checkpoint

Makefile

@@ -14,6 +14,7 @@ endif
 
 UNAME = $(shell uname)
 OPTS=-Ofast -flto
+#OPTS=-O3
 ifeq ($(UNAME), Darwin)
 COMMON+= -isystem /usr/local/Cellar/opencv/2.4.6.1/include/opencv -isystem /usr/local/Cellar/opencv/2.4.6.1/include
 ifeq ($(GPU), 1)

src/activations.c

@@ -128,7 +128,7 @@ void activate_array_ongpu(cl_mem x, int n, ACTIVATION a)
 
     size_t gsize = n;
 
-    clEnqueueNDRangeKernel(queue, kernel, 1, 0, &gsize, 0, 0, 0, 0);
+    cl.error = clEnqueueNDRangeKernel(queue, kernel, 1, 0, &gsize, 0, 0, 0, 0);
     check_error(cl);
 }
 
@@ -158,7 +158,7 @@ void gradient_array_ongpu(cl_mem x, int n, ACTIVATION a, cl_mem delta)
 
     size_t gsize = n;
 
-    clEnqueueNDRangeKernel(queue, kernel, 1, 0, &gsize, 0, 0, 0, 0);
+    cl.error = clEnqueueNDRangeKernel(queue, kernel, 1, 0, &gsize, 0, 0, 0, 0);
     check_error(cl);
 }
 #endif

src/axpy.c

@@ -87,7 +87,7 @@ void axpy_ongpu_offset(int N, float ALPHA, cl_mem X, int OFFX, int INCX, cl_mem
 
     const size_t global_size[] = {N};
 
-    clEnqueueNDRangeKernel(queue, kernel, 1, 0, global_size, 0, 0, 0, 0);
+    cl.error = clEnqueueNDRangeKernel(queue, kernel, 1, 0, global_size, 0, 0, 0, 0);
     check_error(cl);
 
 }
@@ -113,7 +113,7 @@ void copy_ongpu_offset(int N, cl_mem X, int OFFX, int INCX, cl_mem Y, int OFFY,
 
     const size_t global_size[] = {N};
 
-    clEnqueueNDRangeKernel(queue, kernel, 1, 0, global_size, 0, 0, 0, 0);
+    cl.error = clEnqueueNDRangeKernel(queue, kernel, 1, 0, global_size, 0, 0, 0, 0);
     check_error(cl);
 }
 void scal_ongpu(int N, float ALPHA, cl_mem X, int INCX)
@@ -131,7 +131,7 @@ void scal_ongpu(int N, float ALPHA, cl_mem X, int INCX)
 
     const size_t global_size[] = {N};
 
-    clEnqueueNDRangeKernel(queue, kernel, 1, 0, global_size, 0, 0, 0, 0);
+    cl.error = clEnqueueNDRangeKernel(queue, kernel, 1, 0, global_size, 0, 0, 0, 0);
     check_error(cl);
 }
 #endif

src/cnn.c

@@ -265,10 +265,8 @@ void test_rotate()
 
 void test_parser()
 {
-	network net = parse_network_cfg("cfg/test_parser.cfg");
+	network net = parse_network_cfg("cfg/trained_imagenet.cfg");
-    save_network(net, "cfg/test_parser_1.cfg");
+    save_network(net, "cfg/trained_imagenet_smaller.cfg");
-	network net2 = parse_network_cfg("cfg/test_parser_1.cfg");
-    save_network(net2, "cfg/test_parser_2.cfg");
 }
 
