Getting started with tensor flow datasets
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Getting started with tensor flow datasets
- 2. About | Godfrey Nolan ● Founder and President of RIIS, LLC. ● Author of six books on development ● Adjunct Professor
- 3. TensorFlow | Hello World
- 4. TensorFlow | Workflow ● Label ● Load ● Train ● Test
- 5. TensorFlow Datasets | Core Values Simplicity Performance Determinism/ Reproducibility Customisability
- 6. TensorFlow Datasets Audio Image Image Classification Object Detection Question Answering Structured Summarization Text Translate Video
- 7. TensorFlow Datasets ds = tfds.load('mnist', split='train', shuffle_files=True)
- 8. train_data, validation_data, test_data = tfds.load( name="yelp_polarity_reviews", split=('train[:60%]', 'train[:60%]', 'test'), as_supervised=True) TensorFlow Datasets
- 9. TensorFlow Datasets ● caltech_birds ● cats_vs_dogs ● cars ● horses_or_humans ● stanford_dogs ● tf_flowers ● rock_paper_scissors Image Classification
- 10. ● coco ● kitti ● open_images ● voc ● waymo_open_dataset ● wider_face TensorFlow Datasets Object Detection
- 11. TensorFlow Datasets Each dataset includes some (not all) of the following metadata: ● Description ● Dataset Homepage ● Download Size ● Example Code ● Example Images ● etc.
- 12. Demo
- 13. Colab ● Simpler than Google Cloud Platform ● GPU/TPU access for $10/month with Colab Pro ● Mount on Google Drive ● Where does everything live?
- 14. Examples
- 15. Other Datasets
- 16. Future Plans ● Snakes ● Mushrooms (bad data) ● Spiders ● TensorFlow 2 Coco ● Cattle & Sheep TFDS
- 18. Thank You! godfrey@riis.com RIIS.com | 248.351.1200 | 1250 Stephenson Hwy, Troy, MI 48083
Editor's Notes
- Who am I What is RIIS Why the interest in TensorFlow Why the interest in TensorFlow Datasets
- Founder and President of RIIS, LLC. – a mobile and web development company based in Troy, MI Author of six books on development Adjunct Professor – currently teaching Android Development at Saginaw Valley State University in the fall term and University of Detroit Mercy in the Winter Term
- What is TensorFlow - back in the 90s, if you wanted to do any AI or machine learning you had to roll your own back propagation neural network. From personal experience I can say it wasn’t impossible but it took a while. These days it’s much simpler, you can call any number of machine learning models from frameworks such as Tensorflow or Pytorch etc. that sit on top of the latest and greatest neural network or other types of models allowing you to easily train your models. The following example is an Hello World for TensorFlow, we’re trying to get the NN to match the equation y = (3*x) + 1, so if x is 1 y is 4. We train the model on a 6 element x,y array over 500 epochs. When we ask it what y is when the value of y is when x = 10 we get a prediction of 31.00025749. So it’s not exactly right but it’s pretty close. EXPLAIN THE MODEL https://codelabs.developers.google.com/codelabs/tensorflow-lab1-helloworld#0 URL is in the resources
- While this is a really simple example we see a lot of the common elements of a standard TensorFlow workflow, i.e. load the data, train the model, test the model and deploy I’m mostly interested in image classification and object detection, as we do a lot of work on drones. We typically deploy the model as a tensorflow lite file on an android or iphone These differ from our hello world as we have to label the data the workflow is now label, load, train, test and deploy A few years ago now, we created an app that counts sheep or cattle from a DJI or Parrot drone. I quickly ran into a problem, where am I going to find my labeled data? Creating your own labeled data can be expensive, you have to get 5000 to 10000 images and then someone has to manually label (draw a box around) each image for what you to detect, that can cost up to $1 per image depending on how many objects you have on an image
- TF Data Sets solve my labeling problem by providing a simple to use loading mechanism for 200+ public research datasets That’s assuming your use case is covered by one of these datsets It’s another front end to make our lives easier so I don’t have to write custom code to load each dataset TFDS has been built with these principles in mind: Simplicity: Standard use-cases should work out-of-the box Performance: TFDS follows best practices and can achieve state-of-the-art speed Determinism/reproducibility: All users get the same examples in the same order Customisability: Advanced users can have fine-grained control
