C++ Parameters::GetNodesPerLayer方法代码示例

// Trains a neural net with the training set provided. The weights for the
// trained net will be stored in the file specified by the Parameter object.
void training(Parameters &params, vector<YearData> &trainingSet)
{
	string weights_filename = params.GetWeightsFileName();

	// Create a neural net
	Net ann(params.GetNodesPerLayer(), params.GetEta(), params.GetAlpha());

	// Set the weights according to the weights file
	ann.read_in_weights(weights_filename, params.GetNodesPerLayer());

	// Get number of training epochs
	int num_epochs = params.GetEpochs();

	// Get error threshold for stopping
	double error_thresh = params.GetErrorThresh();

	// Only present floats to the thousandths place
	cout << setprecision(3);

	// Epoch loop
	for (int i = 0; i < num_epochs; i++)
	{
		// Shuffle order of records
		random_shuffle(trainingSet.begin(), trainingSet.end());

		// Perform feed forward and back prop once for each record
		for (unsigned int j = 0; j < trainingSet.size(); j++)
		{
			ann.feed_forward(trainingSet[j].inputs);
			ann.back_prop(trainingSet[j].class_outputs);
		}

		// Stop if error threshold is reached
		if (ann.get_avg_error() < error_thresh)
		{
			cout << "Epoch" << setw(7) << i << ": RMS error = "
				<< ann.get_avg_error() << endl;
			cout << "Error Threshold Met" << endl;
			break;
		}

		// Print out the average RMS error for every ten epochs
		if (i % 10 == 0)
		{
			cout << "Epoch" << setw(7) << i << ": RMS error = "
				<< ann.get_avg_error() << endl;
			ann.reset_avg_error();
		}
	}

	// Save net's weights to the weight file
	ann.print_weights(weights_filename);

	return;
}

// Tests a neural net with the testing set provided.
void testing(Parameters &params, vector<YearData> &testSet)
{
	int numberCorrect = 0;		// count of number of correct predictions
	bool low, mid, high;		// bools to determine what the actual was
	unsigned int i;		// count of total number of test sets
	double percentCorrect;		// the percent of correct predictions
	vector<double> outputsFromNet;		// outputs from the net
	vector<double> expected_outputs;		// expected outputs
	double error = 0.0;		// current error of the output node
	double avg_error = 0.0;		// average error of the net

	// create the net
	Net ann(params.GetNodesPerLayer(), params.GetEta(), params.GetAlpha());

	// read in weight file
	ann.read_in_weights(params.GetWeightsFileName(), params.GetNodesPerLayer());

	// output the type of format
	cout << "Sample, Actual, Predicted" << endl;

	// loop through each test set
	for (i = 0; i < testSet.size(); i++)
	{
		// set the low, mid, and high bools to false
		low = false;
		mid = false;
		high = false;

		// perform the feed forward on the current test set
		ann.feed_forward(testSet[i].inputs);

		// get the output of the net for the current test set
		ann.get_output(outputsFromNet);

		// output the sample
		cout << i << ", ";

		// if the actual fire severity was mid
		if (testSet[i].actualburnedacres > params.GetFireSeverityCutoffs().at(0) &&
			testSet[i].actualburnedacres < params.GetFireSeverityCutoffs().at(1))
		{
			// output the mid
			cout << "010, ";
			mid = true;
		}
		// if the actual fire severity was high
		else if (testSet[i].actualburnedacres > params.GetFireSeverityCutoffs().at(1))
		{
			// output the high
			cout << "001, ";
			high = true;
		}
		// if the acutal fire severity was low
		else
		{
			// output the low
			cout << "100, ";
			low = true;
		}

		// if low fire severity is predicted
		if (outputsFromNet.at(0) > outputsFromNet.at(1) &&
			outputsFromNet.at(0) > outputsFromNet.at(2))
		{
			// output low
			cout << "100";

			// if it predicted correctly increment the count
			if (low)
				numberCorrect++;
			// if its wrong star that line
			else
				cout << ", *";
		}
		// if mid fire severity is predicted
		else if (outputsFromNet.at(1) > outputsFromNet.at(0) &&
			outputsFromNet.at(1) > outputsFromNet.at(2))
		{
			// output mid
			cout << "010";

			// if it predicted correctly increment the count
			if (mid)
				numberCorrect++;
			// if its wrong star that line
			else
				cout << ", *";
		}
		// if high fire severity is predcted
		else
		{
			// output high
			cout << "001";

			// if it predicted correctly increment the count
			if (high)
				numberCorrect++;
			// if its wrong star that line
			else
//.........这里部分代码省略.........

void crossValidate(Parameters &params, vector<YearData> &cvSet)
{
	//split cv set into training set and testing set
	vector<YearData> trainingSet;
	YearData testSet;
	int numberCorrect = 0;
	bool low, mid, high;
	double percentCorrect;
	vector<double> outputsFromNet;
	// Get number of training epochs
	int num_epochs = params.GetEpochs();

	// Get error threshold for stopping
	double error_thresh = params.GetErrorThresh();
	double avg_error;
	string break_error_thresh;

	cout << "Year, Burned, Actual, Predicted (training error)" << endl;

	for (unsigned int q = 0; q < cvSet.size(); q++)
	{
		trainingSet = vector<YearData>(cvSet);
		trainingSet.erase(trainingSet.begin() + q);
		testSet = YearData(cvSet.at(q));
		break_error_thresh.clear();
		// Create a neural net
		Net ann(params.GetNodesPerLayer(), params.GetEta(), params.GetAlpha());

		// Only present floats to the thousandths place
		cout << setprecision(3);

		// Epoch loop
		for (int i = 0; i < num_epochs; i++)
		{
			// Shuffle order of records
			random_shuffle(trainingSet.begin(), trainingSet.end());

			// Perform feed forward and back prop once for each record
			for (unsigned int j = 0; j < trainingSet.size(); j++)
			{
				ann.feed_forward(trainingSet[j].inputs);
				ann.back_prop(trainingSet[j].class_outputs);
			}

			// Stop if error threshold is reached
			if (ann.get_avg_error() < error_thresh)
			{
				break_error_thresh = " Reached error threshold at epoch " + i;
				break;
			}
		}
		avg_error = ann.get_avg_error();

		// set the low, mid, and high bools to false
		low = false;
		mid = false;
		high = false;

		// perform the feed forward on the current test set
		ann.feed_forward(testSet.inputs);

		// get the output of the net for the current test set
		ann.get_output(outputsFromNet);

		// output the sample
		cout << testSet.year << ", " << setw(6) << right << testSet.actualburnedacres << ", ";

		// if the actual fire severity was mid
		if (testSet.actualburnedacres > params.GetFireSeverityCutoffs().at(0) &&
			testSet.actualburnedacres < params.GetFireSeverityCutoffs().at(1))
		{
			// output the mid
			cout << setw(8) << right << "010, ";
			mid = true;
		}
		// if the actual fire severity was high
		else if (testSet.actualburnedacres > params.GetFireSeverityCutoffs().at(1))
		{
			// output the high
			cout << setw(8) << right << "001, ";
			high = true;
		}
		// if the acutal fire severity was low
		else
		{
			// output the low
			cout << setw(8) << right << "100, ";
			low = true;
		}

		// if low fire severity is predicted
		if (outputsFromNet.at(0) > outputsFromNet.at(1) &&
			outputsFromNet.at(0) > outputsFromNet.at(2))
		{


			// if it predicted correctly increment the count
			if (low)
			{
				cout << setw(9) << right << "100   ";
//.........这里部分代码省略.........