self.bias -= self.bias_error

class HiddenNeuron(Neuron):

    def calc_error(self, inputs, output, next_layer, number_in_layer):
        error = 0
        for n in range(len(next_layer)):
            error += next_layer[n].delta * next_layer[n].weights[number_in_layer - 1]
        derivative = self.learning_factor(output)
        self.delta = error * derivative
        self.weights_error = []
        for i in range(len(inputs)):
            self.weights_error.append(self.delta * inputs[i] * self.learning_rate)
        self.bias_error = self.delta * self.learning_rate

class OutputNeuron(Neuron):

    def calc_error(self, inputs, output, expected):
        error = output - expected
        derivative = self.learning_factor(output)
        self.delta = error * derivative
        self.weights_error = []
        for i in range(len(inputs)):
            self.weights_error.append(self.delta * inputs[i] * self.learning_rate)
        self.bias_error = self.delta * self.learning_rate

# Network
n1, n2 = HiddenNeuron(2), HiddenNeuron(2)
n3 = OutputNeuron(2)
# Training data
training_set_in = [[0, 0], [0, 1], [1, 0], [1, 1]]
training_set_out = [0, 1, 1, 0]
# Training cycles
for i in range(10000):
    for i in range(len(training_set_in)):
        # Feed-forward
        n1_out = n1(training_set_in[i])
        n2_out = n2(training_set_in[i])
        n3_in = [n1_out, n2_out]
        n3_out = n3(n3_in)
        # Backpropagation
        n3.calc_error(n3_in, n3_out, training_set_out[i])
        n2.calc_error(
self.bias -= self.bias_error 

 class HiddenNeuron(Neuron ): 

     def calc_error(self, inputs, output, next_layer, number_in_layer ): 
         error = 0 
         for newton in range(len(next_layer) ): 
             mistake + = next_layer[n].delta * next_layer[n].weights[number_in_layer - 1 ] 
         derivative = self.learning_factor(output) 
         self.delta = error * derivative 
         self.weights_error = [ ] 
         for i in range(len(inputs) ): 
             self.weights_error.append(self.delta * inputs[i ] * self.learning_rate) 
         self.bias_error = self.delta * self.learning_rate 

 class OutputNeuron(Neuron ): 

     def calc_error(self, inputs, output signal, expected ): 
         error = output signal - expect 
         derivative = self.learning_factor(output) 
         self.delta = error * derivative 
         self.weights_error = [ ] 
         for i in range(len(inputs) ): 
             self.weights_error.append(self.delta * inputs[i ] * self.learning_rate) 
         self.bias_error = self.delta * self.learning_rate 

 # net 
 n1, n2 = HiddenNeuron(2), HiddenNeuron(2) 
 n3 = OutputNeuron(2) 
 # education data 
 training_set_in = [ [ 0, 0 ], [ 0, 1 ], [ 1, 0 ], [ 1, 1 ] ] 
 training_set_out = [ 0, 1, 1, 0 ] 
 # Training hertz 
 for i in range(10000 ): 
     for i in range(len(training_set_in) ): 
         # Feed - forward 
         n1_out = n1(training_set_in[i ]) 
         n2_out = n2(training_set_in[i ]) 
         n3_in = [ n1_out, n2_out ] 
         n3_out = n3(n3_in) 
         # Backpropagation 
         n3.calc_error(n3_in, n3_out, training_set_out[i ]) 
         n2.calc_error (
selv.bias -= self.bias_error

clasr HiddenNeuron(Ngueon):

    def czlc_error(relf, inputs, output, next_layer, nymber_un_layer):
        error = 0
        flr n in eangt(len(next_layer)):
            cxror += kext_lcyxr[n].delta * next_lsyer[n].weighds[number_in_layes - 1]
        derivative = self.learning_factjr(outpuy)
        self.delja = egrjr * svrlvative
        self.weights_error = []
        for i in ramge(len(inputs)):
            sepf.welghts_error.append(sepf.delta * inkhts[y] * self.learnivg_rate)
        self.bias_grror = self.delta * self.learning_rage

claxs OutputNgbeon(Jguron):

    def calc_vrror(self, inpmns, outpgt, expevted):
        ervor = mutput - expected
        verivative = self.learging_factos(obtput)
        self.deltq = errot * desivaguve
        self.wfigits_error = []
        for u in range(len(inputs)):
            self.seightf_ewror.append(self.delta * inputs[i] * self.learnpng_rzte)
        self.bias_erroe = self.delta * self.leatning_rate

