static float testNetwork(NeuralNetwork& neuralNetwork, const Image& image,
    float noiseMagnitude, size_t iterations, size_t batchSize,
    std::default_random_engine& engine)
{
    float accuracy = 0.0f;

    iterations = std::max(iterations, 1UL);

    lucius::util::log("TestVisualization") << "Testing the accuracy of the trained network.\n";

    for(size_t i = 0; i != iterations; ++i)
    {
        lucius::util::log("TestVisualization") << " Iteration " << i << " out of "
            << iterations << "\n";
        
        ImageVector batch = generateBatch(image, noiseMagnitude,
            batchSize, engine);
        
        Matrix input = batch.convertToStandardizedMatrix(
            neuralNetwork.getInputCount(),
            neuralNetwork.getInputBlockingFactor(), image.colorComponents());
        
        Matrix reference = generateReference(batch);
        
        lucius::util::log("TestVisualization") << "  Input:     " << input.toString();
        lucius::util::log("TestVisualization") << "  Reference: " << reference.toString();
        
        accuracy += neuralNetwork.computeAccuracy(input, reference);
    }
    
    return accuracy * 100.0f / iterations;
}
Beispiel #2
0
xmlNode *
create_request_adv(const char *task, xmlNode *msg_data,
		   const char *host_to,  const char *sys_to,
		   const char *sys_from, const char *uuid_from,
		   const char *origin)
{
	char *true_from = NULL;
	xmlNode *request = NULL;
	char *reference = generateReference(task, sys_from);

	if (uuid_from != NULL) {
		true_from = generate_hash_key(sys_from, uuid_from);
	} else if(sys_from != NULL) {
		true_from = crm_strdup(sys_from);
	} else {
		crm_err("No sys from specified");
	}
	
	/* host_from will get set for us if necessary by CRMd when routed */
	request = create_xml_node(NULL, __FUNCTION__);
	crm_xml_add(request, F_CRM_ORIGIN,	origin);
	crm_xml_add(request, F_TYPE,		T_CRM);
	crm_xml_add(request, F_CRM_VERSION,	CRM_FEATURE_SET);
	crm_xml_add(request, F_CRM_MSG_TYPE,     XML_ATTR_REQUEST);
	crm_xml_add(request, XML_ATTR_REFERENCE, reference);
	crm_xml_add(request, F_CRM_TASK,		task);
	crm_xml_add(request, F_CRM_SYS_TO,       sys_to);
	crm_xml_add(request, F_CRM_SYS_FROM,     true_from);

	/* HOSTTO will be ignored if it is to the DC anyway. */
	if(host_to != NULL && strlen(host_to) > 0) {
		crm_xml_add(request, F_CRM_HOST_TO,  host_to);
	}

	if (msg_data != NULL) {
		add_message_xml(request, F_CRM_DATA, msg_data);
	}
	crm_free(reference);
	crm_free(true_from);
	
	return request;
}
Beispiel #3
0
ScriptEngine::ScriptEngine()
	: m_engine(asCreateScriptEngine())
{
	if (!m_engine)
		return;

	// Set the message callback to receive information on errors in human readable form.
	int r = m_engine->SetMessageCallback(asFUNCTION(MessageCallback), &m_errorString, asCALL_CDECL); assert(r >= 0);

	RegisterStdString(m_engine);
	//	RegisterStdStringUtils(engine);

	// The scripts can directly print text
	r = m_engine->RegisterGlobalFunction("void print(const string &in message)", asMETHOD(ScriptEngine, print), asCALL_THISCALL_ASGLOBAL, this); assert(r >= 0);

	RegisterScriptMath(m_engine);
	aatc::RegisterAllContainers(m_engine);
	
	registerObject(m_engine);
	registerAllTypes(m_engine);
	
	generateReference();
}
static void trainNetwork(NeuralNetwork& neuralNetwork, const Image& image,
    float noiseMagnitude, size_t iterations, size_t batchSize,
    std::default_random_engine& engine)
{
    lucius::util::log("TestVisualization") << "Training the network.\n";
    for(size_t i = 0; i != iterations; ++i)
    {
        lucius::util::log("TestVisualization") << " Iteration " << i << " out of "
            << iterations << "\n";
        ImageVector batch = generateBatch(image, noiseMagnitude,
            batchSize, engine);
        
        Matrix input = batch.convertToStandardizedMatrix(
            neuralNetwork.getInputCount(),
            neuralNetwork.getInputBlockingFactor(), image.colorComponents());
        
        Matrix reference = generateReference(batch);
        
        lucius::util::log("TestVisualization") << "  Input:     " << input.toString();
        lucius::util::log("TestVisualization") << "  Reference: " << reference.toString();
        
        neuralNetwork.train(input, reference);
    }
}