 void test_data()
@@ -294,7 +292,8 @@ void train_asirra()
 		normalize_data_rows(train);
         printf("Loaded: %lf seconds\n", sec(clock()-time));
         time=clock();
-		float loss = train_network_data_gpu(net, train, imgs);
+		//float loss = train_network_data(net, train, imgs);
+        float loss = 0;
 		printf("%d: %f, Time: %lf seconds\n", i*net.batch*imgs, loss, sec(clock()-time));
 		free_data(train);
 		if(i%10==0){
@@ -309,7 +308,8 @@ void train_asirra()
 void train_imagenet()
 {
     float avg_loss = 1;
-	network net = parse_network_cfg("/home/pjreddie/imagenet_backup/imagenet_nin_2680.cfg");
+	network net = parse_network_cfg("/home/pjreddie/imagenet_backup/imagenet_2280.cfg");
+	//network net = parse_network_cfg("cfg/imagenet2.cfg");
     printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay);
     int imgs = 1000/net.batch+1;
 	srand(time(0));
@@ -335,7 +335,7 @@ void train_imagenet()
 		free_data(train);
 		if(i%10==0){
 			char buff[256];
-			sprintf(buff, "/home/pjreddie/imagenet_backup/imagenet_nin_%d.cfg", i);
+			sprintf(buff, "/home/pjreddie/imagenet_backup/imagenet_%d.cfg", i);
 			save_network(net, buff);
 		}
 	}
@@ -408,7 +408,7 @@ void test_imagenet()
     char filename[256];
     int indexes[10];
     while(1){
-        gets(filename);
+        fgets(filename, 256, stdin);
         image im = load_image_color(filename, 256, 256);
         z_normalize_image(im);
         printf("%d %d %d\n", im.h, im.w, im.c);
@@ -548,35 +548,16 @@ void train_nist()
     data train = load_categorical_data_csv("data/mnist/mnist_train.csv", 0, 10);
     data test = load_categorical_data_csv("data/mnist/mnist_test.csv",0,10);
     translate_data_rows(train, -144);
-    //scale_data_rows(train, 1./128);
     translate_data_rows(test, -144);
-    //scale_data_rows(test, 1./128);
-    //randomize_data(train);
     int count = 0;
-    //clock_t start = clock(), end;
+    int iters = 50000/net.batch;
-    int iters = 10000/net.batch;
     while(++count <= 2000){
         clock_t start = clock(), end;
         float loss = train_network_sgd(net, train, iters);
         end = clock();
         float test_acc = network_accuracy(net, test);
-        //float test_acc = 0;
+        printf("%d: Loss: %f, Test Acc: %f, Time: %lf seconds\n", count, loss, test_acc,(float)(end-start)/CLOCKS_PER_SEC);
-        printf("%d: Loss: %f, Test Acc: %f, Time: %lf seconds, LR: %f, Momentum: %f, Decay: %f\n", count, loss, test_acc,(float)(end-start)/CLOCKS_PER_SEC, net.learning_rate, net.momentum, net.decay);
-        /*printf("%f %f %f %f %f\n", mean_array(get_network_output_layer(net,0), 100),
-          mean_array(get_network_output_layer(net,1), 100),
-          mean_array(get_network_output_layer(net,2), 100),
-          mean_array(get_network_output_layer(net,3), 100),
-          mean_array(get_network_output_layer(net,4), 100));
-         */
-        //save_network(net, "cfg/nist_final2.cfg");
-
-        //printf("%5d Training Loss: %lf, Params: %f %f %f, ",count*1000, loss, lr, momentum, decay);
-        //end = clock();
-        //printf("Time: %lf seconds\n", (float)(end-start)/CLOCKS_PER_SEC);
-        //start=end;
-        //lr *= .5;
     }
-    //save_network(net, "cfg/nist_basic_trained.cfg");
 }
 
 void test_ensemble()
@@ -1052,6 +1033,7 @@ int main(int argc, char *argv[])
     }
     if(0==strcmp(argv[1], "train")) train_imagenet();
     else if(0==strcmp(argv[1], "asirra")) train_asirra();
+    else if(0==strcmp(argv[1], "nist")) train_nist();
     else if(0==strcmp(argv[1], "train_small")) train_imagenet_small();
     else if(0==strcmp(argv[1], "test_correct")) test_gpu_net();
     else if(0==strcmp(argv[1], "test")) test_imagenet();

src/col2im.c

@@ -82,7 +82,7 @@ void col2im_ongpu(cl_mem data_col,  int batch,
 
     size_t global_size = channels*height*width*batch;
 
