I think deep learning is accessible enough now that if you know how to program,
you know how to get started using it for your own tasks. This great
shows you how you can use
tflearn, a TensorFlow
based Python library to create predictive models based on the CIFAR-10 dataset.
This is a simple implementation using the same neural network layout to identify labeled photos of my cat.
To run this, first create an Anaconda environment based off the
environment.yml using Python 3.5. Then, create a folder
images in the local
directory with two subfolders
not_cat. Sort through your own files
and copy your cat photos into
cat and your non-cat photos into
To run the training step, run:
which will read all of the files and train a network based on the image
features. That script will also write to a file
cat-classifier.tfl which is a
binary representation of the trained model that you can use in later scripts.
To use a trained model from
cnn.py to classify your own images, run:
python classify.py <image_path>
<image_path> is the path of an image you want to classify. The output
from this is a JSON object with probabilities for