In this project, we will classify images from the CIFAR-10 dataset. The dataset consists of airplanes, dogs, cats, and other objects. The dataset will need to be preprocessed, then train a convolutional neural network on all the samples. We will normalize the images, one-hot encode the labels, build a convolutional layer, max pool layer, and fully connected layer. At then end, we will see the predictions on the sample images.
This project requires Python 3.x and the following Python libraries installed:
- urllib.request
- os.path
- tqdm
- tarfile
- pickle
- numpy
- tensorflow
In a terminal or command window, navigate to the top-level project directory image_classification/ (that contains this README) and run one of the following commands:
ipython notebook image_classification.ipynbor
jupyter notebook image_classification.ipynbThis will open the Jupyter Notebook software and project file in your browser.