【deep learning学习笔记】注释yusugomori的DA代码 --- dA.cpp -- 模型测试

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：