When training a neural network, there should be 10 epochs, but when executing a command, there is an infinite increase in epochs with an index of 0 (Epoch 0). I can't find where I made a mistake.
In IDLE, I execute commands:
>>>import os
>>>os.chdir ('C:\\Python\\NeuralNetwork\\Networ k1')
>>>import mnist_loader
>>>training_data, validation_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)
network:
Код:
import random
import numpy as np
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 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 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 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 cost_derivative(self, output_activations, y):
return (output_activations-y)
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 sigmoid_prime(z):
return sigmoid(z)*(1-sigmoid(z))
net = Network([2, 3, 1])
def sigmoid(z):
return 1.0/(1.0+np.exp(-z))
def sigmoid_prime(z):
return sigmoid(z)*(1-sigmoid(z))
print('Сеть net:')
print('Количество 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))
mnist_loader:
Код:
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()
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