做文案的网站有些什么软件,邯郸做移动网站价格表,网站如何维护,赣州网页设计师培训这是学习《Neural Network and Deep Learning》的笔记。在刚开始学习时#xff0c;电脑安装的是python3#xff0c;而作者用的是2.我想着反正要熟悉代码#xff0c;大学期间MATLAB用的比较多#xff0c;也懒得去折腾python2或是重写成python3.于是用MATLAB完成了里面的实例…这是学习《Neural Network and Deep Learning》的笔记。在刚开始学习时电脑安装的是python3而作者用的是2.我想着反正要熟悉代码大学期间MATLAB用的比较多也懒得去折腾python2或是重写成python3.于是用MATLAB完成了里面的实例和例题。这本书很适合新手特别是像我这种非科班的人。通过启发式的思想引导读者由浅入深自己动手操作代码成就感很强。这个笔记主要是书本里例子的操作还有习题的解答难免会有错误欢迎指正。
下面就是前两章的主要代码后面的内容都是在这基础上修改的。
function NW(a)
%NW([784,30,10])
global NetWork;
NetWork.length length(a);
for i2:1: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 i1:1:NetWork.length-1;
nabla_bi{i} zeros(size(NetWork.bias{i}));
end
for i1:1:NetWork.length-1;
nabla_wi{i} zeros(size(NetWork.weight{i}));
end
for i1:1:mini_batch_size
[delta_nabla_b,delta_nabla_w] backprop(mini_batch{1,1}(:,i),mini_batch{1,2}(i));
for j1: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 i1:1:NetWork.length-1;
nabla_b{i} zeros(size(NetWork.bias{i}));
end
for i1:1:NetWork.length-1;
nabla_w{i} zeros(size(NetWork.weight{i}));
end
% 向前传播
activation x./256;
activations{1} activation;
for i1:NetWork.length-1
z NetWork.weight{i}*activationNetWork.bias{i};
zs{i} z;
activation sigmoid(z);
activations{i1} activation;
end
% 向后传播
% 输出层误差
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 iNetWork.length-2:-1:1
z zs{i};
sp sigmoid_prime(z);
delta (NetWork.weight{i1}*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(y1) 1;
c output_activations - y1;
end
function s sigmoid(z)
s (1./(1exp(-z)));
end
function sp sigmoid_prime(z)
sp (sigmoid(z).*(1-sigmoid(z)));
end
