Ошибки при запуске процедуры исполнения нейронной сети

import os
os.chdir ('C:\\NeuralNetwork\\Network1')
import mnist_loader
training_data, validation_n_data, test_data = mnist_loader.load_data_wrapper()
import network
net = network.Network([784, 30, 10])
net.SGD(training_data, 30, 10, 3.0, test_data=test_data)

import random
 
import numpy as np # библиотека функций для работы с матрицами
 
class Network(object): 
    def __init__(self, sizes): 
        self.num_layers = len(sizes) 
        self.sizes = sizes 
        self.biases = [np.random.randn(y, 1) for y in sizes[1:]] 
        self.weights = [np.random.randn(y, x) for x, y in zip(sizes[:-1],sizes[1:])] 
 
    def feedforward(self, a): 
        for b, w in zip(self.biases, self.weights):
            a = sigmoid(np.dot(w, a)+b)
            return a
        
    def sigmoid(z): 
        return 1.0/(1.0+np.exp(-z))
 
    def sigmoid_prime(z):
        return sigmoid(z)*(1-sigmoid(z))
    
    def SGD( 
          self 
          , training_data 
          , epochs 
          , mini_batch_size 
          , eta 
          , test_data 
          ):
 
          test_data = list(test_data) 
          n_test = len(test_data) 
          training_data = list(training_data) 
          n = len(training_data) 
          for j in range(epochs): 
              random.shuffle(training_data)
 
              mini_batches = [training_data[k:k+mini_batch_size] for k in range(0, n, mini_batch_size)] 
 
              for mini_batch in mini_batches: 
                  self.update_mini_batch(mini_batch, eta) 
              print ("Epoch {0}: {1} / {2}".format(j,self.evaluate(test_data), n_test))
 
              def evaluate(self, test_data):
                  test_results = [(np.argmax(self.feedforward(x)), y)
                                  for (x, y) in test_data]
                  return sum(int(x == y) for (x, y) in test_results)
 
    def update_mini_batch( 
         self 
         , mini_batch 
         , eta 
         ):
        
         nabla_b = [np.zeros(b.shape) for b in self.biases] 
         nabla_w = [np.zeros(w.shape) for w in self.weights] 
         for x, y in mini_batch:
             delta_nabla_b, delta_nabla_w = self.backprop(x, y) 
             nabla_b = [nb+dnb for nb, dnb in zip(nabla_b, delta_nabla_b)] 
             nabla_w = [nw+dnw for nw, dnw in zip(nabla_w, delta_nabla_w)] 
         self.weights = [w-(eta/len(mini_batch))*nw
                        for w, nw in zip(self.weights, nabla_w)] 
         self.biases = [b-(eta/len(mini_batch))*nb
                       for b, nb in zip(self.biases, nabla_b)] 
 
 
    def backprop( 
         self
         , x
         , y
         ):
 
        nabla_b = [np.zeros(b.shape) for b in self.biases] 
        nabla_w = [np.zeros(w.shape) for w in self.weights] 
      
        activation = x 
        activations = [x] 
        zs = [] 
 
        for b, w in zip(self.biases, self.weights):
                z = np.dot(w, activation)+b 
                zs.append(z) 
                activation = sigmoid(z) 
                activations.append(activation) 
 
        delta = self.cost_derivative(activations[-1], y) * sigmoid_prime(zs[-1]) 
        nabla_b[-1] = delta 
        nabla_w[-1] = np.dot(delta, activations[-2].transpose()) 
        for l in range(2, self.num_layers):
            z = zs[-l] 
            sp = sigmoid_prime(z) 
            delta = np.dot(self.weights[-l+1].transpose(), delta) * sp 
            nabla_b[-l] = delta 
            nabla_w[-l] = np.dot(delta, activations[-l-1].transpose())
        return (nabla_b, nabla_w)
 
        def cost_derivative(self, output_activations, y): 
            return (output_activations-y)
 
net = Network([2, 3, 1])
 
print('Сеть net:')
print('Количетво слоев:', net.num_layers)
for i in range(net.num_layers):
    print('Количество нейронов в слое', i,':',net.sizes[i])
for i in range(net.num_layers-1):
    print('W_',i+1,':')
    print(np.round(net.weights[i],2))
    print('b_',i+1,':')
    print(np.round(net.biases[i],2))

import gzip 
import pickle 
import numpy as np 
 
def load_data():
    f = gzip.open('mnist.pkl.gz', 'rb') 
    training_data, validation_data, test_data = pickle.load(f,encoding='latin1') 
    f.close() # закрываем файл
    return (training_data, validation_data, test_data)
 
def load_data_wrapper():
    tr_d, va_d, te_d = load_data() 
    training_inputs = [np.reshape(x, (784, 1)) for x in tr_d[0]] 
    training_results = [vectorized_result(y) for y in tr_d[1]] 
    training_data = zip(training_inputs, training_results) 
    validation_inputs = [np.reshape(x, (784, 1)) for x in va_d[0]] 
    validation_data = zip(validation_inputs, va_d[1]) 
    test_inputs = [np.reshape(x, (784, 1)) for x in te_d[0]] 
    test_data = zip(test_inputs, te_d[1]) 
    return (training_data, validation_data, test_data)
 
 
def vectorized_result(j):
 
    e = np.zeros((10, 1))
    e[j] = 1.0
    return e