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neuralnetwork.py
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neuralnetwork.py
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import numpy as np
import math
import random
import pickle
class NeuralNetwork():
def __init__(self, input_nodes, hidden_nodes1, output_nodes):
self.input_nodes = input_nodes
self.hidden_nodes1 = hidden_nodes1
# self.hidden_nodes2 = hidden_nodes2
self.output_nodes = output_nodes
self.in_hidden1_weights = np.random.rand(self.hidden_nodes1, self.input_nodes)
# self.h1_h2_weights = np.random.rand(self.hidden_nodes2,self.hidden_nodes1)
self.hidden1_output_weights = np.random.rand(self.output_nodes, self.hidden_nodes1)
self.in_hidden1_biases = np.random.rand(self.hidden_nodes1, 1)
# self.h1_h2_biases = np.random.rand(self.hidden_nodes2,1)
self.hidden1_output_biases = np.random.rand(self.output_nodes, 1)
self.sigmoid_v = np.vectorize(self.sigmoid)
def sigmoid(self, x):
return (1 / (1 + math.exp(-x)))
def feedforward(self, inputs):
self.inputs = inputs
self.hidden_layer1 = self.in_hidden1_weights.dot(self.inputs)
self.hidden_layer1 = self.sigmoid_v(self.hidden_layer1 + self.in_hidden1_biases)
# self.hidden_layer2 = self.h1_h2_weights.dot(self.hidden_layer1)
# self.hidden_layer2 = self.sigmoid_v(self.hidden_layer2+self.h1_h2_biases)
self.output = self.hidden1_output_weights.dot(self.hidden_layer1)
self.output = self.sigmoid_v(self.output + self.hidden1_output_biases)
return self.output
def crossover(self, mat1, mat2):
childMat = np.zeros((mat1.shape[0], mat1.shape[1]))
x = mat1.shape[0] // 2
childMat[:x], childMat[x:] = mat1[:x], mat2[x:]
return childMat
def mutate(self, mat, rate):
for i in range(mat.shape[0]):
if rate > (random.uniform(0, 1)):
for j in range(mat.shape[1]):
mat[i][j] += random.uniform(-1, 1)
def serialize(self):
return pickle.dumps(self)