- TensorFlow Datasets have something for everyone, I’m mostly interested in image classification for mobile apps and object detection for drones Why - easy to use, consistency, everything collected together in one place, as well as sample code to show you how to load and train the data. These are NOT Google datasets, the collection is mostly public research datasets Format is always the same which makes it easy to load into a TF notebook. Rather than having to figure out how to download and import it each time Currently there are 224 datasets and it’s growing all the time, there’s also a mechanism to add your own dataset SHOW THE DATASETS - https://www.tensorflow.org/datasets/catalog/mnist https://www.tensorflow.org/datasets/catalog/coco https://www.tensorflow.org/datasets/catalog/yelp_polarity_reviews
- If we want to load the mnist database of handwritten digits we use the following command Some common arguments: * `split=`: Which split to read (e.g. `'train'`, `['train', 'test']`, `'train[80%:]'`,...). * `shuffle_files=`: Control whether to shuffle the files between each epoch (TFDS store big datasets in multiple smaller files). * `data_dir=`: Location where the dataset is saved (defaults to `~/tensorflow_datasets/`) * `with_info=True`: Returns the `tfds.core.DatasetInfo` containing dataset metadata * `download=False`: Disable download
- If we want to load the yelp database of reviews we do the following. Aside from some of the parameters it’s the same. It’s always the same which is the beauty of TFDS. No custom code, no hacks to get at the dataset. Because the datasets are also public research datasets at universities they have a tendency to go offline from time to time It’s downloaded and ready for the next stage of our workflow. We’ll return to the Yelp dataset later in our colab demo.
- There are lots more, just pulling out the ones that we’re interested in
- We use a retrained version of the coco dataset to count cattle from drones COCO – Common object’s in Context Kitti – autonomous driving Voc – lots of different objects, people, cars etc. Waymo is for automated driving Wider face – facial expressions
- Description Dataset Homepage Download Size Example Code Example Images etc.
- Run Through Colab https://colab.research.google.com/drive/1hO7G4Tn-2OAlkDK9-3hpr7Yr0rzKdquz#scrollTo=FmNNPNjR3XsN Show Colab Pro – Choose GPU – Runtime -> Change Runtime Type Runtime all Show RAM Show where the files are /root/tensorflow_datasets/yelp_polarity_reviews/ SKIP AHEAD TO NEXT SLIDE WHILE RUNNING
- One of the issues we had in the past was getting the Google Cloud Platform up and running, could spend days trying to get it configured With these jupyter notebooks on Colab you’re up and running in minutes GO BACK TO YELP COLAB
- Originally I trained as a Mech Eng, so I always tend to the practical rather than the theoretical So over the summer a couple of our interns turned some of the TFDS datasets into real apps. Go to RIIS apps page on Google Play https://play.google.com/store/apps/developer?id=RIIS+LLC Doggie in the window – stanford dogs dataset - SHOW VIDEO - SHOW CODE https://github.com/riis/DogClassificationAndroid Identiplant - Plant Village dataset - SHOW VIDEO – this uses a non TFDS dataset Food Classifier – Food101 dataset – SHOW VIDEO Flower Classifier – Oxford Flower Dataset – SHOW VIDEO
- Problem with other datasets is we need custom script to load them Many of these are also moving into TF Datasets as you can add to the datasets At 225 datasets with more datasets being added all the time But you still might need to look elsewhere for datasets such as Kaggle or Aicrowd or just by doing a search on google
- We’re planning on doing some more apps early in the new year We’re also looking for Canadian interns this time around if anyone knows anyone who might be interested? Snakes – want to see if we can write an app to tell if a snake is poisonous or not Mushrooms (bad data) – There’s a Danish app that detects if a mushroom is going to make you sick or not The app uses crowdsourcing to get lots of people to upload their own mushrooms pictures and definitions Unfortunately there’s nobody looking at the uploaded pictures so the dataset is corrupt Spiders – is the spider a Brown recluse or not? As you can see here I have a problem with poisonous things, maybe I should add Quicksand to the list. TensorFlow 2 Coco – Object Detection API recently was upgraded with support for TF2, redo our Cattle & Sheep counters Cattle & Sheep TFDS - going to upload our cattle and sheep labeled dataset to TFDS
- Read them out