hope i didn't break anything

Makefile

@@ -3,7 +3,7 @@ CUDNN=0
 OPENCV=0
 DEBUG=0
 
-ARCH= --gpu-architecture=compute_20 --gpu-code=compute_20 
+ARCH= --gpu-architecture=compute_52 --gpu-code=compute_52 
 
 VPATH=./src/
 EXEC=darknet

cfg/darknet.cfg

@@ -14,7 +14,6 @@ power=4
 max_batches=500000
 
 [convolutional]
-batch_normalize=1
 filters=16
 size=3
 stride=1
@@ -26,7 +25,6 @@ size=2
 stride=2
 
 [convolutional]
-batch_normalize=1
 filters=32
 size=3
 stride=1
@@ -38,7 +36,6 @@ size=2
 stride=2
 
 [convolutional]
-batch_normalize=1
 filters=64
 size=3
 stride=1
@@ -50,7 +47,6 @@ size=2
 stride=2
 
 [convolutional]
-batch_normalize=1
 filters=128
 size=3
 stride=1
@@ -62,7 +58,6 @@ size=2
 stride=2
 
 [convolutional]
-batch_normalize=1
 filters=256
 size=3
 stride=1
@@ -74,7 +69,6 @@ size=2
 stride=2
 
 [convolutional]
-batch_normalize=1
 filters=512
 size=3
 stride=1
@@ -86,7 +80,6 @@ size=2
 stride=2
 
 [convolutional]
-batch_normalize=1
 filters=1024
 size=3
 stride=1

cfg/imagenet1k.dataset

@@ -0,0 +1,9 @@
+classes=1000
+labels = data/inet.labels.list
+names = data/shortnames.txt
+train = /data/imagenet/imagenet1k.train.list
+valid = /data/imagenet/imagenet1k.valid.list
+top=5
+test = /Users/pjreddie/Documents/sites/selfie/paths.list
+backup = /home/pjreddie/backup/
+

src/classifier.c

@@ -38,7 +38,7 @@ list *read_data_cfg(char *filename)
     return options;
 }
 
-void train_classifier(char *datacfg, char *cfgfile, char *weightfile)
+void train_classifier(char *datacfg, char *cfgfile, char *weightfile, int clear)
 {
     data_seed = time(0);
     srand(time(0));
@@ -49,6 +49,7 @@ void train_classifier(char *datacfg, char *cfgfile, char *weightfile)
     if(weightfile){
         load_weights(&net, weightfile);
     }
+    if(clear) *net.seen = 0;
     printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay);
     int imgs = net.batch;
 
@@ -96,14 +97,14 @@ void train_classifier(char *datacfg, char *cfgfile, char *weightfile)
         printf("Loaded: %lf seconds\n", sec(clock()-time));
         time=clock();
 
-/*
+        /*
-        int u;
+           int u;
-        for(u = 0; u < net.batch; ++u){
+           for(u = 0; u < net.batch; ++u){
-            image im = float_to_image(net.w, net.h, 3, train.X.vals[u]);
+           image im = float_to_image(net.w, net.h, 3, train.X.vals[u]);
-            show_image(im, "loaded");
+           show_image(im, "loaded");
-            cvWaitKey(0);
+           cvWaitKey(0);
-        }
+           }
-        */
+         */
 
         float loss = train_network(net, train);
         if(avg_loss == -1) avg_loss = loss;
@@ -116,7 +117,7 @@ void train_classifier(char *datacfg, char *cfgfile, char *weightfile)
             sprintf(buff, "%s/%s_%d.weights",backup_directory,base, epoch);
             save_weights(net, buff);
         }
-        if(*net.seen%100 == 0){
+        if(get_current_batch(net)%100 == 0){
             char buff[256];
             sprintf(buff, "%s/%s.backup",backup_directory,base);
             save_weights(net, buff);
@@ -378,8 +379,8 @@ void validate_classifier_single(char *datacfg, char *filename, char *weightfile)
         //cvWaitKey(0);
         float *pred = network_predict(net, crop.data);
 
+        if(resized.data != im.data) free_image(resized);
         free_image(im);
-        free_image(resized);
         free_image(crop);
         top_k(pred, classes, topk, indexes);
 
