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;
}
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;
}
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);
}
}
