全连接层实现二分类 matlab
时间: 2023-11-07 20:58:23 浏览: 45
全连接层实现二分类的MATLAB代码如下:
```MATLAB
% 设置参数
input_size = size(train_data2, 2); % 输入层大小
hidden_size = 10; % 隐藏层大小
output_size = 1; % 输出层大小
% 初始化权重和偏置
weight_u1 = randn(input_size, hidden_size);
weight_v1 = randn(hidden_size, output_size);
bias1 = randn(hidden_size, 1);
bias2 = randn(output_size, 1);
% 定义激活函数
sigmoid = @(x) 1./(1 + exp(-x));
% 定义前向传播函数
forward = @(x) sigmoid(weight_v1' * sigmoid(weight_u1' * x' + bias1) + bias2);
% 定义损失函数
loss = @(y_pred, y_true) sum((y_pred - y_true).^2);
% 开始训练
learning_rate = 0.01;
num_epochs = 100;
for epoch = 1:num_epochs
for i = 1:size(train_data2, 1)
% 前向传播
x = train_data1(i, :)';
y_true = train_data2(i);
layer_2_output = forward(x);
% 反向传播
delta2 = 2 * (layer_2_output - y_true) .* layer_2_output .* (1 - layer_2_output);
delta1 = weight_v1 * delta2 .* sigmoid(weight_u1' * x + bias1) .* (1 - sigmoid(weight_u1' * x + bias1));
% 更新权重和偏置
weight_u1 = weight_u1 - learning_rate * delta1 * x';
weight_v1 = weight_v1 - learning_rate * delta2 * sigmoid(weight_u1' * x + bias1)';
bias1 = bias1 - learning_rate * delta1;
bias2 = bias2 - learning_rate * delta2;
end
end
% 判断学习效果
flag = learning_judge(train_data2, 0, weight_u1, weight_u2, weight_v1, weight_v2, train_data1, bias1, bias2);
```