matlab - 在 Matlab 中用 feedforwardnet 模拟默认 patternnet？

Question

我用下面的网络得到了非常不同的训练效率

net = patternnet(hiddenLayerSize);

和下一个

net = feedforwardnet(hiddenLayerSize, 'trainscg');
net.layers{1}.transferFcn = 'tansig';
net.layers{2}.transferFcn = 'softmax';
net.performFcn = 'crossentropy';

在相同的数据上。

我在想网络应该是一样的。

我忘记了什么？

更新

下面的代码演示了网络行为唯一依赖于网络创建函数。

每种类型的网络都运行了两次。这不包括随机生成器问题或其他问题。数据相同。

hiddenLayerSize = 10;

% pass 1, with patternnet
net = patternnet(hiddenLayerSize);

net.divideParam.trainRatio = 70/100;
net.divideParam.valRatio = 15/100;
net.divideParam.testRatio = 15/100;

[net,tr] = train(net,x,t);

y = net(x);
performance = perform(net,t,y);

fprintf('pass 1, patternnet, performance: %f\n', performance);
fprintf('num_epochs: %d, stop: %s\n', tr.num_epochs, tr.stop);

% pass 2, with feedforwardnet
net = feedforwardnet(hiddenLayerSize, 'trainscg');
net.layers{1}.transferFcn = 'tansig';
net.layers{2}.transferFcn = 'softmax';
net.performFcn = 'crossentropy';

net.divideParam.trainRatio = 70/100;
net.divideParam.valRatio = 15/100;
net.divideParam.testRatio = 15/100;

[net,tr] = train(net,x,t);

y = net(x);
performance = perform(net,t,y);

fprintf('pass 2, feedforwardnet, performance: %f\n', performance);
fprintf('num_epochs: %d, stop: %s\n', tr.num_epochs, tr.stop);

% pass 1, with patternnet
net = patternnet(hiddenLayerSize);

net.divideParam.trainRatio = 70/100;
net.divideParam.valRatio = 15/100;
net.divideParam.testRatio = 15/100;

[net,tr] = train(net,x,t);

y = net(x);
performance = perform(net,t,y);

fprintf('pass 3, patternnet, performance: %f\n', performance);
fprintf('num_epochs: %d, stop: %s\n', tr.num_epochs, tr.stop);

% pass 2, with feedforwardnet
net = feedforwardnet(hiddenLayerSize, 'trainscg');
net.layers{1}.transferFcn = 'tansig';
net.layers{2}.transferFcn = 'softmax';
net.performFcn = 'crossentropy';

net.divideParam.trainRatio = 70/100;
net.divideParam.valRatio = 15/100;
net.divideParam.testRatio = 15/100;

[net,tr] = train(net,x,t);

y = net(x);
performance = perform(net,t,y);

fprintf('pass 4, feedforwardnet, performance: %f\n', performance);
fprintf('num_epochs: %d, stop: %s\n', tr.num_epochs, tr.stop);

输出如下:

pass 1, patternnet, performance: 0.116445
num_epochs: 353, stop: Validation stop.
pass 2, feedforwardnet, performance: 0.693561
num_epochs: 260, stop: Validation stop.
pass 3, patternnet, performance: 0.116445
num_epochs: 353, stop: Validation stop.
pass 4, feedforwardnet, performance: 0.693561
num_epochs: 260, stop: Validation stop.

Answer 1

看起来这两个不太一样:

>> net = patternnet(hiddenLayerSize);
>> net2 = feedforwardnet(hiddenLayerSize,'trainscg');
>> net.outputs{2}.processParams{2}

ans =

    ymin: 0
    ymax: 1

>> net2.outputs{2}.processParams{2}

ans =

    ymin: -1
    ymax: 1

net.outputs{2}.processFcns{2} 是 mapminmax 所以我知道其中之一是重新缩放它的输出以匹配输出范围你的真实数据更好。

为了将来引用，您可以做一些肮脏的事情，例如通过转换为 struct 来比较内部数据结构。所以我做了类似的事情

n = struct(net); n2 = struct(net2);
for fn=fieldnames(n)';
  if(~isequaln(n.(fn{1}),n2.(fn{1})))
    fprintf('fields %s differ\n', fn{1});
  end
end

帮助查明差异。