@@ -441,7 +442,7 @@ void validate_classifier_multi(char *datacfg, char *filename, char *weightfile)
             flip_image(r);
             p = network_predict(net, r.data);
             axpy_cpu(classes, 1, p, 1, pred, 1);
-            free_image(r);
+            if(r.data != im.data) free_image(r);
         }
         free_image(im);
         top_k(pred, classes, topk, indexes);
@@ -501,6 +502,46 @@ void predict_classifier(char *datacfg, char *cfgfile, char *weightfile, char *fi
     }
 }
 
+
+void label_classifier(char *datacfg, char *filename, char *weightfile)
+{
+    int i;
+    network net = parse_network_cfg(filename);
+    set_batch_network(&net, 1);
+    if(weightfile){
+        load_weights(&net, weightfile);
+    }
+    srand(time(0));
+
+    list *options = read_data_cfg(datacfg);
+
+    char *label_list = option_find_str(options, "names", "data/labels.list");
+    char *test_list = option_find_str(options, "test", "data/train.list");
+    int classes = option_find_int(options, "classes", 2);
+
+    char **labels = get_labels(label_list);
+    list *plist = get_paths(test_list);
+
+    char **paths = (char **)list_to_array(plist);
+    int m = plist->size;
+    free_list(plist);
+
+    for(i = 0; i < m; ++i){
+        image im = load_image_color(paths[i], 0, 0);
+        image resized = resize_min(im, net.w);
+        image crop = crop_image(resized, (resized.w - net.w)/2, (resized.h - net.h)/2, net.w, net.h);
+        float *pred = network_predict(net, crop.data);
+
+        if(resized.data != im.data) free_image(resized);
+        free_image(im);
+        free_image(crop);
+        int ind = max_index(pred, classes);
+
+        printf("%s\n", labels[ind]);
+    }
+}
+
+
 void test_classifier(char *datacfg, char *cfgfile, char *weightfile, int target_layer)
 {
     int curr = 0;
@@ -649,6 +690,7 @@ void run_classifier(int argc, char **argv)
     }
 
     int cam_index = find_int_arg(argc, argv, "-c", 0);
+    int clear = find_arg(argc, argv, "-clear");
     char *data = argv[3];
     char *cfg = argv[4];
     char *weights = (argc > 5) ? argv[5] : 0;
@@ -656,9 +698,10 @@ void run_classifier(int argc, char **argv)
     char *layer_s = (argc > 7) ? argv[7]: 0;
     int layer = layer_s ? atoi(layer_s) : -1;
     if(0==strcmp(argv[2], "predict")) predict_classifier(data, cfg, weights, filename);
-    else if(0==strcmp(argv[2], "train")) train_classifier(data, cfg, weights);
+    else if(0==strcmp(argv[2], "train")) train_classifier(data, cfg, weights, clear);
     else if(0==strcmp(argv[2], "demo")) demo_classifier(data, cfg, weights, cam_index, filename);
     else if(0==strcmp(argv[2], "test")) test_classifier(data, cfg, weights, layer);
+    else if(0==strcmp(argv[2], "label")) label_classifier(data, cfg, weights);
     else if(0==strcmp(argv[2], "valid")) validate_classifier(data, cfg, weights);
     else if(0==strcmp(argv[2], "valid10")) validate_classifier_10(data, cfg, weights);
     else if(0==strcmp(argv[2], "validmulti")) validate_classifier_multi(data, cfg, weights);

src/convolutional_kernels.cu

@@ -161,6 +161,7 @@ void backward_convolutional_layer_gpu(convolutional_layer l, network_state state
             l.filter_updates_gpu);
 
     if(state.delta){
+        if(l.binary || l.xnor) swap_binary(&l);
         cudnnConvolutionBackwardData(cudnn_handle(),
                 &one,
                 l.filterDesc,
@@ -174,6 +175,7 @@ void backward_convolutional_layer_gpu(convolutional_layer l, network_state state
                 &one,
                 l.dsrcTensorDesc,
                 state.delta);
+        if(l.binary || l.xnor) swap_binary(&l);
     }
 
