https://github.com/burness/tensorflow-101

learn code with tensorflow
https://github.com/burness/tensorflow-101

deeplearning flask gan infogan tensorflow tensorflow-experiments

Last synced: 2 days ago
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learn code with tensorflow

• Host: GitHub
• URL: https://github.com/burness/tensorflow-101
• Owner: burness
• Created: 2016-11-10T08:23:35.000Z (about 8 years ago)
• Default Branch: master
• Last Pushed: 2019-05-17T09:05:23.000Z (over 5 years ago)
• Last Synced: 2024-12-22T11:07:47.163Z (9 days ago)
• Topics: deeplearning, flask, gan, infogan, tensorflow, tensorflow-experiments
• Language: Python
• Size: 39.1 MB
• Stars: 1,113
• Watchers: 86
• Forks: 552
• Open Issues: 10
• Metadata Files:
  • Readme: README.md

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README

        
## algorithm

MLP for mnist done

## covert to tfrecord

test on 102flowers done

command like this:

`python covert_somedata_to_tfrecord.py --dataset_name=102flowers --dataset_dir='./'`

## serving

test on MLP model on mnist done

download the mnist jpgs on baidu yunpan [mnist jpgs](https://pan.baidu.com/s/1o8EWkVS)

![](./images/mnist_server.png)

![](./images/mnist_client_result.png)

Reference: [https://github.com/tobegit3hub/deep_recommend_system](https://github.com/tobegit3hub/deep_recommend_system)

## finetuning and deploy model with flask

![](./images/flask_with_pretrain_model.png)

![](./images/flask_with_pretrain_model_00.png)

In folder finetuning, we use tf.slim to finetuning the pretrain model (I use the same method in my porn detection) and use flask to buid a very simple inference system.

## Inference Demo

![](./images/demo_result.png)

I deploy a image classification in [demo page](http://demo.duanshishi.com). It is based on Tensorflow and Flask. Feel free to try.

## Chinese rec

![](./images/chinese_rec_example.png)

You can get the detailed introductation in [TensorFlow与中文手写汉字识别](http://hacker.duanshishi.com/?p=1753)

## GAN

### AC-GAN and Info-GAN in TensorFlow

The project in GAN includ AC-GAN and Info-GAN:

 - AC-GAN ![](./images/ac-gan-fig-01.png)

 - Info-GAN ![](./images/infogan-fig-01.png)

### Result

Infogan result:

![](./images/infogan-result.png)

![](./images/infogan-result-01.png)

AC-GAN result:

![](./images/acgan-result.png)

![](./images/acgan-result-01.png)

You can get detailed information in [GAN的理解与TF的实现](http://hacker.duanshishi.com/?p=1766)

## DeepColor

The images from [safebooru.org](http://safebooru.org), you can download from the baidu yunpan [https://pan.baidu.com/s/1c1HOIHU](https://pan.baidu.com/s/1c1HOIHU).

![](./images/mac_blogs_deepcolor-01.png)

The results:

![](./images/mac_blogs_deepcolor-03.png)

details in [CGAN之deepcolor实践](http://www.duanshishi.com/?p=1791)

## Text Classifer

You can get the detail information in [自然语言处理第一番之文本分类器](http://hacker.duanshishi.com/?p=1805).

Classic methods such as Word Count Distribution and TF-IDF and DeepLearning Methods based CNN and C-LSTM are provided in [nlp/text_classifier](https://github.com/burness/tensorflow-101/tree/master/nlp/text_classifier)