[GitHub] ThomasDelteil commented on a change in pull request #13411: [WIP] Gluon end to end tutorial

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ThomasDelteil commented on a change in pull request #13411: [WIP] Gluon end to end tutorial
URL: https://github.com/apache/incubator-mxnet/pull/13411#discussion_r236902654
 
 

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 File path: docs/tutorials/gluon/gluon_from_experiment_to_deploymen.md
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+# Gluon: from experiment to deployment, an end to end example
+
+## Overview
+
+MXNet Gluon API comes with a lot of great features and it can provide you everything you need from experiment to deploy the model.
+In this tutorial, we will walk you through a common used case on how to build a model using gluon, train it on your data, and deploy it for inference.
+
+Let's say you want to build a service that provides flower species recognition. A common use case is, you don't have enough data to train a good model like ResNet50.
+What you can do is utilize pre-trained model from Gluon, tweak the model according to your neeed, fine-tune the model on your small dataset, and deploy the model to integrate with your service.
+
+We will use the [Oxford 102 Category Flower Dateset](http://www.robots.ox.ac.uk/~vgg/data/flowers/102/) as an example to show you the steps.
+
+## Prepare training data
+
+You can use this [script](https://github.com/Arsey/keras-transfer-learning-for-oxford102/blob/master/bootstrap.py) to download and organize your data into train, test, and validation sets. Simply run:
+```python
+python bootstrap.py
+```
+
+Now your data will be organized into the following format, all the images belong to the same category will be put together
+```
+data
+├── train
+│   ├── 0
+│   │   ├── image_06736.jpg
+│   │   ├── image_06741.jpg
+...
+│   ├── 1
+│   │   ├── image_06755.jpg
+│   │   ├── image_06899.jpg
+...
+├── test
+│   ├── 0
+│   │   ├── image_00731.jpg
+│   │   ├── image_0002.jpg
+...
+│   ├── 1
+│   │   ├── image_00036.jpg
+│   │   ├── image_05011.jpg
+
+```
+
+
+# Training using Gluon
+### Define Hyper-paramerters
+Now let's first import neccesarry packages:
+```python
+import mxnet as mx
+import numpy as np
+import os, time
+
+from mxnet import gluon, init
+from mxnet import autograd as ag
+from mxnet.gluon import nn
+from mxnet.gluon.data.vision import transforms
+from gluoncv.model_zoo import get_model
+```
+
+and define the hyper parameter we will use for fine-tuning:
 
 Review comment:
   hyperparameters* 

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