tensorflow

description

• test code for tensorflow
• tensorflow
  • version : tf 1.0

simple test code

• test1.py
• test2.py
• test3.py
• test4.py
• test5.py
• test6.py
• test7.py
• test8.py
• test9.py
• test10.py
• test11.py
• test12.py

tutorial video by hunkim(in korean)

• linear regression
  • predict real value
  • code
    • linear_regression1.py
    • linear_regression2.py
    • linear_regression3.py
    • linear_regression4.py
    • train_linear.txt
• logistic regression
  • binary classification 0 or 1
  • code
    • logistic_regression.py
    • train_logistic.txt
    • xor_logistic_regression.py
    • xor_logistic_regression_onelayer.py
• softmax regression
  • multinomial (logistic) classification
  • code
    • softmax_regression.py
    • train_softmax.txt
• RNN
  • recurrent neural network
  • code
    • simple_rnn.py
• tutorial git

IRIS softmax regression test code

• code
  • softmax_regression_iris_train.py
  • softmax_regression_iris_inference.py
  • train_iris.txt
  • training accuracy : around 96%
  • for other traning data

  - convert training data format into the format like train_iris.txt
  - modify softmax_regression3_train.py
  - train and save model
  - modify softmax_regression3_inference.py
  - restore model and test inference
  - these steps are basic usage what you are familar with

IRIS multi-layer perceptron test code

• code
  • mlp_iris.py
    • accuracy : around 96%

MNIST softmax regression test code

• download MNIST data from http://yann.lecun.com/exdb/mnist/
• code
  • softmax_regression_mnist.py
    • accuracy : around 92%
• softmax regression is same as :
  • multinomial logistic regression
  • maximum entropy classifier
  • neural net without hidden layer

MNIST multi-layer perceptron test code

• code
  • mlp_mnist_train.py
  • mlp_mnist_inference.py
    • accuracy : around 98%
• distributed version
  • mlp_mnist_dist.py
  • training using parameter servers and workers

  $ ./mlp_mnist_dist.sh -v -v
  
  # worker0 log
  job : worker/0 step :  0 ,training accuracy : 0.9
  job : worker/0 step :  100 ,training accuracy : 0.9
  job : worker/0 step :  200 ,training accuracy : 0.86
  job : worker/0 step :  300 ,training accuracy : 0.9
  ...
  
  # worker1 log
  job : worker/1 step :  0 ,training accuracy : 0.12
  job : worker/1 step :  0 ,training accuracy : 0.14
  job : worker/1 step :  300 ,training accuracy : 0.82
  job : worker/1 step :  500 ,training accuracy : 0.92
  job : worker/1 step :  600 ,training accuracy : 0.94
  ....

  • accuracy : 0.9604
  • if you have a trouble like 'failed to connect...', read

MNIST convolutaional neural network test code

• code
  • conv_mnist1.py
    • accuracy : around 99%
  • conv_mnist2.py
    • accuracy : around 98.15%

MNIST LSTM(recurrent neural network) test code

• code
  • lstm_mnist.py
    • accuracy : around 97%

RNN

• code
  • simple_lstm.py
• segmentation(auto-spacing) using lstm
• char-rnn

word2vec

• vector representation of words

tensorflow serving

• setup tensorflow serving
  • if you have trouble on installing gRPC, see http://dchua.com/2016/04/08/installing-grpc,-protobuf-and-its-dependencies-for-python-development/

  $ sudo find /usr/lib -name "*protobuf*" -delete
  $ git clone https://github.com/grpc/grpc.git
  $ cd grpc/
  $ git submodule update --init
  $ cd third_party/protobuf
  # install autoconf, libtool (on OS X)
  $ brew install autoconf && brew install libtool
  $ ./autogen.sh
  # if you got an error related to 'libtool' on OS X, edit Makefile to use '/usr/bin/libtool' instead of '/usr/local/bin/libtool'
  $ ./configure; make; sudo make install
  $ cd python
  $ python setup.py build; python setup.py test; sudo python setup.py install --user
  $ cd ../../..
  $ make; sudo make install
  $ which grpc_python_plugin
  $ pip install grpcio --user

• serving basic

data preprocessing(tf.SequenceExample)

• RNNS IN TENSORFLOW, A PRACTICAL GUIDE AND UNDOCUMENTED FEATURES
  • in korean

model dump and inference

• simple example
  • dump_model.py
  • inference_model.py
• build api using keras with tf backend
  • reference

references

• RNN
• LSTM
• Word2Vec
• tensorflow
  • tensorflow tutorial
  • tensorflow mnist inference test using web interface
  • distributed tensorflow
  • distributed tensorflow, a gentle introduction
  • tensorboard
  • tensorflow C++ image recognition demo
• gpu check
  • command

  $ lspci
  $ nvidia-smi
  $ cat /proc/driver/nvidia/gpus/0/information
  $ cat /proc/driver/nvidia/gpus/1/information
  $ cat /usr/local/cuda/include/cudnn.h | grep CUDNN_MAJOR -A 2