43 lines
1.8 KiB
Python
43 lines
1.8 KiB
Python
import numpy as np
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import random
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def mat_mult(A,B):
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return [[sum([A[i][m]*B[m][j] for m in range(len(A[0]))]) for j in range(len(B[0]))] for i in range(len(A))]
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class Neural_Network(object):
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# inspired from https://enlight.nyc/projects/neural-network/
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def __init__(self, W1=None, W2=None):
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#parameters
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self.inputSize = 3
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self.outputSize = 2
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self.hiddenSize = 3
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self.fitness = 0
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#weights
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if W1 is not None:
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self.W1=W1
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else :
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self.W1 = np.random.randn(self.inputSize, self.hiddenSize) # weights from input to hidden layer
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if W2 is not None:
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self.W2=W2
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else :
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self.W2 = np.random.randn(self.hiddenSize, self.outputSize) # weights from hidden to output layer
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# self.w1 = [[random.random() for i in range(self.hiddenSize)] for i in range(self.inputSize)]
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# self.w2 = [[random.random() for i in range(self.outputSize)] for i in range(self.hiddenSize)]
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def predict(self, X):
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#forward propagation through our network
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self.z = np.dot(X, self.W1) # dot product of X (input) and first set of 3x2 weights
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self.z2 = self.sigmoid(self.z) # activation function
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self.z3 = np.dot(self.z2, self.W2) # dot product of hidden layer (z2) and second set of 3x1 weights
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o = self.sigmoid(self.z3) # final activation function
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# self.z = mat_mult(X, self.w1) # dot product of X (input) and first set of 3x2 weights
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# self.z2 = self.sigmoid(self.z) # activation function
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# self.z3 = mat_mult(self.z2, self.w2) # dot product of hidden layer (z2) and second set of 3x1 weights
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# o = self.sigmoid(self.z3) # final activation function
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return o
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def sigmoid(self, s):
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# activation function
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return 1/(1+np.exp(-s)) -0.5 |