-    clEnqueueNDRangeKernel(queue, kernel, 1, 0,
+    cl.error = clEnqueueNDRangeKernel(queue, kernel, 1, 0,
             &global_size, 0, 0, 0, 0);
     check_error(cl);
 }

src/connected_layer.c

@@ -9,7 +9,6 @@
 
 connected_layer *make_connected_layer(int batch, int inputs, int outputs, ACTIVATION activation, float learning_rate, float momentum, float decay)
 {
-    fprintf(stderr, "Connected Layer: %d inputs, %d outputs\n", inputs, outputs);
     int i;
     connected_layer *layer = calloc(1, sizeof(connected_layer));
 
@@ -51,6 +50,7 @@ connected_layer *make_connected_layer(int batch, int inputs, int outputs, ACTIVA
     layer->delta_cl = cl_make_array(layer->delta, outputs*batch);
     #endif
     layer->activation = activation;
+    fprintf(stderr, "Connected Layer: %d inputs, %d outputs\n", inputs, outputs);
     return layer;
 }
 

src/convolutional_layer.c

@@ -304,7 +304,7 @@ void learn_bias_convolutional_layer_ongpu(convolutional_layer layer)
 
     const size_t global_size[] = {layer.n};
 
-    clEnqueueNDRangeKernel(queue, kernel, 1, 0, global_size, 0, 0, 0, 0);
+    cl.error = clEnqueueNDRangeKernel(queue, kernel, 1, 0, global_size, 0, 0, 0, 0);
     check_error(cl);
 }
 
@@ -338,7 +338,7 @@ void bias_output_gpu(const convolutional_layer layer)
 
     const size_t global_size[] = {layer.n*size, layer.batch};
 
-    clEnqueueNDRangeKernel(queue, kernel, 2, 0, global_size, 0, 0, 0, 0);
+    cl.error = clEnqueueNDRangeKernel(queue, kernel, 2, 0, global_size, 0, 0, 0, 0);
     check_error(cl);
 }
 
@@ -400,7 +400,6 @@ void backward_convolutional_layer_gpu(convolutional_layer layer, cl_mem delta_cl
 
         gemm_ongpu_offset(0,1,m,n,k,1,a,i*m*k,k,b,i*k*n,k,1,c,0,n);
     }
-    //cl_read_array(layer.delta_cl, layer.delta, m*k*layer.batch);
 
     if(delta_cl){
         m = layer.size*layer.size*layer.c;

src/cost_layer.c

@@ -1,4 +1,5 @@
 #include "cost_layer.h"
+#include "utils.h"
 #include "mini_blas.h"
 #include <math.h>
 #include <stdlib.h>
@@ -36,11 +37,12 @@ void forward_cost_layer_gpu(cost_layer layer, cl_mem input, cl_mem truth)
 {
     if (!truth) return;
 
-
     copy_ongpu(layer.batch*layer.inputs, truth, 1, layer.delta_cl, 1);
     axpy_ongpu(layer.batch*layer.inputs, -1, input, 1, layer.delta_cl, 1);
+
     cl_read_array(layer.delta_cl, layer.delta, layer.batch*layer.inputs);
     *(layer.output) = dot_cpu(layer.batch*layer.inputs, layer.delta, 1, layer.delta, 1);
+    //printf("%f\n", *layer.output);
 }
 
 void backward_cost_layer_gpu(const cost_layer layer, cl_mem input, cl_mem delta)

src/data.c

@@ -19,6 +19,12 @@ list *get_paths(char *filename)
     return lines;
 }
 
+void fill_truth_det(char *path, float *truth)
+{
+    find_replace(path, "imgs", "det");
+    find_replace(path, ".JPEG", ".txt");
+}
+
 void fill_truth(char *path, char **labels, int k, float *truth)
 {
     int i;
@@ -83,7 +89,6 @@ void free_data(data d)
 
