- CUDA install
- MeCab install
git clone https://github.com/taku910/mecab.git
cd mecab/mecab
./configure
make
make check
make install
Assume cuMat directory is in the same row.
cp test.cpp.[Type of processing] test.cpp
make
export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:../cuMat
./test
- If you get an error, test.cpp:8:17: fatal error: png.h: No such file or directory
apt-get install libpng12-0
apt-get install libpng-dev
find / -name png.h
- install boost
apt-get install libboost-all-dev
- install utilities
apt update
apt install git
apt install net-tools
apt install net-tools
apt install vim-tiny
- autoencoder:
- cnn: CONVOLUTION NEURAL NETWORK
- iris: HIERARCHICAL PERCEPTRON BY IRIS DATA SET
- lstm.sin:sin wave reproduction with LSTM
- mlp:stratified interceptron by MNIST
- number:LSTM Counting
- seq2seq:LSTM Translation Model
http://yann.lecun.com/exdb/mnist/
train-images-idx3-ubyte.gz: training set images (9912422 bytes)
train-labels-idx1-ubyte.gz: training set labels (28881 bytes)
t10k-images-idx3-ubyte.gz: test set images (1648877 bytes)
t10k-labels-idx1-ubyte.gz: test set labels (4542 bytes)
Download and Extract to DNN Directory
https://www.cs.toronto.edu/~kriz/cifar.html
CIFAR-10 binary version (suitable for C programs)
Downloaded and extracted to DNN directory with data stored under cifar-10-batches-bin directory.
http://www.edrdg.org/wiki/index.php/Tanaka_Corpus
complete version (UTF-8) Download and extract to the DNN directory, including examples.utf.
Extract only the necessary part of examples.utf from norm_tanaka_corpus.py and create tanaka_corpus_e.txt and tanaka_corpus_j.txt.
Randomly extract 10000 items from the data created above with sample_tanaka_corpus.py and save them in a separate file (tanaka_corpus_e_10000.txt, tanaka_corpus_j_10000.txt). Rename this to use as training data (tanaka_corpus_e_10000.txt.train, tanaka_corpus_j_10000.txt.train)
Similarly, use sample_tanaka_corpus.py again to create data for evaluation.(tanaka_corpus_e_10000.txt.test, tanaka_corpus_j_10000.txt.test)