87 lines
1.9 KiB
Python
87 lines
1.9 KiB
Python
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#!/bin/python3
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import numpy as np
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import random
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cicli = 59990
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hidden_layer = 1
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epsilon = 0.01
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def fdt(x,deriv=False):
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if(deriv==True):
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return (1-np.tanh(x)**2)
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return np.tanh(x)
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def error_func(output):
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e=0
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for i in range(len(output)):
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e += (output[i]-Y[i])**2
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return 0.5*e
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#input data
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X = np.array([[0,1], # primo input
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[0,1],
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[1,1],
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[1,1]])
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#output data
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Y = np.array([[0],
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[0],
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[1],
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[1]])
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np.random.seed(1) #per avere sempre lo stesso
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#sinapsi tra input e Perceptron di output
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syn0 = 0.1*(2*np.random.random((2,hidden_layer)) -1)
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#print(syn0) #2x1
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# fase forward
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l0 = X
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l1 = fdt(np.dot(l0,syn0)) #output layer hidden, 4x4 4 esempi, 4 neuroni di output
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for i in range(cicli):
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print(str(int(100*i/cicli))+"---------------------------------------") #% sul numero totale di cicli
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# fase forward
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old_error = error_func(l1)
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#fase backward
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#calcolo delta_nu
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delta_nu = Y - l1
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#print(syn0[:,0])
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sum = 0
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for nu in range(len(X)): #ciclo tra gli esempi
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sum += epsilon*(delta_nu[nu]*fdt(np.dot(X[nu],syn0),True))*X[nu]
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#print(sum)
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syn0[:,0] += sum
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l1 = fdt(np.dot(l0,syn0)) #output layer hidden, 4x4 4 esempi, 4 neuroni di output
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current_error = error_func(l1)
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print(old_error - current_error)
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if (np.linalg.norm(sum) < 10**-8):
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print("Very low variation in weight-space - exiting")
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print("delta_w :"+str(np.linalg.norm(sum)))
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break
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if (error_func(l1) < 0.001):
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print("Very low error - exiting")
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break
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if (abs(current_error - old_error) < 10**-8):
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print("Very low error variation - exiting")
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break
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print("Error: "+str(error_func(l1)))
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for i in range(len(np.transpose(l1))):
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print(np.transpose(l1)[i])
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#for i in range(cicli):
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#l1 = fdt(np.dot(l0, syn0))
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#print(l1)
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#l2 = fdt(np.dot(l1, syn1))
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#print(l2)
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