35 lines
No EOL
1.2 KiB
Python
35 lines
No EOL
1.2 KiB
Python
#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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"""
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Created on Tue Apr 30 19:57:27 2019
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@author: francois
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"""
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import numpy as np
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import NeuralNetUtils as nnu
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class NeuralNode:
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def __init__(self, input_size, input_weights=None, **kwargs):
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self.input_size = input_size+1
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if (input_weights == None):
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self.input_weights = np.random.uniform(-1,1,self.input_size)
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else:
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self.input_weights = input_weights
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def __str__(self):
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return str(self.input_weights)
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def input_function(self,input_values):
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return np.dot(input_values,self.input_weights)
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def activation(self,input_values):
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input_values_with_bias = np.hstack((1,input_values))
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return round(nnu.sigmoid(self.input_function(input_values_with_bias)))
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def activation_derivative(self,input_values):
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input_values_with_bias = np.hstack((1,input_values))
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return nnu.sigmoid_derivative(self.input_function(input_values_with_bias))
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def update_weights(self,input_values,alpha,delta):
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input_values_with_bias = np.hstack((1,input_values))
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self.input_weights = self.input_weights+(alpha*input_values_with_bias*delta) |