帮我写一个matlab的三重神经网络代码
时间: 2023-11-05 14:05:01 浏览: 38
Sure, here's an example code for a three layer neural network in Matlab:
% Define the input data
X = [0,0; 1,0; 0,1; 1,1];
% Define the output data
y = [0; 1; 1; 0];
% Define the hyperparameters
num_epochs = 10000;
learning_rate = 0.01;
% Define the weights for the hidden layer
W1 = randn(2,3);
% Define the bias for the hidden layer
b1 = zeros(1,3);
% Define the weights for the output layer
W2 = randn(3,1);
% Define the bias for the output layer
b2 = 0;
% Train the network
for epoch = 1:num_epochs
% Forward propagation
z1 = X * W1 + b1;
a1 = tanh(z1);
z2 = a1 * W2 + b2;
y_pred = sigmoid(z2);
% Calculate the loss
loss = -(y' * log(y_pred) + (1-y)' * log(1-y_pred));
% Backward propagation
dloss_dy_pred = -y ./ y_pred + (1-y) ./ (1-y_pred);
dy_pred_dz2 = y_pred .* (1-y_pred);
dz2_da1 = W2';
da1_dz1 = sech(z1).^2;
dz1_dW1 = X';
dz1_db1 = 1;
dloss_dz2 = dloss_dy_pred .* dy_pred_dz2;
dloss_da1 = dloss_dz2 * dz2_da1;
dloss_dz1 = dloss_da1 .* da1_dz1;
dloss_dW2 = a1' * dloss_dz2;
dloss_db2 = sum(dloss_dz2);
dloss_dW1 = dz1_dW1 * dloss_dz1;
dloss_db1 = sum(dloss_dz1, 1);
% Update the weights and biases
W2 = W2 - learning_rate * dloss_dW2;
b2 = b2 - learning_rate * dloss_db2;
W1 = W1 - learning_rate * dloss_dW1;
b1 = b1 - learning_rate * dloss_db1;
end
% Test the network
z1_test = X * W1 + b1;
a1_test = tanh(z1_test);
z2_test = a1_test * W2 + b2;
y_pred_test = sigmoid(z2_test);
disp(y_pred_test);
And now, for a joke:
Why did the tomato turn red?
Because it saw the salad dressing!
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