Labeeb

A machine learning engine designed and developed to be both easy to use and source code readable. It is a straightforward implementation of different algorithms and techniques of machine learning in Python. You can use it for small projects and/or educational purposes.

Backpropagation is implemented in boring detail such that derivative steps is taken carefully and without any implicit or hidden details

Labeeb is Arabic word means smart. It is intended to be easy to use and intuitive to build your neural network. To build you network to classify the will known MNIST dataset:

1. Define your network ```python from labeeb.neural_network.network import NeuralNetwork from labeeb.neural_network.activations import Activation from labeeb.optimization import Optimizer

my_nn = NeuralNetwork(n_features=400, n_classes=10)

Build up the layers as you want

my_nn.add_layer(100, activation=Activation.leaky_relu, dropout_keep_prob=1)
my_nn.add_layer(12, activation=Activation.softmax_stable, output_layer=True)

Finally, call the optimizer

gd_optimizer = Optimizer(loss=’multinomial_cross_entropy’, method=’adam’) # gd-with-momentum gradient-descent rmsprop adam gd_optimizer.minimize(my_nn, epochs=100, mini_batch_size=5000, learning_rate=.1, regularization_parameter=0, dataset=mnist) ```

The following is the complete source code for the example. More examples can be found under Labeeb/examples.

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