Question

我有200个训练样例。我在这个数据集上运行了6个特征的线性回归，它运行正常，所以我也希望在它上运行nueral networs。

问题：每次运行程序时，预测（pred）都是不同的，差别很大！

input_layer_size  = 6;
hidden_layer_size = 3;   
num_labels = 1;

% Load Training Data

load('capitaldata.mat');

% example size

m = size(X, 1);

% initialize theta

initial_Theta1 = randInitializeWeights(input_layer_size, hidden_layer_size);
initial_Theta2 = randInitializeWeights(hidden_layer_size, num_labels);

% Unroll parameters

initial_nn_params = [initial_Theta1(:) ; initial_Theta2(:)];

% find optimal theta

options = optimset('MaxIter', 50);

%  set regularization parameter

lambda = 1;

% Create "short hand" for the cost function to be minimized

costFunction = @(p) nnCostFunctionLinear(p, input_layer_size, hidden_layer_size, num_labels, X, y, lambda);

% Now, costFunction is a function that takes in only one argument (the neural network parameters)

[nn_params, cost] = fmincg(costFunction, initial_nn_params, options);

% Obtain Theta1 and Theta2 back from nn_params

Theta1 = reshape(nn_params(1:hidden_layer_size * (input_layer_size + 1)), hidden_layer_size, (input_layer_size + 1));

Theta2 = reshape(nn_params((1 + (hidden_layer_size * (input_layer_size + 1))):end), num_labels, (hidden_layer_size + 1));

% test case
test = [18 279 86 59 23 16]; 

pred = predict(Theta1, Theta2, test);

display(pred);

上述程序调用的函数：

1）randInitializeWeights.m

function W = randInitializeWeights(L_in, L_out)

W = zeros(L_out, 1 + L_in);

epsilon_init = 0.12;

W = rand(L_out , 1 + L_in)  * 2 * epsilon_init - epsilon_init;

end;

2）nnCostFunctionLinear.m应该是正确的，因为测试结果是正确的。如果你想看到它，请告诉我。

我怀疑问题是数据集大小，功能数量或初始化权重。

提前感谢您的帮助！

Answer 1

作为测试，您可以每次使用相同的值为随机数生成器播种，以便每次都给出相同的随机数序列。搜索

随机种子

以及用于查找如何为随机数生成器设置种子的软件名称。

线性回归的Nueral网络：每次预测都不同

1 个答案: