In this project, I classified images from the CIFAR-10 dataset. The dataset consists of airplanes, dogs, cats, and other objects. The dataset was preprocessed, then trained a convolutional neural network on all the samples. I normalized the images, one-hot encode the labels, build a convolutional layer, max pool layer, and fully connected layer. At then end, I saw their predictions on the sample images.
Clone the Github repository and use condo to install the dependencies
$ git clone https://github.com/TokyoIndex/mlnd_image_classification.git
$ cd mlnd_image_classification
$ conda install conda
$ jupyter notebook
- Python 3
- TensorFlow 1.0
- Numpy
The contents of this repository are covered under the MIT License.