This is Convolutional Neural Network only in python & numpy. It is simple and slow but will get the job done +1

Specification Weight Initialization : HE Normal

Weight Update Policy : ADAM, NAG, Momentum, Vanila

Active Function : ReLU, Sigmoid

Regulization : Droupout(only on fc), L2

Pooling : Max, Average

Loss Function : Softmax, Logistic

Prerequisites numpy (+ mkl for intel processors. recommend anaconda) Used sklearn for LabelEncoder & utils.shuffle on examples.

Example AND gate and CIFAR-10 examples are included.

lr = 1e-4 l2_reg = 8e-6

cnn = NeuralNetwork(train_images.shape[1:], [ {'type': 'conv', 'k': 16, 'u_type': 'nag', 'f': 5, 's': 1, 'p': 2}, {'type': 'pool', 'method': 'average'}, {'type': 'conv', 'k': 20, 'u_type': 'nag', 'f': 5, 's': 1, 'p': 2}, {'type': 'pool', 'method': 'average'}, {'type': 'conv', 'k': 20, 'u_type': 'nag', 'f': 5, 's': 1, 'p': 2}, {'type': 'pool', 'method': 'average'}, {'type': 'output', 'k': len(le.classes_), 'u_type': 'adam'} ] , lr, l2_reg=l2_reg) CIFAR-10 example gets ~72% test accuracy in 20 epoch.

API Reference classes.NeuralNetwork(self, input_shape, layer_list, lr, l2_reg=0, loss='softmax'):

Parameter Description input_shape Data's numpy shape. layer_list List of layers you want to be networked. All of properties goes to **kwargs. lr Learning rate. l2_reg L2 regularization loss Loss function. 'softmax', 'logistic'

classes.NeuralLayer(input_size, k, f=3, s=1, p=1, u_type='adam', a_type='relu', dropout=1)

classes.PoolLayer(input_size, f=2, s=2, method='max', dropout=1):

classes.ConvLayer(input_size, k, f=3, s=1, p=1, u_type='adam', a_type='relu', dropout=1)