Keras example codes

Dependencies

I recommend using anaconda3

Image
- CNN_MNIST
- variational auto encoder for CIFAR10
- Gumbel-softmax with variational auto encoder for MNIST
- residual CIFAR10
Text
- encoder-decoder
- Skip-Gram : not hieraltical softmax and negative sampling
Tutorial
- keras_tutorial : for Machine Learning seminar at Tsukuba (note: 7MB notebook and ja only)

autoencoder
MLP_MNIST
CNN_CIFAR10
CBoW : Normal softmax
RNN_binary_classification
NNLM
SCRNLM : Structurally Constrained Recurrent Nets Language Model
fastText : Bi-gram features and softmax
Skip-Gram&NG : Skip-gram with negative sampling
prodLDA : Product of experts model’s LDA based on VAE
sklearn_MLP_MNIST: Sklearn API example for parameter search on MNIST classification

Name		Name	Last commit message	Last commit date
Latest commit History 48 Commits
.gitignore		.gitignore
CBoW.ipynb		CBoW.ipynb
CNN_CIFAR10.ipynb		CNN_CIFAR10.ipynb
CNN_MNIST.ipynb		CNN_MNIST.ipynb
MLP_MNIST.ipynb		MLP_MNIST.ipynb
NNLM.ipynb		NNLM.ipynb
README.org		README.org
RNN_binary_classification.ipynb		RNN_binary_classification.ipynb
SCRNLM.ipynb		SCRNLM.ipynb
Skip-gram-with-NS.ipynb		Skip-gram-with-NS.ipynb
Skip-gram.ipynb		Skip-gram.ipynb
autoencoder.ipynb		autoencoder.ipynb
encoder-decoder.ipynb		encoder-decoder.ipynb
fasttext.ipynb		fasttext.ipynb
gumbel_softmax_vae_MNIST.ipynb		gumbel_softmax_vae_MNIST.ipynb
keras_tutorial.ipynb		keras_tutorial.ipynb
prodLDA.ipynb		prodLDA.ipynb
residual_cifar10.ipynb		residual_cifar10.ipynb
sklearn_MLP_MNIST.ipynb		sklearn_MLP_MNIST.ipynb
tweet2vec.ipynb		tweet2vec.ipynb
vae_cifar10_z2.ipynb		vae_cifar10_z2.ipynb