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：