complete mlp & cnn on CIFAR10

Author: HugoPhi

.gitignore

@@ -0,0 +1,166 @@
+*/data/
+data.zip
+src/hw1/knn/data/
+
+# Byte-compiled / optimized / DLL files
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README.md

@@ -58,6 +58,7 @@ On MNIST: (a) acc[96.80%] & loss vs. epochs for mlp; (b) acc[97.86%] & loss vs.
   - Mini-Batch GD
   - Adam[6]
 - utils
+  - one hot
   - softmax
   - cross_entropy_loss
   - mean_square_error
@@ -71,4 +72,4 @@ On MNIST: (a) acc[96.80%] & loss vs. epochs for mlp; (b) acc[97.86%] & loss vs.
 [3] [Pascanu, R., Mikolov, T., & Bengio, Y. (2013). On the difficulty of training recurrent neural networks. Proceedings of the 30th International Conference on Machine Learning (ICML), 1310–1318.](https://arxiv.org/abs/1211.5063)    
 [4] [Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., & Polosukhin, I. (2017). Attention is All You Need. In Advances in Neural Information Processing Systems (NeurIPS 2017).](https://arxiv.org/abs/1706.03762)   
 [5] [Chen, T. Q., Rubanova, Y., Bettencourt, J., & Duvenaud, D. (2018). Neural Ordinary Differential Equations. Advances in Neural Information Processing Systems, 31.](https://arxiv.org/abs/1806.07366?spm=5176.28103460.0.0.40f7451eXLzPoY&file=1806.07366)   
-[6] [Kingma, D. P., & Ba, J. (2014). Adam: A Method for Stochastic Optimization. Proceedings of the International Conference on Learning Representations (ICLR).](https://arxiv.org/abs/1412.6980?spm=5176.28103460.0.0.40f7451eXLzPoY&file=1412.6980)   
+[6] [Kingma, D. P., & Ba, J. (2014). Adam: A Method for Stochastic Optimization. Proceedings of the International Conference on Learning Representations (ICLR).](https://arxiv.org/abs/1412.6980?spm=5176.28103460.0.0.40f7451eXLzPoY&file=1412.6980)