From db0397cfaaf488364e3d2e1669dfefae2ee6ea73 Mon Sep 17 00:00:00 2001
From: Joseph Redmon <pjreddie@gmail.com>
Date: Mon, 14 Dec 2015 19:57:10 +0000
Subject: [PATCH] shortcut layers, msr networks
---
src/cost_layer.c | 81 ++++++++++++++++++++++++----------------
1 files changed, 48 insertions(+), 33 deletions(-)
diff --git a/src/cost_layer.c b/src/cost_layer.c
index 1f36232..7593490 100644
--- a/src/cost_layer.c
+++ b/src/cost_layer.c
@@ -26,70 +26,85 @@
return "sse";
}
-cost_layer *make_cost_layer(int batch, int inputs, COST_TYPE type)
+cost_layer make_cost_layer(int batch, int inputs, COST_TYPE cost_type, float scale)
{
fprintf(stderr, "Cost Layer: %d inputs\n", inputs);
- cost_layer *layer = calloc(1, sizeof(cost_layer));
- layer->batch = batch;
- layer->inputs = inputs;
- layer->type = type;
- layer->delta = calloc(inputs*batch, sizeof(float));
- layer->output = calloc(1, sizeof(float));
+ cost_layer l = {0};
+ l.type = COST;
+
+ l.scale = scale;
+ l.batch = batch;
+ l.inputs = inputs;
+ l.outputs = inputs;
+ l.cost_type = cost_type;
+ l.delta = calloc(inputs*batch, sizeof(float));
+ l.output = calloc(1, sizeof(float));
#ifdef GPU
- layer->delta_gpu = cuda_make_array(layer->delta, inputs*batch);
+ l.delta_gpu = cuda_make_array(l.delta, inputs*batch);
#endif
- return layer;
+ return l;
}
-void forward_cost_layer(cost_layer layer, network_state state)
+void resize_cost_layer(cost_layer *l, int inputs)
+{
+ l->inputs = inputs;
+ l->outputs = inputs;
+ l->delta = realloc(l->delta, inputs*l->batch*sizeof(float));
+#ifdef GPU
+ cuda_free(l->delta_gpu);
+ l->delta_gpu = cuda_make_array(l->delta, inputs*l->batch);
+#endif
+}
+
+void forward_cost_layer(cost_layer l, network_state state)
{
if (!state.truth) return;
- if(layer.type == MASKED){
+ if(l.cost_type == MASKED){
int i;
- for(i = 0; i < layer.batch*layer.inputs; ++i){
- if(state.truth[i] == 0) state.input[i] = 0;
+ for(i = 0; i < l.batch*l.inputs; ++i){
+ if(state.truth[i] == SECRET_NUM) state.input[i] = SECRET_NUM;
}
}
- copy_cpu(layer.batch*layer.inputs, state.truth, 1, layer.delta, 1);
- axpy_cpu(layer.batch*layer.inputs, -1, state.input, 1, layer.delta, 1);
- *(layer.output) = dot_cpu(layer.batch*layer.inputs, layer.delta, 1, layer.delta, 1);
- //printf("cost: %f\n", *layer.output);
+ copy_cpu(l.batch*l.inputs, state.truth, 1, l.delta, 1);
+ axpy_cpu(l.batch*l.inputs, -1, state.input, 1, l.delta, 1);
+ *(l.output) = dot_cpu(l.batch*l.inputs, l.delta, 1, l.delta, 1);
+ //printf("cost: %f\n", *l.output);
}
-void backward_cost_layer(const cost_layer layer, network_state state)
+void backward_cost_layer(const cost_layer l, network_state state)
{
- copy_cpu(layer.batch*layer.inputs, layer.delta, 1, state.delta, 1);
+ axpy_cpu(l.batch*l.inputs, l.scale, l.delta, 1, state.delta, 1);
}
#ifdef GPU
-void pull_cost_layer(cost_layer layer)
+void pull_cost_layer(cost_layer l)
{
- cuda_pull_array(layer.delta_gpu, layer.delta, layer.batch*layer.inputs);
+ cuda_pull_array(l.delta_gpu, l.delta, l.batch*l.inputs);
}
-void push_cost_layer(cost_layer layer)
+void push_cost_layer(cost_layer l)
{
- cuda_push_array(layer.delta_gpu, layer.delta, layer.batch*layer.inputs);
+ cuda_push_array(l.delta_gpu, l.delta, l.batch*l.inputs);
}
-void forward_cost_layer_gpu(cost_layer layer, network_state state)
+void forward_cost_layer_gpu(cost_layer l, network_state state)
{
if (!state.truth) return;
- if (layer.type == MASKED) {
- mask_ongpu(layer.batch*layer.inputs, state.input, state.truth);
+ if (l.cost_type == MASKED) {
+ mask_ongpu(l.batch*l.inputs, state.input, SECRET_NUM, state.truth);
}
-
- copy_ongpu(layer.batch*layer.inputs, state.truth, 1, layer.delta_gpu, 1);
- axpy_ongpu(layer.batch*layer.inputs, -1, state.input, 1, layer.delta_gpu, 1);
- cuda_pull_array(layer.delta_gpu, layer.delta, layer.batch*layer.inputs);
- *(layer.output) = dot_cpu(layer.batch*layer.inputs, layer.delta, 1, layer.delta, 1);
+ copy_ongpu(l.batch*l.inputs, state.truth, 1, l.delta_gpu, 1);
+ axpy_ongpu(l.batch*l.inputs, -1, state.input, 1, l.delta_gpu, 1);
+
+ cuda_pull_array(l.delta_gpu, l.delta, l.batch*l.inputs);
+ *(l.output) = dot_cpu(l.batch*l.inputs, l.delta, 1, l.delta, 1);
}
-void backward_cost_layer_gpu(const cost_layer layer, network_state state)
+void backward_cost_layer_gpu(const cost_layer l, network_state state)
{
- copy_ongpu(layer.batch*layer.inputs, layer.delta_gpu, 1, state.delta, 1);
+ axpy_ongpu(l.batch*l.inputs, l.scale, l.delta_gpu, 1, state.delta, 1);
}
#endif
--
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