DrompiX

from pomegranate import DiscreteDistribution, \
    ConditionalProbabilityTable, BayesianNetwork, State


def make_net() -> BayesianNetwork:
    # midterm exam grade: either A or O (Other)
    midterm = DiscreteDistribution({
        'A': 0.11,
        'O': 0.89
    })

DrompiX

class NeuralNetwork(object):
    """Class which implements Neural Network

    For now support single loss function L = 1/2 * (a2-y)^2
    For now support 3 activation functions: sigmoid, tanh, relu

    Parameters
    ----------
    input_size: int, required
        size of input layer