void trainNetwork(Network *nn){
// open MNIST files
FILE *imageFile, *labelFile;
imageFile = openMNISTImageFile(MNIST_TRAINING_SET_IMAGE_FILE_NAME);
labelFile = openMNISTLabelFile(MNIST_TRAINING_SET_LABEL_FILE_NAME);
int errCount = 0;
// Loop through all images in the file
for (int imgCount=0; imgCount<MNIST_MAX_TRAINING_IMAGES; imgCount++){
// Reading next image and its corresponding label
MNIST_Image img = getImage(imageFile);
MNIST_Label lbl = getLabel(labelFile);
// Convert the MNIST image to a standardized vector format and feed into the network
Vector *inpVector = getVectorFromImage(&img);
feedInput(nn, inpVector);
// Feed forward all layers (from input to hidden to output) calculating all nodes' output
feedForwardNetwork(nn);
// Back propagate the error and adjust weights in all layers accordingly
backPropagateNetwork(nn, lbl);
// Classify image by choosing output cell with highest output
int classification = getNetworkClassification(nn);
if (classification!=lbl) errCount++;
// Display progress during training
displayTrainingProgress(imgCount, errCount, 3,5);
// displayImage(&img, lbl, classification, 7,6);
}
// Close files
fclose(imageFile);
fclose(labelFile);
}
void AudioConverter::feedDecoder(AudioConverterComplexInputDataProc dataProc, void* opaque, AVFrame* srcaudio)
{
int gotFrame, err;
do
{
// Read input
if (!m_avpkt.size)
{
UInt32 numDataPackets = 0;
OSStatus err = feedInput(dataProc, opaque, numDataPackets);
if (err != noErr)
throw err;
if (!m_avpkt.size) // numDataPackets cannot be trusted
break;
}
err = avcodec_decode_audio4(m_decoder, srcaudio, &gotFrame, &m_avpkt);
if (err < 0)
throw std::runtime_error("avcodec_decode_audio4() failed");
m_avpkt.size -= err;
m_avpkt.data += err;
if (gotFrame)
{
if (!m_resampler)
setupResampler(srcaudio);
// Resample PCM
err = avresample_convert(m_resampler, nullptr, 0, 0, srcaudio->data, 0, srcaudio->nb_samples);
if (err < 0)
throw std::runtime_error("avresample_convert() failed");
}
}
while (!gotFrame);
}
void AntTweakBarSystem::process()
{
feedInput();
}