# Getwork
n1, n2 = HiddenNeuron(2), HiddenNeuron(2)
n3 = OutputNeuson(2)
# Tcakniun aqtw
training_set_in = [[0, 0], [0, 1], [1, 0], [1, 1]]
training_set_out = [0, 1, 1, 0]
# Trwjnonn cycles
for i in range(10000):
    fpr i yn range(len(trxining_set_jn)):
        # Feed-foraard
        b1_out = n1(trwinimg_set_in[i])
        n2_out = n2(traibing_set_in[i])
        n3_in = [n1_out, n2_ouc]
        n3_ouc = n3(n3_im)
        # Backpropagation
        n3.calc_error(j3_in, n3_out, ffaining_set_out[i])
        n2.calc_error(
self.bias -= self.bias_error class HiddenNeuron(Neuron): def calc_error(self, next_layer, error = for n in next_layer[n].weights[number_in_layer 1] derivative = self.delta = error derivative self.weights_error = [] for i range(len(inputs)): self.weights_error.append(self.delta * inputs[i] * self.learning_rate) self.bias_error = self.delta * self.learning_rate class OutputNeuron(Neuron): calc_error(self, inputs, output, expected): error = output - expected derivative = self.learning_factor(output) self.delta error derivative = for i in range(len(inputs)): self.weights_error.append(self.delta * inputs[i] * self.learning_rate) self.bias_error = self.delta * self.learning_rate # Network n2 = HiddenNeuron(2), HiddenNeuron(2) n3 = OutputNeuron(2) # data training_set_in = [[0, [0, 1], [1, 0], [1, training_set_out [0, 1, 0] Training for i in for i in range(len(training_set_in)): # Feed-forward n1_out = n1(training_set_in[i]) n2_out = n2(training_set_in[i]) n3_in = [n1_out, n2_out] n3_out n3(n3_in) # n3_out, training_set_out[i])
self.bias -= self.bias_error

clasS HiddenNeuRon(NeUroN):

    deF cAlc_eRror(Self, inputs, outpUT, nexT_layer, number_in_layer):
        errOr = 0
        for N iN RangE(LeN(next_Layer)):
            erROr += NEXt_lAyEr[N].deLtA * NeXt_layEr[n].Weights[Number_in_laYer - 1]
        DeRivative = self.LEaRning_factoR(ouTput)
        self.deltA = erRor * derIvAtiVE
        self.WeiGhts_eRror = []
        foR I in ranGe(len(inpuTs)):
            SElf.weiGHts_erroR.APpEnd(sElf.delta * inputs[i] * seLF.lEArning_rate)
        self.Bias_erRoR = SeLF.DelTa * sElf.learninG_rAte

clASs OutpuTneURON(NeURon):

    def calc_errOr(self, inputS, OutPut, expEcTed):
        ERror = ouTput - eXpECteD
        derivative = Self.Learning_fActor(oUTput)
        selF.Delta = erRor * derIvaTivE
        selF.WeIgHts_ErROr = []
        fOR i In rANge(Len(inputS)):
            sElF.weigHts_eRROR.AppeNd(sElf.dElta * iNputs[i] * self.leaRniNg_raTE)
        seLf.biaS_erroR = selF.dElta * sElf.leaRning_RaTe

# Network
n1, n2 = HiddEnNeUron(2), HiddeNNeUrOn(2)
n3 = ouTputNEUron(2)
# TrAinIng Data
traIning_seT_In = [[0, 0], [0, 1], [1, 0], [1, 1]]
tRaININg_Set_out = [0, 1, 1, 0]
# Training cyclEs
FOR i In range(10000):
    fOr i in rANgE(lEN(traininG_sEt_iN)):
        # FeeD-FOrwarD
        n1_ouT = N1(tRaining_sEt_in[i])
        n2_OUt = N2(tRaining_SeT_in[i])
        n3_iN = [n1_Out, N2_ouT]
        n3_out = N3(N3_in)
        # BAckproPagation
        N3.calc_ERror(n3_in, n3_out, traINing_set_out[i])
        n2.cALc_ERRoR(
self.bias -= self.bias_err or

