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Deep convolution/recurrent neural network project with TensorFlow

License: Apache License 2.0

Shell 0.87% Java 0.23% Protocol Buffer 0.47% Python 44.59% C++ 43.04% C 0.30% CMake 0.32% Objective-C 0.02% Objective-C++ 0.22% Makefile 0.08% Jupyter Notebook 6.23% HTML 1.78% Go 0.29% JavaScript 0.04% TypeScript 1.50% Batchfile 0.01%

deep_cnn's Introduction

Deep CNN and RNN

The classic deep convolution and recurrent neural network project with TensorFlow. You can learn and run CNN easily.

Train/test/inference images included.
Visualize images with matplotlib.
Support checkpoint and tensorboard.
Easy to extend more convolution layers.
Support CNN, LSTM, Bicondictional LSTM and Stacked LSTM.

Train

./pokemon_classifer.py --epoch_number 100

Inference

./pokemon_classifer.py --mode inference --image ./data/inference/Pikachu.png

Export model

./pokemon_classifer.py --epoch_number 0

Run TensorFlow serving

./tensorflow_model_server --port=9000 --model_name=deep_cnn --model_base_path=./model

Run gRPC client

./predict_client.py --host 127.0.0.1 --port 9000 --model_name deep_cnn --model_version 1

Notice that cloudml is not released now.

cloudml models predict -n deep_cnn -s 127.0.0.1:9000 -f ./data.json

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