cnn4fun

This is a rather basic Convolutional Neural Network.

The cnn package contains a primary cnn.CNN class as well as convolution, max-pooling, and softmax activation layers at cnn.layers.Conv, cnn.layers.MaxPool and cnn.layers.SoftMax, respectively. These layers can be configured along with the learning rate in order to fine-tune the training of the network. This network currently works with the MNIST handwritten digits dataset, which can be tested by running python run_mnist.py.

The network supports both grayscale and RGB images.

To run with the MNIST dataset:

1. clone this repo locally
2. have Python 3 and pip installed on your machine
3. install dependencies with pip install -r requirements.txt
4. run python run_mnist.py

Package usage

# package must exist locally, whether cloned or copied into a project
import cnn

# get training images (RGB or grayscale) and labels, ordered
training_images = get_ordered_images_list()
training_labels = get_ordered_labels_list()

# define list of classes
classes = ['cat', 'dog']

# initialize layer stack
layers = [
    cnn.layers.Conv(num_kernels=16, kernel_dimension=5, stride=1),
    cnn.layers.MaxPool(kernel_dimension=2, stride=2),
    cnn.layers.Conv(num_kernels=16, kernel_dimension=3, stride=1),
    cnn.layers.MaxPool(kernel_dimension=2, stride=2),
    cnn.layers.SoftMax(num_classes=2),
]

# initialize network object
net = cnn.CNN(layers)

# train
net.train(training_images, training_labels, classes, num_epochs=20, rate=0.001)

# get test image and label
test_image = get_dog_png()
test_label = 'dog'

# test model prediction
prediction_index = net.predict(test_image)

prediction = classes[prediction_index]
correct = prediction == test_label

Tests:

• To run unit tests, run python -m pytest.