This repository aims to provide a simple example for the CIFAR-10 dataset with TensorFlow 2.0 and the Keras API. There are multiple example scripts in this repository, each illustrating a different aspect.
In order to run these scripts, you need to have Python 3.5, 3.6 or 3.7 installed. In your Python environment, please install TensorFlow 2.0 and tensorflow-datasets. At the time of writing TensorFlow 2.0 is in alpha, therefore you can install the CPU version with the following pip command:
pip install --upgrade tensorflow==2.0.0alpha0 tensorflow-datasets
To install the GPU version of TensorFlow, please refer to the official installation instructions.
The FirstNetwork.py script shows how the CIFAR-10 dataset is loaded and trained with a very simple network.
Please note that this network is overly simplified for the ease of understanding the workflow.
To see a model with better performance, please refer to the BetterNetwork.py script.
This script extends the FirstNetwork.py script by adding a Tensorboard callback.
Please note that the callback has to be provided to model.fit(...).
To view the logged data in TensorBoard, open a command line and enter
tensorboard --logdir=<log_dir>
while replacing <log_dir> with the directory provided in the log_dir parameter of the TensorBoard callback.
These two scripts show how to use the trained and saved model to predict the classes of the images. To do this, the model is first loaded from the checkpoint and then fed an image to be predicted.
Please note that the fed values have to be 4-dimensional. This allows predicting multiple images in one go.
The BetterNetwork.py script states a more complex model to achieve better results on the CIFAR-10 dataset (about 85% test accuracy).
Furthermore, it uses tf.data.Dataset to build an input pipeline using some data augmentation to further improve the training.
Augmentations increase the variance in the data by changing the images randomly every time.