 data load_data_image_pathfile_part(char *filename, int part, int total, char **labels, int k, int h, int w)
 {
-    clock_t time = clock();
     list *plist = get_paths(filename);
     char **paths = (char **)list_to_array(plist);
     int start = part*plist->size/total;

src/dropout_layer.c

@@ -1,6 +1,7 @@
 #include "dropout_layer.h"
-#include "stdlib.h"
+#include "utils.h"
-#include "stdio.h"
+#include <stdlib.h>
+#include <stdio.h>
 
 dropout_layer *make_dropout_layer(int batch, int inputs, float probability)
 {
@@ -9,6 +10,10 @@ dropout_layer *make_dropout_layer(int batch, int inputs, float probability)
     layer->probability = probability;
     layer->inputs = inputs;
     layer->batch = batch;
+    #ifdef GPU
+    layer->rand = calloc(inputs*batch, sizeof(float));
+    layer->rand_cl = cl_make_array(layer->rand, inputs*batch);
+    #endif
     return layer;
 } 
 
@@ -16,7 +21,7 @@ void forward_dropout_layer(dropout_layer layer, float *input)
 {
     int i;
     for(i = 0; i < layer.batch * layer.inputs; ++i){
-        if((float)rand()/RAND_MAX < layer.probability) input[i] = 0;
+        if(rand_uniform() < layer.probability) input[i] = 0;
         else input[i] /= (1-layer.probability);
     }
 }
@@ -24,3 +29,38 @@ void backward_dropout_layer(dropout_layer layer, float *input, float *delta)
 {
     // Don't do shit LULZ
 }
+
+#ifdef GPU
+cl_kernel get_dropout_kernel()
+{
+    static int init = 0;
+    static cl_kernel kernel;
+    if(!init){
+        kernel = get_kernel("src/dropout_layer.cl", "forward", 0);
+        init = 1;
+    }
+    return kernel;
+}
+
+void forward_dropout_layer_gpu(dropout_layer layer, cl_mem input)
+{
+    int j;
+    int size = layer.inputs*layer.batch;
+    for(j = 0; j < size; ++j) layer.rand[j] = rand_uniform();
+    cl_write_array(layer.rand_cl, layer.rand, layer.inputs*layer.batch);
+
+    cl_kernel kernel = get_dropout_kernel();
+    cl_command_queue queue = cl.queue;
+
+    cl_uint i = 0;
+    cl.error = clSetKernelArg(kernel, i++, sizeof(input), (void*) &input);
+    cl.error = clSetKernelArg(kernel, i++, sizeof(layer.rand_cl), (void*) &layer.rand_cl);
+    cl.error = clSetKernelArg(kernel, i++, sizeof(layer.probability), (void*) &layer.probability);
+    check_error(cl);
+
+    const size_t global_size[] = {size};
+
+    cl.error = clEnqueueNDRangeKernel(queue, kernel, 1, 0, global_size, 0, 0, 0, 0);
+    check_error(cl);
+}
+#endif

src/dropout_layer.h

@@ -1,15 +1,23 @@
 #ifndef DROPOUT_LAYER_H
 #define DROPOUT_LAYER_H
+#include "opencl.h"
 
 typedef struct{
     int batch;
     int inputs;
     float probability;
+    #ifdef GPU
+    float *rand;
+    cl_mem rand_cl;
+    #endif
 } dropout_layer;
 
 dropout_layer *make_dropout_layer(int batch, int inputs, float probability);
 
 void forward_dropout_layer(dropout_layer layer, float *input);
 void backward_dropout_layer(dropout_layer layer, float *input, float *delta);
+    #ifdef GPU
+void forward_dropout_layer_gpu(dropout_layer layer, cl_mem input);
 