 #else

src/convolutional_layer.c

@@ -88,8 +88,8 @@ image get_convolutional_delta(convolutional_layer l)
     return float_to_image(w,h,c,l.delta);
 }
 
-#ifdef CUDNN
 size_t get_workspace_size(layer l){
+    #ifdef CUDNN
     size_t most = 0;
     size_t s = 0;
     cudnnGetConvolutionForwardWorkspaceSize(cudnn_handle(),
@@ -117,8 +117,10 @@ size_t get_workspace_size(layer l){
             &s);
     if (s > most) most = s;
     return most;
+    #else
+    return (size_t)l.out_h*l.out_w*l.size*l.size*l.c*sizeof(float);
+    #endif
 }
-#endif
 
 convolutional_layer make_convolutional_layer(int batch, int h, int w, int c, int n, int size, int stride, int pad, ACTIVATION activation, int batch_normalize, int binary, int xnor)
 {
@@ -154,8 +156,6 @@ convolutional_layer make_convolutional_layer(int batch, int h, int w, int c, int
     l.outputs = l.out_h * l.out_w * l.out_c;
     l.inputs = l.w * l.h * l.c;
 
-    l.col_image = calloc(out_h*out_w*size*size*c, sizeof(float));
-    l.workspace_size = out_h*out_w*size*size*c*sizeof(float);
     l.output = calloc(l.batch*out_h * out_w * n, sizeof(float));
     l.delta  = calloc(l.batch*out_h * out_w * n, sizeof(float));
 
@@ -255,10 +255,9 @@ convolutional_layer make_convolutional_layer(int batch, int h, int w, int c, int
             CUDNN_CONVOLUTION_BWD_FILTER_PREFER_FASTEST,
             0,
             &l.bf_algo);
+#endif
+#endif
     l.workspace_size = get_workspace_size(l);
-
-#endif
-#endif
     l.activation = activation;
 
     fprintf(stderr, "Convolutional Layer: %d x %d x %d image, %d filters -> %d x %d x %d image\n", h,w,c,n, out_h, out_w, n);
@@ -315,8 +314,6 @@ void resize_convolutional_layer(convolutional_layer *l, int w, int h)
     l->outputs = l->out_h * l->out_w * l->out_c;
     l->inputs = l->w * l->h * l->c;
 
-    l->col_image = realloc(l->col_image,
-            out_h*out_w*l->size*l->size*l->c*sizeof(float));
     l->output = realloc(l->output,
             l->batch*out_h * out_w * l->n*sizeof(float));
     l->delta  = realloc(l->delta,
@@ -328,7 +325,43 @@ void resize_convolutional_layer(convolutional_layer *l, int w, int h)
 
     l->delta_gpu =     cuda_make_array(l->delta, l->batch*out_h*out_w*l->n);
     l->output_gpu =    cuda_make_array(l->output, l->batch*out_h*out_w*l->n);
+    #ifdef CUDNN
+    cudnnSetTensor4dDescriptor(l->dsrcTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, l->batch, l->c, l->h, l->w); 
+    cudnnSetTensor4dDescriptor(l->ddstTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, l->batch, l->out_c, l->out_h, l->out_w); 
+    cudnnSetFilter4dDescriptor(l->dfilterDesc, CUDNN_DATA_FLOAT, CUDNN_TENSOR_NCHW, l->n, l->c, l->size, l->size); 
+
+    cudnnSetTensor4dDescriptor(l->srcTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, l->batch, l->c, l->h, l->w); 
+    cudnnSetTensor4dDescriptor(l->dstTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, l->batch, l->out_c, l->out_h, l->out_w); 
+    cudnnSetFilter4dDescriptor(l->filterDesc, CUDNN_DATA_FLOAT, CUDNN_TENSOR_NCHW, l->n, l->c, l->size, l->size); 
+    int padding = l->pad ? l->size/2 : 0;
+    cudnnSetConvolution2dDescriptor(l->convDesc, padding, padding, l->stride, l->stride, 1, 1, CUDNN_CROSS_CORRELATION);
+    cudnnGetConvolutionForwardAlgorithm(cudnn_handle(),
+            l->srcTensorDesc,
+            l->filterDesc,
+            l->convDesc,
+            l->dstTensorDesc,
+            CUDNN_CONVOLUTION_FWD_PREFER_FASTEST,
+            0,
+            &l->fw_algo);
+    cudnnGetConvolutionBackwardDataAlgorithm(cudnn_handle(),
+            l->filterDesc,
+            l->ddstTensorDesc,
+            l->convDesc,
+            l->dsrcTensorDesc,
+            CUDNN_CONVOLUTION_BWD_DATA_PREFER_FASTEST,
+            0,
+            &l->bd_algo);
+    cudnnGetConvolutionBackwardFilterAlgorithm(cudnn_handle(),
+            l->srcTensorDesc,
+            l->ddstTensorDesc,
+            l->convDesc,
+            l->dfilterDesc,
+            CUDNN_CONVOLUTION_BWD_FILTER_PREFER_FASTEST,
+            0,
+            &l->bf_algo);
+    #endif
 #endif
+    l->workspace_size = get_workspace_size(*l);
 }
 
