| | |
|---|
| | | void train_imagenet(char *cfgfile, char *weightfile) |
|---|
| | | { |
|---|
| | | float avg_loss = -1; |
|---|
| | | // TODO |
|---|
| | | srand(0); |
|---|
| | | srand(time(0)); |
|---|
| | | char *base = basename(cfgfile); |
|---|
| | | printf("%s\n", base); |
|---|
| | | network net = parse_network_cfg(cfgfile); |
|---|
| | |
|---|
| | | cvWaitKey(0); |
|---|
| | | } |
|---|
| | | |
|---|
| | | #ifdef GPU |
|---|
| | | void test_convolutional_layer() |
|---|
| | | { |
|---|
| | | network net = parse_network_cfg("cfg/nist_conv.cfg"); |
|---|
| | | int size = get_network_input_size(net); |
|---|
| | | float *in = calloc(size, sizeof(float)); |
|---|
| | | int i; |
|---|
| | | for(i = 0; i < size; ++i) in[i] = rand_normal(); |
|---|
| | | convolutional_layer layer = *(convolutional_layer *)net.layers[0]; |
|---|
| | | int out_size = convolutional_out_height(layer)*convolutional_out_width(layer)*layer.batch; |
|---|
| | | cuda_compare(layer.output_gpu, layer.output, out_size, "nothing"); |
|---|
| | | cuda_compare(layer.biases_gpu, layer.biases, layer.n, "biases"); |
|---|
| | | cuda_compare(layer.filters_gpu, layer.filters, layer.n*layer.size*layer.size*layer.c, "filters"); |
|---|
| | | bias_output(layer); |
|---|
| | | bias_output_gpu(layer); |
|---|
| | | cuda_compare(layer.output_gpu, layer.output, out_size, "biased output"); |
|---|
| | | } |
|---|
| | | #endif |
|---|
| | | |
|---|
| | | void test_correct_nist() |
|---|
| | | { |
|---|
| | | network net = parse_network_cfg("cfg/nist_conv.cfg"); |
|---|
