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/network.h | 69 +++++++++++++++++++++++-----------
1 files changed, 47 insertions(+), 22 deletions(-)
diff --git a/src/network.h b/src/network.h
index 66873d2..4c108df 100644
--- a/src/network.h
+++ b/src/network.h
@@ -3,32 +3,39 @@
#define NETWORK_H
#include "image.h"
+#include "layer.h"
#include "data.h"
+#include "params.h"
typedef enum {
- CONVOLUTIONAL,
- DECONVOLUTIONAL,
- CONNECTED,
- MAXPOOL,
- SOFTMAX,
- NORMALIZATION,
- DROPOUT,
- FREEWEIGHT,
- CROP,
- COST
-} LAYER_TYPE;
+ CONSTANT, STEP, EXP, POLY, STEPS, SIG
+} learning_rate_policy;
-typedef struct {
+typedef struct network{
int n;
int batch;
- int seen;
- float learning_rate;
+ int *seen;
+ float epoch;
+ int subdivisions;
float momentum;
float decay;
- void **layers;
- LAYER_TYPE *types;
+ layer *layers;
int outputs;
float *output;
+ learning_rate_policy policy;
+
+ float learning_rate;
+ float gamma;
+ float scale;
+ float power;
+ int step;
+ int max_batches;
+ float *scales;
+ int *steps;
+ int num_steps;
+
+ int inputs;
+ int h, w, c;
#ifdef GPU
float **input_gpu;
@@ -36,19 +43,35 @@
#endif
} network;
+typedef struct network_state {
+ float *truth;
+ float *input;
+ float *delta;
+ int train;
+ int index;
+ network net;
+} network_state;
+
#ifdef GPU
float train_network_datum_gpu(network net, float *x, float *y);
float *network_predict_gpu(network net, float *input);
float * get_network_output_gpu_layer(network net, int i);
float * get_network_delta_gpu_layer(network net, int i);
+float *get_network_output_gpu(network net);
+void forward_network_gpu(network net, network_state state);
+void backward_network_gpu(network net, network_state state);
+void update_network_gpu(network net);
#endif
+float get_current_rate(network net);
+int get_current_batch(network net);
+void free_network(network net);
void compare_networks(network n1, network n2, data d);
char *get_layer_string(LAYER_TYPE a);
-network make_network(int n, int batch);
-void forward_network(network net, float *input, float *truth, int train);
-void backward_network(network net, float *input);
+network make_network(int n);
+void forward_network(network net, network_state state);
+void backward_network(network net, network_state state);
void update_network(network net);
float train_network(network net, data d);
@@ -58,7 +81,7 @@
matrix network_predict_data(network net, data test);
float *network_predict(network net, float *input);
float network_accuracy(network net, data d);
-float *network_accuracies(network net, data d);
+float *network_accuracies(network net, data d, int n);
float network_accuracy_multi(network net, data d, int n);
void top_predictions(network net, int n, int *index);
float *get_network_output(network net);
@@ -72,11 +95,13 @@
int get_predicted_class_network(network net);
void print_network(network net);
void visualize_network(network net);
-int resize_network(network net, int h, int w, int c);
+int resize_network(network *net, int w, int h);
void set_batch_network(network *net, int b);
-void set_learning_network(network *net, float rate, float momentum, float decay);
int get_network_input_size(network net);
float get_network_cost(network net);
+int get_network_nuisance(network net);
+int get_network_background(network net);
+
#endif
--
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