 #endif
+#endif

src/freeweight_layer.c

@@ -18,6 +18,7 @@ void forward_freeweight_layer(freeweight_layer layer, float *input)
         input[i] *= 2.*((float)rand()/RAND_MAX);
     }
 }
+
 void backward_freeweight_layer(freeweight_layer layer, float *input, float *delta)
 {
     // Don't do shit LULZ

src/gemm.c

@@ -214,7 +214,7 @@ void gemm_ongpu_offset(int TA, int TB, int M, int N, int K, float ALPHA,
     const size_t global_size[] = {ceil((float)N/BLOCK)*BLOCK, ceil((float)M/BLOCK)*BLOCK};
     const size_t local_size[] = {BLOCK, BLOCK};
 
-    clEnqueueNDRangeKernel(queue, gemm_kernel, 2, 0, global_size, local_size, 0, 0, 0);
+    cl.error = clEnqueueNDRangeKernel(queue, gemm_kernel, 2, 0, global_size, local_size, 0, 0, 0);
     check_error(cl);
     #endif
 }
@@ -368,6 +368,7 @@ void test_gpu_blas()
        test_gpu_accuracy(0,1,1000,10,100); 
        test_gpu_accuracy(1,1,1000,10,100); 
      */
+    time_ongpu(0,0,512,256,1152); 
     time_ongpu(0,0,128,1200,4096); 
     time_ongpu(0,0,128,1200,4096); 
     time_ongpu(0,0,128,1200,4096); 
@@ -377,6 +378,7 @@ void test_gpu_blas()
     time_ongpu(1,0,4096,1200,128); 
     time_ongpu(1,0,1200,128,4096); 
 
+    test_gpu_accuracy(0,0,512,256,1152); 
     test_gpu_accuracy(0,0,131,4093,1199); 
     test_gpu_accuracy(0,1,131,4093,1199); 
     test_gpu_accuracy(1,0,131,4093,1199); 

src/im2col.c

@@ -106,7 +106,7 @@ void im2col_ongpu(cl_mem data_im,  int batch,
 
     size_t global_size = batch*channels_col*height_col*width_col;
 
-    clEnqueueNDRangeKernel(queue, kernel, 1, 0,
+    cl.error = clEnqueueNDRangeKernel(queue, kernel, 1, 0,
             &global_size, 0, 0, 0, 0);
     check_error(cl);
 }

src/maxpool_layer.c

@@ -132,7 +132,7 @@ void forward_maxpool_layer_gpu(maxpool_layer layer, cl_mem input)
 
     const size_t global_size[] = {h*w*c*layer.batch};
 
-    clEnqueueNDRangeKernel(queue, kernel, 1, 0, global_size, 0, 0, 0, 0);
+    cl.error = clEnqueueNDRangeKernel(queue, kernel, 1, 0, global_size, 0, 0, 0, 0);
     check_error(cl);
 }
 
@@ -166,7 +166,7 @@ void backward_maxpool_layer_gpu(maxpool_layer layer, cl_mem delta)
 
     const size_t global_size[] = {layer.h*layer.w*layer.c*layer.batch};
 
-    clEnqueueNDRangeKernel(queue, kernel, 1, 0, global_size, 0, 0, 0, 0);
+    cl.error = clEnqueueNDRangeKernel(queue, kernel, 1, 0, global_size, 0, 0, 0, 0);
     check_error(cl);
 }
 

src/mini_blas.c

@@ -53,6 +53,7 @@ void time_random_matrix(int TA, int TB, int m, int k, int n)
 
 void test_blas()
 {
+
     time_random_matrix(0,0,100,100,100); 
     time_random_matrix(1,0,100,100,100); 
     time_random_matrix(0,1,100,100,100); 

src/network.c

@@ -476,25 +476,11 @@ void visualize_network(network net)
     } 
 }
 
-void top_predictions(network net, int n, int *index)
+void top_predictions(network net, int k, int *index)
 {
-    int i,j;
+    int size = get_network_output_size(net);
-    int k = get_network_output_size(net);
     float *out = get_network_output(net);
-    float thresh = FLT_MAX;
+    top_k(out, size, k, index);
-    for(i = 0; i < n; ++i){
-        float max = -FLT_MAX;
-        int max_i = -1;
-        for(j = 0; j < k; ++j){
-            float val = out[j];
-            if(val > max &&  val < thresh){
-                max = val;
-                max_i = j;
-            }
-        }
-        index[i] = max_i;
-        thresh = max;
-    }
 }
 
