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    <h1>A simple implementation</h1> <p>Here is a readable implementation using classes in <code>Python</code>. This implementation trades efficiency for understandability:</p> <pre><code> import math import random BIAS = -1 """ To view the structure of the Neural Network, type print network_name """ class Neuron: def __init__(self, n_inputs ): self.n_inputs = n_inputs self.set_weights( [random.uniform(0,1) for x in range(0,n_inputs+1)] ) # +1 for bias weight def sum(self, inputs ): # Does not include the bias return sum(val*self.weights[i] for i,val in enumerate(inputs)) def set_weights(self, weights ): self.weights = weights def __str__(self): return 'Weights: %s, Bias: %s' % ( str(self.weights[:-1]),str(self.weights[-1]) ) class NeuronLayer: def __init__(self, n_neurons, n_inputs): self.n_neurons = n_neurons self.neurons = [Neuron( n_inputs ) for _ in range(0,self.n_neurons)] def __str__(self): return 'Layer:\n\t'+'\n\t'.join([str(neuron) for neuron in self.neurons])+'' class NeuralNetwork: def __init__(self, n_inputs, n_outputs, n_neurons_to_hl, n_hidden_layers): self.n_inputs = n_inputs self.n_outputs = n_outputs self.n_hidden_layers = n_hidden_layers self.n_neurons_to_hl = n_neurons_to_hl # Do not touch self._create_network() self._n_weights = None # end def _create_network(self): if self.n_hidden_layers&gt;0: # create the first layer self.layers = [NeuronLayer( self.n_neurons_to_hl,self.n_inputs )] # create hidden layers self.layers += [NeuronLayer( self.n_neurons_to_hl,self.n_neurons_to_hl ) for _ in range(0,self.n_hidden_layers)] # hidden-to-output layer self.layers += [NeuronLayer( self.n_outputs,self.n_neurons_to_hl )] else: # If we don't require hidden layers self.layers = [NeuronLayer( self.n_outputs,self.n_inputs )] def get_weights(self): weights = [] for layer in self.layers: for neuron in layer.neurons: weights += neuron.weights return weights @property def n_weights(self): if not self._n_weights: self._n_weights = 0 for layer in self.layers: for neuron in layer.neurons: self._n_weights += neuron.n_inputs+1 # +1 for bias weight return self._n_weights def set_weights(self, weights ): assert len(weights)==self.n_weights, "Incorrect amount of weights." stop = 0 for layer in self.layers: for neuron in layer.neurons: start, stop = stop, stop+(neuron.n_inputs+1) neuron.set_weights( weights[start:stop] ) return self def update(self, inputs ): assert len(inputs)==self.n_inputs, "Incorrect amount of inputs." for layer in self.layers: outputs = [] for neuron in layer.neurons: tot = neuron.sum(inputs) + neuron.weights[-1]*BIAS outputs.append( self.sigmoid(tot) ) inputs = outputs return outputs def sigmoid(self, activation,response=1 ): # the activation function try: return 1/(1+math.e**(-activation/response)) except OverflowError: return float("inf") def __str__(self): return '\n'.join([str(i+1)+' '+str(layer) for i,layer in enumerate(self.layers)]) </code></pre> <h2>A more efficient implementation (with learning)</h2> <p>If you are looking for a more efficient example of a neural network with learning (backpropagation), take a look at my <a href="http://jorgenkg.github.io/python-neural-network/">neural network Github repository here</a>.</p>
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