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December 25, 2015 14:53
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Python標準ライブラリだけでニューラルネットワークを実装してみた
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import random | |
import itertools | |
import neuron | |
from IPython import embed | |
from IPython.terminal.embed import InteractiveShellEmbed | |
class NeuralNetwork(): | |
def __init__(self, layers, learn_rate): | |
self.learn_rate = learn_rate | |
self.neurons = self.init_neurons(layers) | |
self.init_synapse() | |
def init_neurons(self, layers): | |
neurons = [] | |
for idx, num in enumerate(layers): | |
if idx == 0: | |
neurons.append([neuron.InputNeuron(self.learn_rate) for i in range(num)]) | |
elif idx == len(layers) - 1: | |
neurons.append([neuron.OutputNeuron(self.learn_rate) for i in range(num)]) | |
else: | |
neurons.append([neuron.MediumNeuron(self.learn_rate) for i in range(num)]) | |
return neurons | |
def init_synapse(self): | |
for i in range(len(self.neurons) - 1): | |
for pair in itertools.product(self.neurons[i], self.neurons[i+1]): | |
synapse = neuron.Synapse(pair[0], pair[1]) | |
pair[0].dst.append(synapse) | |
pair[1].src.append(synapse) | |
def fit(self, input_signals, supervisor_signals): | |
for input_neuron, input_signal in zip(self.neurons[0], input_signals): | |
input_neuron.set_value(input_signal) | |
for layer in self.neurons[1:]: | |
for neuron in layer: | |
neuron.set_value() | |
for output_neuron, supervisor_signal in zip(self.neurons[-1], supervisor_signals): | |
output_neuron.fit(supervisor_signal) | |
for layer in self.neurons[-2:0:-1]: | |
for neuron in layer: | |
neuron.fit() | |
def classify(self, input_signals): | |
for input_neuron, input_signal in zip(self.neurons[0], input_signals): | |
input_neuron.set_value(input_signal) | |
for layer in self.neurons[1:]: | |
for neuron in layer: | |
neuron.set_value() | |
return [on.value() for on in self.neurons[-1]] | |
if __name__ == '__main__': | |
# タブルの要素数がレイヤー数、左から各レイヤーのニューロン数 | |
layer_setting = (2, 4, 2) | |
xor_signal = [ | |
((0, 0),(0, 0)), | |
((0, 1),(0, 1)), | |
((1, 0),(0, 1)), | |
((1, 1),(1, 0)), | |
] | |
nn = NeuralNetwork(layer_setting, 0.25) | |
for i in range(400000): | |
signals = random.choice(xor_signal) | |
nn.fit(signals[0], signals[1]) | |
embed() |
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import random | |
import math | |
import functools | |
class Synapse(): | |
def __init__(self, src_neuron, dst_neuron): | |
self.src = src_neuron | |
self.dst = dst_neuron | |
self.weight = (random.random() * 0.2) - 0.1 | |
def sigmoid(x): | |
return 1 / (1+math.exp(-x)) | |
class BaseNeuron(): | |
def __init__(self, learn_rate): | |
self._value = 0 | |
self.learn_rate = learn_rate | |
self._epsilon = 0 | |
def set_value(self): | |
pass | |
def value(self): | |
return self._value | |
def epsilon(self): | |
return self._epsilon | |
class InputNeuron(BaseNeuron): | |
def __init__(self, learn_rate): | |
super().__init__(learn_rate) | |
self.dst = [] | |
def set_value(self, value): | |
self._value = value | |
class MediumNeuron(BaseNeuron): | |
def __init__(self, learn_rate): | |
super().__init__(learn_rate) | |
self.src = [] | |
self.dst = [] | |
def set_value(self): | |
in_signal = functools.reduce( | |
lambda x, y: x+y, [s.weight * s.src.value() for s in self.src] | |
) | |
self._value = sigmoid(in_signal) | |
def fit(self): | |
err = sum([s.weight * s.dst.epsilon() for s in self.dst]) | |
self._epsilon = err * self._value * (1 - self._value) | |
for s in self.src: | |
s.weight -= self.learn_rate * self._epsilon * s.src.value() | |
class OutputNeuron(BaseNeuron): | |
def __init__(self, learn_rate): | |
super().__init__(learn_rate) | |
self.src = [] | |
def set_value(self): | |
in_signal = functools.reduce( | |
lambda x, y: x+y, [s.weight * s.src.value() for s in self.src] | |
) | |
self._value = sigmoid(in_signal) | |
def fit(self, supervisor_signal): | |
err = (self._value - supervisor_signal) | |
self._epsilon = err * self._value * (1 - self._value) | |
for s in self.src: | |
s.weight -= self.learn_rate * self._epsilon * s.src.value() | |
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