 void add_bias(float *output, float *biases, int batch, int n, int size)
@@ -386,7 +419,7 @@ void forward_convolutional_layer(convolutional_layer l, network_state state)
         int n = out_h*out_w;
 
         char  *a = l.cfilters;
-        float *b = l.col_image;
+        float *b = state.workspace;
         float *c = l.output;
 
         for(i = 0; i < l.batch; ++i){
@@ -407,7 +440,7 @@ void forward_convolutional_layer(convolutional_layer l, network_state state)
     int n = out_h*out_w;
 
     float *a = l.filters;
-    float *b = l.col_image;
+    float *b = state.workspace;
     float *c = l.output;
 
     for(i = 0; i < l.batch; ++i){
@@ -439,7 +472,7 @@ void backward_convolutional_layer(convolutional_layer l, network_state state)
 
     for(i = 0; i < l.batch; ++i){
         float *a = l.delta + i*m*k;
-        float *b = l.col_image;
+        float *b = state.workspace;
         float *c = l.filter_updates;
 
         float *im = state.input+i*l.c*l.h*l.w;
@@ -451,11 +484,11 @@ void backward_convolutional_layer(convolutional_layer l, network_state state)
         if(state.delta){
             a = l.filters;
             b = l.delta + i*m*k;
-            c = l.col_image;
+            c = state.workspace;
 
             gemm(1,0,n,k,m,1,a,n,b,k,0,c,k);
 
-            col2im_cpu(l.col_image, l.c,  l.h,  l.w,  l.size,  l.stride, l.pad, state.delta+i*l.c*l.h*l.w);
+            col2im_cpu(state.workspace, l.c,  l.h,  l.w,  l.size,  l.stride, l.pad, state.delta+i*l.c*l.h*l.w);
         }
     }
 }

src/darknet.c

@@ -270,6 +270,8 @@ int main(int argc, char **argv)
         run_dice(argc, argv);
     } else if (0 == strcmp(argv[1], "writing")){
         run_writing(argc, argv);
+    } else if (0 == strcmp(argv[1], "3d")){
+        composite_3d(argv[2], argv[3], argv[4]);
     } else if (0 == strcmp(argv[1], "test")){
         test_resize(argv[2]);
     } else if (0 == strcmp(argv[1], "captcha")){

src/data.c

@@ -271,7 +271,7 @@ void fill_truth_region(char *path, float *truth, int classes, int num_boxes, int
     free(boxes);
 }
 
-void fill_truth_detection(char *path, float *truth, int classes, int flip, float dx, float dy, float sx, float sy)
+void fill_truth_detection(char *path, int num_boxes, float *truth, int classes, int flip, float dx, float dy, float sx, float sy)
 {
     char *labelpath = find_replace(path, "images", "labels");
     labelpath = find_replace(labelpath, "JPEGImages", "labels");
@@ -283,7 +283,7 @@ void fill_truth_detection(char *path, float *truth, int classes, int flip, float
     box_label *boxes = read_boxes(labelpath, &count);
     randomize_boxes(boxes, count);
     correct_boxes(boxes, count, dx, dy, sx, sy, flip);
-    if(count > 17) count = 17;
+    if(count > num_boxes) count = num_boxes;
     float x,y,w,h;
     int id;
     int i;
@@ -297,11 +297,11 @@ void fill_truth_detection(char *path, float *truth, int classes, int flip, float
 
         if (w < .01 || h < .01) continue;
 