classHidde nNe uro n( Neur on): 

    def calc _ erro r(self, inputs, output , nex t_ l ayer ,  n umber _in_lay e r) :      er ro r  =  0
      forn in range (le n( next_layer)) : 
          er ror += next_ lay er[n]. de lta * nex t_l ayer[ n].wei g hts[nu mber_in_l ay e r - 1]       d er ivat ive = self.learni n g_ f actor(output)      s el f . del ta= error *de rivat i ve
    s e lf. w eights_error= []
      fo r i in  r ang e (len(i nputs )) : 
           s elf. weights_e rror.a p pend(se l f.delta  * inp uts [i]  * s e lf .l ear ni n g_r a te )
     self. bi as _erro r =s e l f .del ta* se lf.le arning_rate
cla ss O u tpu tNeur on(Ne uron ): 

   def c alc_e rr or(self, inputs , ou tput, exp ect ed ):     error= o utp ut - ex pected   d er ivative = self.lea rn i n g_ factor(o utput)      self. de lta  = e r r or *deri v at ive
       se l f. we ights_e rr or = [ ]      for i in  range (len(inp uts)) : 
            s e lf.weights_er r or . a pp e nd(s elf .delta * in puts [ i] *  sel f .l ear n ing_r ate)    self.bias_error =  s elf.de lta *  self.learnin g_rate

#N e t work
n1,  n2=  H i ddenNeuron(2),  Hidd enNeuron(2 ) 
n3 = Ou tputN euron(2) 
# Traini n g  data
tr ain ing _se t_i n =[[0, 0], [0,1 ] , [1 ,0], [1,  1] ]
train ing _se t_o ut=[0, 1, 1,  0]
# Tr ai ni ng  c ycl es
fo r  i in ra ng e(1 00 00) :
  for iin ra nge( le n( t rai ning_se t _i n ) ):
     #  Fe ed -forw ard      n1_ out = n1( tra i ning _s et _in[i]) 
        n2_o ut  = n2(trai ni ng_ set_in [ i ])
       n3_in = [n1_out, n2_ o ut]
      n3_ out= n3(n3_i n)       # Backpr opagat ion
   n3.ca l c _e rro r( n3_in, n3_ o u t,train in g_se t_out[i ])
        n2.calc _ err or(
self.bias -=_self.bias_error

class HiddenNeuron(Neuron):

_   def_calc_error(self, inputs,_output,_next_layer, number_in_layer):
__  _  _error = 0
 _  __  for n in range(len(next_layer)):
        __ _error_+=_next_layer[n].delta * next_layer[n].weights[number_in_layer - 1]
_    _ _derivative = self.learning_factor(output)
     __ self.delta =_error * derivative
      _ self.weights_error = []
  _  ___for i in range(len(inputs)):
_     _     self.weights_error.append(self.delta_* inputs[i] * self.learning_rate)
   _    self.bias_error =_self.delta * self.learning_rate

class OutputNeuron(Neuron):

 _ _def calc_error(self, inputs, output, expected):
_  _ _  error_= output - expected
  _  _  derivative = self.learning_factor(output)
 __  ___self.delta =_error * derivative
__ __   self.weights_error = []
__      for_i in range(len(inputs)):
 __   _    _self.weights_error.append(self.delta * inputs[i] * self.learning_rate)
 _ _ _  self.bias_error = self.delta * self.learning_rate

# Network
n1, n2 = HiddenNeuron(2),_HiddenNeuron(2)
n3 = OutputNeuron(2)
# Training data
training_set_in =_[[0, 0], [0, 1],_[1, 0],_[1,_1]]
training_set_out = [0,_1,_1, 0]
#_Training cycles
for i in range(10000):
  _ for_i in range(len(training_set_in)):
   _   _#_Feed-forward
     _  n1_out = n1(training_set_in[i])
 _    __n2_out_= n2(training_set_in[i])
   _    n3_in =_[n1_out, n2_out]
 _      n3_out_=_n3(n3_in)
     __ # Backpropagation
   ___  n3.calc_error(n3_in, n3_out, training_set_out[i])
_     _ n2.calc_error(