 

src/network_gpu.c

@@ -22,7 +22,9 @@ void forward_network_gpu(network net, cl_mem input, cl_mem truth, int train)
 {
     //printf("start\n");
     int i;
+   // printf("Truth: %f\n", cl_checksum(truth, 1000*net.batch));
     for(i = 0; i < net.n; ++i){
+        //printf("Truth %i: %f\n", i, cl_checksum(truth, 1000*net.batch));
         //clock_t time = clock();
         if(net.types[i] == CONVOLUTIONAL){
             convolutional_layer layer = *(convolutional_layer *)net.layers[i];
@@ -48,6 +50,11 @@ void forward_network_gpu(network net, cl_mem input, cl_mem truth, int train)
             forward_softmax_layer_gpu(layer, input);
             input = layer.output_cl;
         }
+        else if(net.types[i] == DROPOUT){
+            if(!train) continue;
+            dropout_layer layer = *(dropout_layer *)net.layers[i];
+            forward_dropout_layer_gpu(layer, input);
+        }
         //printf("%d %f\n", i, sec(clock()-time));
         /*
            else if(net.types[i] == CROP){
@@ -134,6 +141,8 @@ cl_mem get_network_output_cl_layer(network net, int i)
     else if(net.types[i] == SOFTMAX){
         softmax_layer layer = *(softmax_layer *)net.layers[i];
         return layer.output_cl;
+    } else if(net.types[i] == DROPOUT){
+        return get_network_output_cl_layer(net, i-1);
     }
     return 0;
 }
@@ -155,6 +164,8 @@ cl_mem get_network_delta_cl_layer(network net, int i)
     else if(net.types[i] == SOFTMAX){
         softmax_layer layer = *(softmax_layer *)net.layers[i];
         return layer.delta_cl;
+    } else if(net.types[i] == DROPOUT){
+        return get_network_delta_cl_layer(net, i-1);
     }
     return 0;
 }
@@ -173,14 +184,18 @@ float train_network_datum_gpu(network net, float *x, float *y)
     }
     //printf("trans %f\n", sec(clock()-time));
     //time = clock();
+
     forward_network_gpu(net, *net.input_cl, *net.truth_cl, 1);
+
     //printf("forw %f\n", sec(clock()-time));
     //time = clock();
     backward_network_gpu(net, *net.input_cl);
     //printf("back %f\n", sec(clock()-time));
     //time = clock();
+
     update_network_gpu(net);
     float error = get_network_cost(net);
+
     //printf("updt %f\n", sec(clock()-time));
     //time = clock();
     return error;

src/opencl.c

@@ -11,14 +11,16 @@
 
 #include "opencl.h"
 #include "utils.h"
+#include "activations.h"
 
 cl_info cl = {0};
 
