WANN算法matlab
时间: 2023-09-03 18:10:37 浏览: 53
WANN(Weight Agnostic Neural Networks)算法是一种无需调整权重的神经网络算法。它的主要思想是构建一种通用的神经网络结构,使得该结构在不同的任务上都能够表现良好,而无需针对每个任务进行权重调整。该算法已经被应用于许多不同领域,例如强化学习、机器人控制和图像分类等。
以下是WANN算法的Matlab实现示例:
```matlab
function [y] = WANN(x)
% WANN algorithm implementation in Matlab
% x: input data
% y: network output
% define network structure
num_inputs = size(x, 1);
num_outputs = 1;
num_hidden_layers = 2;
num_neurons_per_layer = 5;
% initialize weights
weights = ones(1, num_neurons_per_layer * num_hidden_layers + ...
num_neurons_per_layer * num_inputs + num_neurons_per_layer * num_outputs);
% evaluate network
y = evaluate_network(x, weights, num_inputs, num_outputs, ...
num_hidden_layers, num_neurons_per_layer);
end
function [y] = evaluate_network(x, weights, num_inputs, num_outputs, ...
num_hidden_layers, num_neurons_per_layer)
% evaluate the network with given weights
% extract weights for each layer
start_index = 1;
end_index = num_neurons_per_layer * num_inputs;
input_weights = reshape(weights(start_index:end_index), ...
num_neurons_per_layer, num_inputs);
start_index = end_index + 1;
end_index = end_index + num_neurons_per_layer * num_hidden_layers;
hidden_weights = reshape(weights(start_index:end_index), ...
num_neurons_per_layer, num_hidden_layers);
start_index = end_index + 1;
end_index = end_index + num_neurons_per_layer * num_outputs;
output_weights = reshape(weights(start_index:end_index), ...
num_outputs, num_neurons_per_layer);
% evaluate network
hidden_layer = relu(input_weights * x);
for i = 1:num_hidden_layers-1
hidden_layer = relu(hidden_weights(:, i) * hidden_layer);
end
y = output_weights * hidden_layer;
end
function [y] = relu(x)
% rectified linear unit activation function
y = max(0, x);
end
```
这里的实现是一个非常简单的WANN网络,只有两个隐藏层,每个隐藏层有5个神经元。权重初始化为1,激活函数使用ReLU。你可以根据自己的需求进行修改和扩展。
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