這是學習《Neural Network and Deep Learning》的筆記。在剛開始學習時，電腦安裝的是python3，而作者用的是2.我想著反正要熟悉代碼，大學期間MATLAB用的比較多，也懶得去折騰python2或是重寫成python3.于是用MATLAB完成了里面的實例和例題。這本書很適合新手，特別是像我這種非科班的人。通過啟發式的思想引導讀者由淺入深，自己動手操作代碼，成就感很強。這個筆記主要是書本里例子的操作，還有習題的解答，難免會有錯誤，歡迎指正。

下面就是前兩章的主要代碼，后面的內容都是在這基礎上修改的。

function NW(a)

%NW([784,30,10])

global NetWork;

NetWork.length = length(a);

for i=2:1:NetWork.length

NetWork.bias{i-1} = randn(a(i),1);

NetWork.weight{i-1} = randn(a(i),a(i-1));

end

end

function update_mini_batch(mini_batch,eta,mini_batch_size)

global NetWork;

for i=1:1:NetWork.length-1;

nabla_bi{i} = zeros(size(NetWork.bias{i}));

end

for i=1:1:NetWork.length-1;

nabla_wi{i} = zeros(size(NetWork.weight{i}));

end

for i=1:1:mini_batch_size

[delta_nabla_b,delta_nabla_w] = backprop(mini_batch{1,1}(:,i),mini_batch{1,2}(i));

for j=1:NetWork.length-1

nabla_bi{j} = nabla_bi{j} + delta_nabla_b{j};

nabla_wi{j} = nabla_wi{j} + delta_nabla_w{j};

end

end

for k = 1:NetWork.length-1

NetWork.weight{k}=NetWork.weight{k}-(eta/mini_batch_size)*nabla_wi{k};

NetWork.bias{k}=NetWork.bias{k}-(eta/mini_batch_size)*nabla_bi{k};

end

end

function [nabla_b,nabla_w] = backprop(x,y)

global NetWork;

for i=1:1:NetWork.length-1;

nabla_b{i} = zeros(size(NetWork.bias{i}));

end

for i=1:1:NetWork.length-1;

nabla_w{i} = zeros(size(NetWork.weight{i}));

end

% 向前傳播

activation = x./256;

activations{1} = activation;

for i=1:NetWork.length-1

z = NetWork.weight{i}*activation+NetWork.bias{i};

zs{i} = z;

activation = sigmoid(z);

activations{i+1} = activation;

end

% 向后傳播

% 輸出層誤差：

a = cost_derivative(activations{NetWork.length},y);

b = sigmoid_prime(zs{NetWork.length-1});

delta = cost_derivative(activations{NetWork.length},y).*sigmoid_prime(zs{NetWork.length-1});

nabla_b{NetWork.length-1} = delta;

nabla_w{NetWork.length-1} = delta*activations{NetWork.length-1}';

for i=NetWork.length-2:-1:1

z = zs{i};

sp = sigmoid_prime(z);

delta = (NetWork.weight{i+1}'*delta).*sp;

nabla_b{i}=delta;

nabla_w{i}=delta*activations{i}';

end

end

function c = cost_derivative(output_activations,y)

y1 = zeros(10,1);

y1(y+1) = 1;

c = output_activations - y1;

end

function s = sigmoid(z)

s = (1./(1+exp(-z)));

end

function sp = sigmoid_prime(z)

sp = (sigmoid(z).*(1-sigmoid(z)));

end

```

總結

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