测试代码。能看到,训练的时候是单个样本、单个样本的训练的,在NN中是属于“stochastic gradient descent”,否则,一批样本在一起的,就是“standard gradient descent”。
void test_dA() { srand(0); double learning_rate = 0.1; double corruption_level = 0.3; int training_epochs = 100; int train_N = 10; int test_N = 2; int n_visible = 20; int n_hidden = 5; // training data int train_X[10][20] = { {1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}, {1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}, {1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}, {1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}, {0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}, {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1}, {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1}, {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1}, {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1}, {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0} }; // construct dA dA da(train_N, n_visible, n_hidden, NULL, NULL, NULL); // train for(int epoch=0; epoch<training_epochs; epoch++) { // train it sample by sample for(int i=0; i<train_N; i++) { da.train(train_X[i], learning_rate, corruption_level); } } // test data int test_X[2][20] = { {1, 0, 1, 0, 1, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}, {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0} }; double reconstructed_X[2][20]; // test for(int i=0; i<test_N; i++) { da.reconstruct(test_X[i], reconstructed_X[i]); for(int j=0; j<n_visible; j++) { printf("%.5f ", reconstructed_X[i][j]); } cout << endl; } cout << endl; } int main() { test_dA(); getchar(); return 0; }程序运行结果: