neural_network

from random import seed
from random import random
import numpy as np

# Initialize the network

def initialize_network(n_inputs,n_hiddens,n_outputs):
    network=list()
    hidden_layer=[{'weights':[random() for i in range(n_inputs+1)]} for i in range(n_hiddens)]
    network.append(hidden_layer)
    output_layer = [{'weights': [random() for i in range(n_hiddens + 1)]} for i in range(n_outputs)]
    network.append(output_layer)
    return network

# calculate neuron activation for an input
def activate(weights, inputs):
    activation=weights[-1]
    for i in range(len(weights)-1):
        activation+=weights[i] * inputs[i]
    return activation

# Transfer Neuron Activation
def transfer(activation):
    return 1.0/(1.0+np.exp(-activation))

# Forward propogate input to a network output
def forward_propogate(network,row):
    inputs=row
    for layer in network:
        new_inputs=[]
        for neuron in layer:
            activation=activate(neuron['weights'],inputs)
            neuron['output']=transfer(activation)
            new_inputs.append(neuron['output'])
        inputs=new_inputs
    return inputs



seed(1)

network=initialize_network(2,1,8)

# for layer in network:
#     print layer

row=[1,0,None]
output=forward_propogate(network,row)
print output