Kaggle_Galaxy_Zoo
所属分类:Leetcode/题库
开发工具:Python
文件大小:18KB
下载次数:0
上传日期:2014-07-14 23:52:57
上 传 者:
sh-1993
说明: 卡格尔银河动物园的Python和Theano代码-银河挑战
(Python & Theano code for Kaggle s Galaxy Zoo - The Galaxy Challenge)
文件列表:
helper_function.py (8204, 2014-07-15)
layer_factory.py (22056, 2014-07-15)
train_CNN.py (38161, 2014-07-15)
# Kaggle's Galaxy Zoo - The Galaxy Challenge
This is the Python & Theano code I used to make my submission to [Kaggle's
Galaxy Zoo - The Galaxy Challenge](http://www.kaggle.com/c/galaxy-zoo-the-galaxy-challenge/)
For this competition, I use a (deep) neural network architecture with a
convolutional neural network (CNN) followed by a multilayer perceptron (MLP).
In specific, I use CNN as first layers to process the galaxy images, and to
extract useful and learnable features, which are then fed to MLP. The whole
neural network are trained with back-propagation with MSE as cost function.
My score on the private leaderboard is RMSE = 0.10494, ranking 60th out of 331. [With an average of few different NN's, I obtain 0.10379, which is my best private score]. With a few epoch [e.g., 5~10] through the whole training set, one can easily obtain RMSE = 0.11xxx. With a few more, you can get to 0.10xxx, which seems
the limit of my approach and hard to break! I would appreciate if anyone tell me how to break it, anything regarding to the network architecture or parameter configurations etc.
Update: The winning solution for this competition is [here](http://benanne.github.io/2014/04/05/galaxy-zoo.html). **ConvNet is again the winner for such task!!**
The code itself contains lots of comments. So, you'd better see there for details.
## Requirements
You should have theano, numpy, pandas, cv2 installed in python.
## Instructions
* download data from the [competition website](http://www.kaggle.com/c/galaxy-zoo-the-galaxy-challenge/)
* put these code and the following folders/files in the same dir
- `images_traing_rev1`
- `images_test_rev1`
- `training_solutions_rev1.csv`
- `central_pixel_benchmark.csv`
* run `train_CNN.py`
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