-        truth[i*5] = id;
+        truth[i*5+0] = id;
-        truth[i*5+2] = x;
+        truth[i*5+1] = x;
-        truth[i*5+3] = y;
+        truth[i*5+2] = y;
-        truth[i*5+4] = w;
+        truth[i*5+3] = w;
-        truth[i*5+5] = h;
+        truth[i*5+4] = h;
     }
     free(boxes);
 }
@@ -601,7 +601,7 @@ data load_data_swag(char **paths, int n, int classes, float jitter)
     return d;
 }
 
-data load_data_detection(int n, int boxes, char **paths, int m, int w, int h, int classes, float jitter)
+data load_data_detection(int n, char **paths, int m, int w, int h, int boxes, int classes, float jitter)
 {
     char **random_paths = get_random_paths(paths, n, m);
     int i;
@@ -643,7 +643,7 @@ data load_data_detection(int n, int boxes, char **paths, int m, int w, int h, in
         if(flip) flip_image(sized);
         d.X.vals[i] = sized.data;
 
-        fill_truth_detection(random_paths[i], d.y.vals[i], classes, flip, dx, dy, 1./sx, 1./sy);
+        fill_truth_detection(random_paths[i], boxes, d.y.vals[i], classes, flip, dx, dy, 1./sx, 1./sy);
 
         free_image(orig);
         free_image(cropped);
@@ -669,12 +669,12 @@ void *load_thread(void *ptr)
         *a.d = load_data_augment(a.paths, a.n, a.m, a.labels, a.classes, a.min, a.max, a.size);
     } else if (a.type == STUDY_DATA){
         *a.d = load_data_study(a.paths, a.n, a.m, a.labels, a.classes, a.min, a.max, a.size);
-    } else if (a.type == DETECTION_DATA){
-        *a.d = load_data_detection(a.n, a.num_boxes, a.paths, a.m, a.classes, a.w, a.h, a.background);
     } else if (a.type == WRITING_DATA){
         *a.d = load_data_writing(a.paths, a.n, a.m, a.w, a.h, a.out_w, a.out_h);
     } else if (a.type == REGION_DATA){
         *a.d = load_data_region(a.n, a.paths, a.m, a.w, a.h, a.num_boxes, a.classes, a.jitter);
+    } else if (a.type == DETECTION_DATA){
+        *a.d = load_data_detection(a.n, a.paths, a.m, a.w, a.h, a.num_boxes, a.classes, a.jitter);
     } else if (a.type == SWAG_DATA){
         *a.d = load_data_swag(a.paths, a.n, a.classes, a.jitter);
     } else if (a.type == COMPARE_DATA){

src/data.h

@@ -70,7 +70,7 @@ void print_letters(float *pred, int n);
 data load_data_captcha(char **paths, int n, int m, int k, int w, int h);
 data load_data_captcha_encode(char **paths, int n, int m, int w, int h);
 data load_data(char **paths, int n, int m, char **labels, int k, int w, int h);
-data load_data_detection(int n, int boxes, char **paths, int m, int w, int h, int classes, float jitter);
+data load_data_detection(int n, char **paths, int m, int w, int h, int boxes, int classes, float jitter);
 data load_data_tag(char **paths, int n, int m, int k, int min, int max, int size);
 data load_data_augment(char **paths, int n, int m, char **labels, int k, int min, int max, int size);
 data load_data_study(char **paths, int n, int m, char **labels, int k, int min, int max, int size);

src/image.h

@@ -44,6 +44,7 @@ void saturate_exposure_image(image im, float sat, float exposure);
 void hsv_to_rgb(image im);
 void rgbgr_image(image im);
 void constrain_image(image im);
+void composite_3d(char *f1, char *f2, char *out);
 