 void check_error(cl_info info)
 {
-    clFinish(cl.queue);
+   // clFinish(cl.queue);
     if (info.error != CL_SUCCESS) {
         printf("\n Error number %d", info.error);
+        abort();
         exit(1);
     }
 }
@@ -72,6 +74,8 @@ cl_info cl_init()
         printf("  DEVICE_MAX_CLOCK_FREQUENCY = %u\n", (unsigned int)buf_uint);
         clGetDeviceInfo(devices[i], CL_DEVICE_GLOBAL_MEM_SIZE, sizeof(buf_ulong), &buf_ulong, NULL);
         printf("  DEVICE_GLOBAL_MEM_SIZE = %llu\n", (unsigned long long)buf_ulong);
+        clGetDeviceInfo(devices[i], CL_DEVICE_MAX_MEM_ALLOC_SIZE, sizeof(buf_ulong), &buf_ulong, NULL);
+        printf("  DEVICE_MAX_MEM_ALLOC_SIZE = %llu\n", (unsigned long long)buf_ulong);
         clGetDeviceInfo(devices[i], CL_DEVICE_MAX_WORK_GROUP_SIZE, sizeof(buf_ulong), &buf_ulong, NULL);
         printf("  DEVICE_MAX_WORK_GROUP_SIZE = %llu\n", (unsigned long long)buf_ulong);
         cl_uint items;
@@ -151,21 +155,31 @@ cl_kernel get_kernel(char *filename, char *kernelname, char *options)
 void cl_read_array(cl_mem mem, float *x, int n)
 {
     cl_setup();
-    clEnqueueReadBuffer(cl.queue, mem, CL_TRUE, 0, sizeof(float)*n,x,0,0,0);
+    cl.error = clEnqueueReadBuffer(cl.queue, mem, CL_TRUE, 0, sizeof(float)*n,x,0,0,0);
     check_error(cl);
 }
 
+float cl_checksum(cl_mem mem, int n)
+{
+    
+    float *x = calloc(n, sizeof(float));
+    cl_read_array(mem, x, n);
+    float sum = sum_array(x, n);
+    free(x);
+    return sum;
+}
+
 void cl_write_array(cl_mem mem, float *x, int n)
 {
     cl_setup();
-    clEnqueueWriteBuffer(cl.queue, mem, CL_TRUE, 0,sizeof(float)*n,x,0,0,0);
+    cl.error = clEnqueueWriteBuffer(cl.queue, mem, CL_TRUE, 0,sizeof(float)*n,x,0,0,0);
     check_error(cl);
 }
 
 void cl_copy_array(cl_mem src, cl_mem dst, int n)
 {
     cl_setup();
-    clEnqueueCopyBuffer(cl.queue, src, dst, 0, 0, sizeof(float)*n,0,0,0);
+    cl.error = clEnqueueCopyBuffer(cl.queue, src, dst, 0, 0, sizeof(float)*n,0,0,0);
     check_error(cl);
 }
 
@@ -179,6 +193,7 @@ cl_mem cl_sub_array(cl_mem src, int offset, int size)
     return sub;
 }
 
+
 cl_mem cl_make_array(float *x, int n)
 {
     cl_setup();
@@ -186,6 +201,7 @@ cl_mem cl_make_array(float *x, int n)
             CL_MEM_READ_WRITE|CL_MEM_COPY_HOST_PTR,
             sizeof(float)*n, x, &cl.error);
     check_error(cl);
+    activate_array_ongpu(mem, n, LINEAR);
     return mem;
 }
 

src/opencl.h

@@ -28,5 +28,6 @@ cl_mem cl_make_array(float *x, int n);
 cl_mem cl_make_int_array(int *x, int n);
 void cl_copy_array(cl_mem src, cl_mem dst, int n);
 cl_mem cl_sub_array(cl_mem src, int offset, int size);
+float cl_checksum(cl_mem mem, int n);
 #endif
 #endif

src/softmax_layer.c

@@ -81,10 +81,10 @@ void forward_softmax_layer_gpu(const softmax_layer layer, cl_mem input)
 
     const size_t global_size[] = {layer.batch};
 
-    clEnqueueNDRangeKernel(queue, kernel, 1, 0, global_size, 0, 0, 0, 0);
+    cl.error = clEnqueueNDRangeKernel(queue, kernel, 1, 0, global_size, 0, 0, 0, 0);
     check_error(cl);
 
