Machine Learning with TensorFlow 1.x by Quan Hua

Author:Quan Hua
Language: eng
Format: epub, mobi
Tags: COM021030 - COMPUTERS / Databases / Data Mining, COM018000 - COMPUTERS / Data Processing, COM051360 - COMPUTERS / Programming Languages / Python
Publisher: Packt
Published: 2018-02-26T10:05:34+00:00

In order to simplify the project, we will do the extraction manually using the compression software. After the extraction is completed, the structure of the diabetic folder will look like this:

diabetic train

10_left.jpeg

10_right.jpeg

...

trainLabels.csv

train.zip.001

train.zip.002

train.zip.003

train.zip.004

train.zip.005

trainLabels.csv.zip

In this case, the train folder contains all the images in the .zip files and trainLabels.csv contains the ground truth labels for each image.

The author of the models repository has provided some example code to work with some popular image classification datasets. Our diabetic problem can be solved with the same approach. Therefore, we can follow the code that works with other datasets such as flower or MNIST dataset. We have already provided the modification to work with diabetic in the repository of this book at https://github.com/mlwithtf/mlwithtf/.

You need to clone the repository and navigate to the chapter_08 folder. You can run the download_and_convert_data.py file as follows:

python download_and_convert_data.py --dataset_name diabetic --dataset_dir D:\\datasets\\diabetic

In this case, we will use dataset_name as diabetic and dataset_dir is the folder that contains the trainLabels.csv and train folder.

It should run without any issues, start preprocessing our dataset into a suitable (299x299) format, and create some TFRecord file in a newly created folder named tfrecords. The following figure shows the content of the tfrecords folder:

Download

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