 image grayscale_image(image im);
 image threshold_image(image im, float thresh);

src/layer.h

@@ -50,6 +50,7 @@ struct layer{
     int h,w,c;
     int out_h, out_w, out_c;
     int n;
+    int max_boxes;
     int groups;
     int size;
     int side;

src/network.c

@@ -137,6 +137,7 @@ network make_network(int n)
 
 void forward_network(network net, network_state state)
 {
+    state.workspace = net.workspace;
     int i;
     for(i = 0; i < net.n; ++i){
         state.index = i;
@@ -400,6 +401,7 @@ int resize_network(network *net, int w, int h)
     net->w = w;
     net->h = h;
     int inputs = 0;
+    size_t workspace_size = 0;
     //fprintf(stderr, "Resizing to %d x %d...", w, h);
     //fflush(stderr);
     for (i = 0; i < net->n; ++i){
@@ -419,12 +421,20 @@ int resize_network(network *net, int w, int h)
         }else{
             error("Cannot resize this type of layer");
         }
+        if(l.workspace_size > workspace_size) workspace_size = l.workspace_size;
         inputs = l.outputs;
         net->layers[i] = l;
         w = l.out_w;
         h = l.out_h;
         if(l.type == AVGPOOL) break;
     }
+#ifdef GPU
+        cuda_free(net->workspace);
+        net->workspace = cuda_make_array(0, (workspace_size-1)/sizeof(float)+1);
+#else
+        free(net->workspace);
+        net->workspace = calloc(1, (workspace_size-1)/sizeof(float)+1);
+#endif
     //fprintf(stderr, " Done!\n");
     return 0;
 }

src/parser.c

@@ -257,6 +257,7 @@ detection_layer parse_detection(list *options, size_params params)
     layer.softmax = option_find_int(options, "softmax", 0);
     layer.sqrt = option_find_int(options, "sqrt", 0);
 
+    layer.max_boxes = option_find_int_quiet(options, "max",30);
     layer.coord_scale = option_find_float(options, "coord_scale", 1);
     layer.forced = option_find_int(options, "forced", 0);
     layer.object_scale = option_find_float(options, "object_scale", 1);
@@ -600,8 +601,11 @@ network parse_network_cfg(char *filename)
     net.outputs = get_network_output_size(net);
     net.output = get_network_output(net);
     if(workspace_size){
+    //printf("%ld\n", workspace_size);
 #ifdef GPU
         net.workspace = cuda_make_array(0, (workspace_size-1)/sizeof(float)+1);
+#else
+        net.workspace = calloc(1, workspace_size);
 #endif
     }
     return net;

src/rnn.c

@@ -280,6 +280,104 @@ void test_char_rnn(char *cfgfile, char *weightfile, int num, char *seed, float t
     printf("\n");
 }
 