-/*
+    /*
     cl_read_array(layer.output_cl, layer.output, layer.inputs*layer.batch);
     int z;
     for(z = 0; z < layer.inputs*layer.batch; ++z) printf("%f,",layer.output[z]);

src/utils.c

@@ -1,14 +1,51 @@
-#include "utils.h"
 #include <stdio.h>
 #include <stdlib.h>
 #include <string.h>
 #include <math.h>
+#include <float.h>
+
+#include "utils.h"
+
+char *find_replace(char *str, char *orig, char *rep)
+{
+    static char buffer[4096];
+    char *p;
+
+    if(!(p = strstr(str, orig)))  // Is 'orig' even in 'str'?
+        return str;
+
+    strncpy(buffer, str, p-str); // Copy characters from 'str' start to 'orig' st$
+    buffer[p-str] = '\0';
+
+    sprintf(buffer+(p-str), "%s%s", rep, p+strlen(orig));
+
+    return buffer;
+}
 
 float sec(clock_t clocks)
 {
     return (float)clocks/CLOCKS_PER_SEC;
 }
 
+void top_k(float *a, int n, int k, int *index)
+{
+    int i,j;
+    float thresh = FLT_MAX;
+    for(i = 0; i < k; ++i){
+        float max = -FLT_MAX;
+        int max_i = -1;
+        for(j = 0; j < n; ++j){
+            float val = a[j];
+            if(val > max &&  val < thresh){
+                max = val;
+                max_i = j;
+            }
+        }
+        index[i] = max_i;
+        thresh = max;
+    }
+}
+
 void error(char *s)
 {
     fprintf(stderr, "Error: %s\n", s);
@@ -79,7 +116,7 @@ char *fgetl(FILE *fp)
     }
 
     int curr = strlen(line);
-    
+
     while(line[curr-1]!='\n'){
         size *= 2;
         line = realloc(line, size*sizeof(char));
@@ -121,34 +158,34 @@ list *parse_csv_line(char *line)
 
 int count_fields(char *line)
 {
-	int count = 0;
+    int count = 0;
-	int done = 0;
+    int done = 0;
     char *c;
-	for(c = line; !done; ++c){
+    for(c = line; !done; ++c){
-		done = (*c == '\0');
+        done = (*c == '\0');
-		if(*c == ',' || done) ++count;
+        if(*c == ',' || done) ++count;
-	}
+    }
-	return count;
+    return count;
 }
 
 float *parse_fields(char *line, int n)
 {
-	float *field = calloc(n, sizeof(float));
+    float *field = calloc(n, sizeof(float));
-	char *c, *p, *end;
+    char *c, *p, *end;
-	int count = 0;
+    int count = 0;
-	int done = 0;
+    int done = 0;
-	for(c = line, p = line; !done; ++c){
+    for(c = line, p = line; !done; ++c){
-		done = (*c == '\0');
+        done = (*c == '\0');
-		if(*c == ',' || done){
+        if(*c == ',' || done){
-			*c = '\0';
+            *c = '\0';
-			field[count] = strtod(p, &end);
+            field[count] = strtod(p, &end);
-			if(p == c) field[count] = nan("");
+            if(p == c) field[count] = nan("");
-			if(end != c && (end != c-1 || *end != '\r')) field[count] = nan(""); //DOS file formats!
+            if(end != c && (end != c-1 || *end != '\r')) field[count] = nan(""); //DOS file formats!
-			p = c+1;
+            p = c+1;
-			++count;
+            ++count;
-		}
+        }
-	}
+    }
-	return field;
+    return field;
 }
 
 float sum_array(float *a, int n)

src/utils.h

@@ -4,11 +4,13 @@
 #include <time.h>
 #include "list.h"
 
+char *find_replace(char *str, char *orig, char *rep);
 void error(char *s);
 void malloc_error();
 void file_error(char *s);
 void strip(char *s);
 void strip_char(char *s, char bad);
+void top_k(float *a, int n, int k, int *index);
 list *split_str(char *s, char delim);
 char *fgetl(FILE *fp);
 list *parse_csv_line(char *line);