本文整理汇总了C++中NeuralNetwork::calculate_outputs方法的典型用法代码示例。如果您正苦于以下问题：C++ NeuralNetwork::calculate_outputs方法的具体用法？C++ NeuralNetwork::calculate_outputs怎么用？C++ NeuralNetwork::calculate_outputs使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类NeuralNetwork的用法示例。
在下文中一共展示了NeuralNetwork::calculate_outputs方法的2个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的C++代码示例。
示例1: inputs
Vector<double> Car::calculate_dependent_variables_dots(const NeuralNetwork& neural_network, const Vector<double>& variables) const
{
const double time = variables[0];
// const double position = variables[1];
const double velocity = variables[2];
const Vector<double> inputs(1, time);
const Vector<double> outputs = neural_network.calculate_outputs(inputs);
const double acceleration = outputs[0];
const double deceleration = outputs[1];
const double position_dot = velocity;
const double velocity_dot = acceleration + deceleration;
Vector<double> dependent_variables_dots(dependent_variables_number, 0.0);
dependent_variables_dots[0] = position_dot;
dependent_variables_dots[1] = velocity_dot;
return(dependent_variables_dots);
}示例2: test_calculate_outputs
void NeuralNetworkTest::test_calculate_outputs(void) {
message += "test_calculate_outputs\n";
NeuralNetwork nn;
unsigned inputs_number;
unsigned outputs_number;
Vector<unsigned> architecture;
Vector<double> inputs;
Vector<double> outputs;
unsigned parameters_number;
Vector<double> parameters;
// Test
nn.set(3, 4, 2);
nn.initialize_parameters(0.0);
inputs.set(3, 0.0);
outputs = nn.calculate_outputs(inputs);
assert_true(outputs == 0.0, LOG);
// Test
nn.set(1, 1, 1);
nn.initialize_parameters(0.0);
inputs.set(1, 0.0);
outputs = nn.calculate_outputs(inputs);
assert_true(outputs == 0.0, LOG);
// Test
nn.set(1, 1);
inputs.set(1);
inputs.randomize_normal();
parameters = nn.arrange_parameters();
assert_true(
nn.calculate_outputs(inputs) == nn.calculate_outputs(inputs, parameters),
LOG);
// Test
nn.set(4, 3, 5);
inputs.set(4, 0.0);
parameters_number = nn.count_parameters_number();
parameters.set(parameters_number, 0.0);
outputs = nn.calculate_outputs(inputs, parameters);
assert_true(outputs.size() == 5, LOG);
assert_true(outputs == 0.0, LOG);
// Test
architecture.set(5);
architecture.randomize_uniform(5, 10);
nn.set(architecture);
inputs_number = nn.get_inputs_pointer()->get_inputs_number();
outputs_number = nn.get_outputs_pointer()->get_outputs_number();
inputs.set(inputs_number, 0.0);
parameters_number = nn.count_parameters_number();
parameters.set(parameters_number, 0.0);
outputs = nn.calculate_outputs(inputs, parameters);
assert_true(outputs.size() == outputs_number, LOG);
assert_true(outputs == 0.0, LOG);
}