)[T.arange(y.shape[0]), y])

\n

Since we're trying to minimize the difference between the output of p_y_given_x and y (predict the correct likelihood interval), I tried this cost function:

\n

return -T.mean(T.log(T.abs_(self.p_y_given_x - y)))

\n

But that failed to converge.

\n

 

\n

Am I thinking about this wrong? I'm having trouble coming up with another cost function.

", "rawMarkdown": "", "votes": null }, { "id": "36890", "postDate": "01/01/2014 11:12:51", "content": "

Since the cost function the competition is being evaluated with is RMSE, it's a good idea to just use that, in my opinion :) There's no need to use a surrogate loss because the (R)MSE is easy to optimize.

\n

Also, due to the decision tree weighting scheme used, only the targets for questions 1 and 6 can really be thought of as probabilities. The other columns are products of probabilities.

", "rawMarkdown": "", "votes": null }, { "id": "36894", "postDate": "01/01/2014 17:52:14", "content": "

Thanks for the tips. I was mistaken about my contrived cost function not converging at all. A combination of two things made it appear to be randomly fluctuating: my impatience to let it run long enough before deciding that it was not converging, and also I was running on a very small sample (40 images) with a batch size of 20, which gave it the appears of flip-flopping, because indeed it was.

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However, you are absolutely right about not using a proxy cost function. RSME is the way to go.

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Are you doing anything special in regards to the solutions being results of a decision tree or can I rely on the network to figure that out by just training with the RSME across all the solutions?

", "rawMarkdown": "", "votes": null }, { "id": "36896", "postDate": "01/01/2014 19:51:44", "content": "

At the moment it seems that the data does not actually obey this decision tree structure all that well in practice ( see this post: http://www.kaggle.com/c/galaxy-zoo-the-galaxy-challenge/forums/t/6706/is-question-6-also-answered-for-stars-artifacts-answer-1-3/36798#post36798 ).

\n

So far I've tried both approaches taking into account these constraints, and approaches ignoring them, for now there is no clear winner.

", "rawMarkdown": "", "votes": null }, { "id": "36900", "postDate": "01/02/2014 01:13:36", "content": "

What do you use for your class memberships for the training set for this sort of model?

\n

I see how to use the RMSE in place of the negative_log_likelihood method, but the model itself predicts which class each image falls into, and we are not determining a class but instead 37 values between 0 and 1.  

", "rawMarkdown": "", "votes": null }, { "id": "36901", "postDate": "01/02/2014 01:23:08", "content": "

The output of p_y_given_x gives the probabilities for classes 1-37: just what you want. Normally, a prediction function would just take the argmax of that.

", "rawMarkdown": "", "votes": null } ], "comments": [ { "id": 36890, "author_name": "sedielem", "author_url": "", "post_date": "01/01/2014 11:12:51", "content": "

Since the cost function the competition is being evaluated with is RMSE, it's a good idea to just use that, in my opinion :) There's no need to use a surrogate loss because the (R)MSE is easy to optimize.

\n

Also, due to the decision tree weighting scheme used, only the targets for questions 1 and 6 can really be thought of as probabilities. The other columns are products of probabilities.

", "votes": null, "replies": [] }, { "id": 36894, "author_name": "zacstewart", "author_url": "", "post_date": "01/01/2014 17:52:14", "content": "

Thanks for the tips. I was mistaken about my contrived cost function not converging at all. A combination of two things made it appear to be randomly fluctuating: my impatience to let it run long enough before deciding that it was not converging, and also I was running on a very small sample (40 images) with a batch size of 20, which gave it the appears of flip-flopping, because indeed it was.

\n

However, you are absolutely right about not using a proxy cost function. RSME is the way to go.

\n

Are you doing anything special in regards to the solutions being results of a decision tree or can I rely on the network to figure that out by just training with the RSME across all the solutions?

", "votes": null, "replies": [] }, { "id": 36896, "author_name": "sedielem", "author_url": "", "post_date": "01/01/2014 19:51:44", "content": "

At the moment it seems that the data does not actually obey this decision tree structure all that well in practice ( see this post: http://www.kaggle.com/c/galaxy-zoo-the-galaxy-challenge/forums/t/6706/is-question-6-also-answered-for-stars-artifacts-answer-1-3/36798#post36798 ).

\n

So far I've tried both approaches taking into account these constraints, and approaches ignoring them, for now there is no clear winner.

", "votes": null, "replies": [] }, { "id": 36900, "author_name": "erikgregory", "author_url": "", "post_date": "01/02/2014 01:13:36", "content": "

What do you use for your class memberships for the training set for this sort of model?

\n

I see how to use the RMSE in place of the negative_log_likelihood method, but the model itself predicts which class each image falls into, and we are not determining a class but instead 37 values between 0 and 1.  

", "votes": null, "replies": [] }, { "id": 36901, "author_name": "zacstewart", "author_url": "", "post_date": "01/02/2014 01:23:08", "content": "

The output of p_y_given_x gives the probabilities for classes 1-37: just what you want. Normally, a prediction function would just take the argmax of that.

", "votes": null, "replies": [] } ], "raw_markdown_by_id": { "36887": "", "36890": "", "36894": "", "36896": "", "36900": "", "36901": "" }, "source": "meta" }