+void test_tactic_rnn(char *cfgfile, char *weightfile, int num, char *seed, float temp, int rseed, char *token_file)
+{
+    char **tokens = 0;
+    if(token_file){
+        size_t n;
+        tokens = read_tokens(token_file, &n);
+    }
+
+    srand(rseed);
+    char *base = basecfg(cfgfile);
+    fprintf(stderr, "%s\n", base);
+
+    network net = parse_network_cfg(cfgfile);
+    if(weightfile){
+        load_weights(&net, weightfile);
+    }
+    int inputs = get_network_input_size(net);
+
+    int i, j;
+    for(i = 0; i < net.n; ++i) net.layers[i].temperature = temp;
+    int c = 0;
+    int len = strlen(seed);
+    float *input = calloc(inputs, sizeof(float));
+    float *out;
+
+    while((c = getc(stdin)) != EOF){
+        input[c] = 1;
+        out = network_predict(net, input);
+        input[c] = 0;
+    }
+    for(i = 0; i < num; ++i){
+        for(j = 0; j < inputs; ++j){
+            if (out[j] < .0001) out[j] = 0;
+        }
+        int next = sample_array(out, inputs);
+        if(c == '.' && next == '\n') break;
+        c = next;
+        print_symbol(c, tokens);
+
+        input[c] = 1;
+        out = network_predict(net, input);
+        input[c] = 0;
+    }
+    printf("\n");
+}
+
+void valid_tactic_rnn(char *cfgfile, char *weightfile, char *seed)
+{
+    char *base = basecfg(cfgfile);
+    fprintf(stderr, "%s\n", base);
+
+    network net = parse_network_cfg(cfgfile);
+    if(weightfile){
+        load_weights(&net, weightfile);
+    }
+    int inputs = get_network_input_size(net);
+
+    int count = 0;
+    int words = 1;
+    int c;
+    int len = strlen(seed);
+    float *input = calloc(inputs, sizeof(float));
+    int i;
+    for(i = 0; i < len; ++i){
+        c = seed[i];
+        input[(int)c] = 1;
+        network_predict(net, input);
+        input[(int)c] = 0;
+    }
+    float sum = 0;
+    c = getc(stdin);
+    float log2 = log(2);
+    int in = 0;
+    while(c != EOF){
+        int next = getc(stdin);
+        if(next == EOF) break;
+        if(next < 0 || next >= 255) error("Out of range character");
+
+        input[c] = 1;
+        float *out = network_predict(net, input);
+        input[c] = 0;
+
+        if(c == '.' && next == '\n') in = 0;
+        if(!in) {
+            if(c == '>' && next == '>'){
+                in = 1;
+                ++words;
+            }
+            c = next;
+            continue;
+        }
+        ++count;
+        sum += log(out[next])/log2;
+        c = next;
+        printf("%d %d Perplexity: %4.4f    Word Perplexity: %4.4f\n", count, words, pow(2, -sum/count), pow(2, -sum/words));
+    }
+}
+
 void valid_char_rnn(char *cfgfile, char *weightfile, char *seed)
 {
     char *base = basecfg(cfgfile);
@@ -389,6 +487,8 @@ void run_char_rnn(int argc, char **argv)
     char *weights = (argc > 4) ? argv[4] : 0;
     if(0==strcmp(argv[2], "train")) train_char_rnn(cfg, weights, filename, clear, tokenized);
     else if(0==strcmp(argv[2], "valid")) valid_char_rnn(cfg, weights, seed);
+    else if(0==strcmp(argv[2], "validtactic")) valid_tactic_rnn(cfg, weights, seed);
     else if(0==strcmp(argv[2], "vec")) vec_char_rnn(cfg, weights, seed);
     else if(0==strcmp(argv[2], "generate")) test_char_rnn(cfg, weights, len, seed, temp, rseed, tokens);
+    else if(0==strcmp(argv[2], "generatetactic")) test_tactic_rnn(cfg, weights, len, seed, temp, rseed, tokens);
 }

src/utils.c

@@ -424,6 +424,13 @@ float variance_array(float *a, int n)
     return variance;
 }
 
+int constrain_int(int a, int min, int max)
+{
+    if (a < min) return min;
+    if (a > max) return max;
+    return a;
+}
+
 float constrain(float min, float max, float a)
 {
     if (a < min) return min;
@@ -431,6 +438,14 @@ float constrain(float min, float max, float a)
     return a;
 }
 
+float dist_array(float *a, float *b, int n, int sub)
+{
+    int i;
+    float sum = 0;
+    for(i = 0; i < n; i += sub) sum += pow(a[i]-b[i], 2);
+    return sqrt(sum);
+}
+
 float mse_array(float *a, int n)
 {
     int i;

src/utils.h

@@ -36,6 +36,7 @@ void scale_array(float *a, int n, float s);
 void translate_array(float *a, int n, float s);
 int max_index(float *a, int n);
 float constrain(float min, float max, float a);
+int constrain_int(int a, int min, int max);
 float mse_array(float *a, int n);
 float rand_normal();
 size_t rand_size_t();
@@ -46,6 +47,7 @@ float mean_array(float *a, int n);
 void mean_arrays(float **a, int n, int els, float *avg);
 float variance_array(float *a, int n);
 float mag_array(float *a, int n);
+float dist_array(float *a, float *b, int n, int sub);
 float **one_hot_encode(float *a, int n, int k);
 float sec(clock_t clocks);
 int find_int_arg(int argc, char **argv, char *arg, int def);