1. Move string resouce from source code to json.

2. Add CIFAR-100 dataset

Author: whwnsdlr1

README.md

@@ -2,7 +2,22 @@
 
 This is a client web application to demonstrate a simple deployment of a classification network using tensorflow.js
 <br />
-currently testing handwriting-number and photograpy possible
+currently test available on model trained by MNIST, CIFAR-100 and ImageNet.
+<br />
+<br />
+always welcome to issue & PR to fix the awkward and wrong parts of the text and complement for the lack of content.
+<br />
+edit the value of the html-key in /frontend/src/assets/strings/*.json.
+<br />
+String elements in the array are combined with line breaks and can contain html tags within the string.
+<br />
+<br />
+본문 중 어색한 부분, 잘못된 부분에 대한 내용 수정과 부족한 내용 보충을 위한 issue & PR은 언제나 환영입니다.
+<br />
+/frontend/src/assets/strings/*.json의 html-key의 value를 편집하면 됩니다.
+<br />
+배열의 문자열 엘리먼트들은 줄바꿈으로 결합되며 문자열안에 html 태그를 포함 할 수 있습니다.
+
 
 ## Demo
 you can online demo in [https://whwnsdlr1.github.io/tensorflow.js-classification-example](https://whwnsdlr1.github.io/tensorflow.js-classification-example).
@@ -12,13 +27,14 @@ or
 install project as below
 ```
 git clone https://github.com/whwnsdlr1/tensorflow.js-classification-example
+cd tensorflow.js-classification-example/frontend
 yarn install
 yarn run serve
 ```
 and access to http://localhost:port/tensorflow.js-classification-example
 
 ## Usage
-1. draw or upload image
+1. draw or upload image.
 2. run !
 
 ## Browser support - (tested)
@@ -47,5 +63,12 @@ See [Configuration Reference](https://cli.vuejs.org/config/).
 - babel-eslint
 - eslint
 - eslint-plugin-vue
-- sitemap-webpack-plugin
-- vue-template-compiler
+- vue-template-compiler
+
+## used sources
+### dataset
+- [MNIST](http://yann.lecun.com/exdb/mnist/)
+- [CIFAR](https://www.cs.toronto.edu/~kriz/cifar.html)
+- [ImageNet](http://www.image-net.org/)
+### images
+- [https://wikimedia.org/api/rest_v1/media/math/render/svg/bdc1f8eaa8064d15893f1ba6426f20ff8e7149c5](https://wikimedia.org/api/rest_v1/media/math/render/svg/bdc1f8eaa8064d15893f1ba6426f20ff8e7149c5)

docs/cifar/cifar.png

@@ -0,0 +1 @@
+{"format": "layers-model", "generatedBy": "keras v2.2.4-tf", "convertedBy": "TensorFlow.js Converter v1.3.1", "modelTopology": {"keras_version": "2.2.4-tf", "backend": "tensorflow", "model_config": {"class_name": "Model", "config": {"name": "CIFAR", "layers": [{"name": "0", "class_name": "InputLayer", "config": {"batch_input_shape": [null, 32, 32, 3], "dtype": "float32", "sparse": false, "name": "0"}, "inbound_nodes": []}, {"name": "1", "class_name": "Conv2D", "config": {"name": "1", "trainable": true, "dtype": "float32", "filters": 16, "kernel_size": [1, 1], "strides": [1, 1], "padding": "same", "data_format": "channels_last", "dilation_rate": [1, 1], "activation": "linear", "use_bias": true, "kernel_initializer": {"class_name": "VarianceScaling", "config": {"scale": 2.0, "mode": "fan_in", "distribution": "truncated_normal", "seed": null, "dtype": "float32"}}, "bias_initializer": {"class_name": "Zeros", "config": {"dtype": "float32"}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "inbound_nodes": [[["0", 0, 0, {}]]]}, {"name": "2", "class_name": "LeakyReLU", "config": {"name": "2", "trainable": true, "dtype": "float32", "alpha": 0.30000001192092896}, "inbound_nodes": [[["1", 0, 0, {}]]]}, {"name": "3", "class_name": "Conv2D", "config": {"name": "3", "trainable": true, "dtype": "float32", "filters": 16, "kernel_size": [3, 3], "strides": [1, 1], "padding": "same", "data_format": "channels_last", "dilation_rate": [1, 1], "activation": "linear", "use_bias": true, "kernel_initializer": {"class_name": "VarianceScaling", "config": {"scale": 2.0, "mode": "fan_in", "distribution": "truncated_normal", "seed": null, "dtype": "float32"}}, "bias_initializer": {"class_name": "Zeros", "config": {"dtype": "float32"}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "inbound_nodes": [[["2", 0, 0, 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