84 lines
2.6 KiB
Python
84 lines
2.6 KiB
Python
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import math
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from Matrix import Matrix
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def sigmoid(x):
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"""The Sigmoid function"""
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return 1 / (1 + math.exp(-x))
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def dsigmoid(x):
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"""The derivative of the Sigmoid function"""
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return sigmoid(x) * (1 - sigmoid(x))
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class NeuralNetwork:
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def __init__(self, num_i, num_h, num_o):
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self.learning_rate = 0.1
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self.input_nodes = num_i
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self.hidden_nodes = num_h
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self.output_nodes = num_o
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self.weights_ih = Matrix(self.hidden_nodes, self.input_nodes)
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self.weights_ho = Matrix(self.output_nodes, self.hidden_nodes)
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self.bias_h = Matrix(self.hidden_nodes, 1)
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self.bias_o = Matrix(self.output_nodes, 1)
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self.weights_ih.randomize()
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self.weights_ho.randomize()
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self.bias_h.randomize()
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self.bias_o.randomize()
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def feedforward(self, input_list):
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"""Feedforward algorithm, basically the hidden layer"""
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inputs = Matrix.from_list(input_list)
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hidden = Matrix.product(self.weights_ih, inputs)
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hidden.add(self.bias_h)
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hidden.map(lambda x, i, j: sigmoid(x))
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output = Matrix.product(self.weights_ho, hidden)
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output.add(self.bias_o)
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output.map(lambda x, i, j: sigmoid(x))
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return output.to_list()
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def train(self, input_list, answer):
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"""Train the NN on a known input/answer combination"""
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inputs = Matrix.from_list(input_list)
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hidden = Matrix.product(self.weights_ih, inputs)
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hidden.add(self.bias_h)
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hidden.map(lambda x, i, j: sigmoid(x))
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outputs = Matrix.product(self.weights_ho, hidden)
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outputs.add(self.bias_o)
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outputs.map(lambda x, i, j: sigmoid(x))
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targets = Matrix.from_list(answer)
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output_errors = Matrix.subtract(targets, outputs)
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gradients = Matrix.clone(outputs)
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gradients.map(lambda x, i, j: x * (1 - x))
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gradients.multiply(output_errors)
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gradients.multiply(self.learning_rate)
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hidden_t = Matrix.transpose(hidden)
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weight_ho_deltas = Matrix.product(gradients, hidden_t)
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self.weights_ho.add(weight_ho_deltas)
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self.bias_o.add(gradients)
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who_t = Matrix.transpose(self.weights_ho)
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hidden_errors = Matrix.product(who_t, output_errors)
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hidden_gradient = Matrix.clone(hidden)
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hidden_gradient.map(lambda x, i, j: x * (1 - x))
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hidden_gradient.multiply(hidden_errors)
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hidden_gradient.multiply(self.learning_rate)
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inputs_t = Matrix.transpose(inputs)
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weight_ih_deltas = Matrix.product(hidden_gradient, inputs_t)
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self.weights_ih.add(weight_ih_deltas)
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self.bias_h.add(hidden_gradient)
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