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# HW3 | ||
**Image Sentiment Classification** | ||
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> 本次作業為Image Sentiment Classification。我們提供給各位的training dataset為兩萬八千張左右48x48 pixel的圖片,以及每一張圖片的表情label(注意:每張圖片都會唯一屬於一種表情)。總共有七種可能的表情(0:生氣, 1:厭惡, 2:恐懼, 3:高興, 4:難過, 5:驚訝, 6:中立(難以區分為前六種的表情))。 | ||
> Testing data則是七千張左右48x48的圖片,希望各位同學能利用training dataset訓練一個CNN model,預測出每張圖片的表情label(同樣地,為0~6中的某一個)並存在csv檔中。 | ||
> 相關格式及報告說明請詳閱: | ||
> [PPT](https://docs.google.com/presentation/d/1QFK4-inv2QJ9UhuiUtespP4nC5ZqfBjd_jP2O41fpTc/edit#slide=id.p) | ||
> [作業網址](https://sunprinces.github.io/ML-Assignment3/index.html) | ||
> [注意] 這次作業希望大家在衝高Kaggle上Accuracy的同時,對訓練的model及預測的結果多做一些觀察(P3-P5),並在報告中多加詳述。 | ||
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## Kaggle Link | ||
<https://inclass.kaggle.com/c/ml2017-hw3> | ||
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## Data Link | ||
<https://drive.google.com/file/d/0B8Si647wj9ZoTHlJR1pDazUxSVE/view?usp=sharing> | ||
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## Reference | ||
<https://keras.io/#getting-started-30-seconds-to-keras> | ||
<https://keras.io/getting-started/sequential-model-guide/> | ||
<https://keras.io/layers/convolutional/#conv2d> | ||
<https://keras.io/losses/> | ||
<https://keras.io/optimizers/> | ||
<https://keras.io/models/sequential/> | ||
<https://keras.io/getting-started/faq/#how-can-i-save-a-keras-model> | ||
<https://keras.io/layers/core/> | ||
<https://keras.io/layers/advanced-activations/> | ||
<https://keras.io/models/model/> | ||
<https://keras.io/visualization/#model-visualization> | ||
<https://keras.io/callbacks/> | ||
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<https://docs.scipy.org/doc/numpy/reference/generated/numpy.save.html> | ||
<https://docs.scipy.org/doc/numpy/reference/generated/numpy.ndarray.squeeze.html> | ||
<https://docs.scipy.org/doc/numpy/reference/generated/numpy.argwhere.html> | ||
<https://docs.scipy.org/doc/numpy/reference/generated/numpy.argmax.html> | ||
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### Code Reference | ||
<https://github.com/fchollet/keras/blob/master/examples/mnist_cnn.py> | ||
<https://blog.keras.io/building-powerful-image-classification-models-using-very-little-data.html> | ||
<http:https://stackoverflow.com/questions/37674306/what-is-the-difference-between-same-and-valid-padding-in-tf-nn-max-pool-of-t> | ||
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<https://en.wikipedia.org/wiki/Test_set> | ||
<https://www.zhihu.com/question/23437871> | ||
<http:https://scikit-learn.org/stable/index.html> | ||
<https://en.wikipedia.org/wiki/Confusion_matrix> | ||
<http:https://scikit-learn.org/stable/modules/generated/sklearn.metrics.confusion_matrix.html> | ||
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<http:https://machinelearningmastery.com/a-gentle-introduction-to-scikit-learn-a-python-machine-learning-library/> | ||
<http:https://blog.fastforwardlabs.com/2016/02/24/hello-world-in-keras-or-scikit-learn-versus.html> | ||
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### Saliency map | ||
<https://github.com/raghakot/keras-vis/tree/master/vis> | ||
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### Visualize filters & outputs | ||
<https://blog.keras.io/how-convolutional-neural-networks-see-the-world.html> | ||
<https://github.com/fchollet/keras/blob/master/examples/conv_filter_visualization.py> | ||
<https://en.wikipedia.org/wiki/Median_filter> | ||
<https://docs.scipy.org/doc/scipy-0.16.1/reference/generated/scipy.ndimage.filters.median_filter.html> | ||
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### Papers | ||
<http:https://deeplearning.net/wp-content/uploads/2013/03/dlsvm.pdf> | ||
<https://arxiv.org/pdf/1608.02833.pdf> |