From d9f1b0b16edeb59281355a855e18a8be343fc33c Mon Sep 17 00:00:00 2001
From: Joseph Redmon <pjreddie@gmail.com>
Date: Fri, 08 Aug 2014 19:04:15 +0000
Subject: [PATCH] probably how maxpool layers should be
---
src/network.c | 369 +++++++++++++++++++++++++++++++++++++++++++++-------
1 files changed, 320 insertions(+), 49 deletions(-)
diff --git a/src/network.c b/src/network.c
index cce673c..ed927a8 100644
--- a/src/network.c
+++ b/src/network.c
@@ -7,18 +7,71 @@
#include "connected_layer.h"
#include "convolutional_layer.h"
#include "maxpool_layer.h"
+#include "normalization_layer.h"
#include "softmax_layer.h"
+#include "dropout_layer.h"
-network make_network(int n)
+network make_network(int n, int batch)
{
network net;
net.n = n;
+ net.batch = batch;
net.layers = calloc(net.n, sizeof(void *));
net.types = calloc(net.n, sizeof(LAYER_TYPE));
+ net.outputs = 0;
+ net.output = 0;
+ #ifdef GPU
+ net.input_cl = 0;
+ #endif
return net;
}
-void forward_network(network net, double *input)
+#ifdef GPU
+void forward_network(network net, float *input, int train)
+{
+ cl_setup();
+ size_t size = get_network_input_size(net);
+ if(!net.input_cl){
+ net.input_cl = clCreateBuffer(cl.context,
+ CL_MEM_READ_WRITE, size*sizeof(float), 0, &cl.error);
+ check_error(cl);
+ }
+ cl_write_array(net.input_cl, input, size);
+ cl_mem input_cl = net.input_cl;
+ int i;
+ for(i = 0; i < net.n; ++i){
+ if(net.types[i] == CONVOLUTIONAL){
+ convolutional_layer layer = *(convolutional_layer *)net.layers[i];
+ forward_convolutional_layer_gpu(layer, input_cl);
+ input_cl = layer.output_cl;
+ input = layer.output;
+ }
+ else if(net.types[i] == CONNECTED){
+ connected_layer layer = *(connected_layer *)net.layers[i];
+ forward_connected_layer(layer, input, train);
+ input = layer.output;
+ }
+ else if(net.types[i] == SOFTMAX){
+ softmax_layer layer = *(softmax_layer *)net.layers[i];
+ forward_softmax_layer(layer, input);
+ input = layer.output;
+ }
+ else if(net.types[i] == MAXPOOL){
+ maxpool_layer layer = *(maxpool_layer *)net.layers[i];
+ forward_maxpool_layer(layer, input);
+ input = layer.output;
+ }
+ else if(net.types[i] == NORMALIZATION){
+ normalization_layer layer = *(normalization_layer *)net.layers[i];
+ forward_normalization_layer(layer, input);
+ input = layer.output;
+ }
+ }
+}
+
+#else
+
+void forward_network(network net, float *input, int train)
{
int i;
for(i = 0; i < net.n; ++i){
@@ -42,16 +95,27 @@
forward_maxpool_layer(layer, input);
input = layer.output;
}
+ else if(net.types[i] == NORMALIZATION){
+ normalization_layer layer = *(normalization_layer *)net.layers[i];
+ forward_normalization_layer(layer, input);
+ input = layer.output;
+ }
+ else if(net.types[i] == DROPOUT){
+ if(!train) continue;
+ dropout_layer layer = *(dropout_layer *)net.layers[i];
+ forward_dropout_layer(layer, input);
+ }
}
}
+#endif
-void update_network(network net, double step)
+void update_network(network net)
{
int i;
for(i = 0; i < net.n; ++i){
if(net.types[i] == CONVOLUTIONAL){
convolutional_layer layer = *(convolutional_layer *)net.layers[i];
- update_convolutional_layer(layer, step, 0.9, .01);
+ update_convolutional_layer(layer);
}
else if(net.types[i] == MAXPOOL){
//maxpool_layer layer = *(maxpool_layer *)net.layers[i];
@@ -59,14 +123,17 @@
else if(net.types[i] == SOFTMAX){
//maxpool_layer layer = *(maxpool_layer *)net.layers[i];
}
+ else if(net.types[i] == NORMALIZATION){
+ //maxpool_layer layer = *(maxpool_layer *)net.layers[i];
+ }
else if(net.types[i] == CONNECTED){
connected_layer layer = *(connected_layer *)net.layers[i];
- update_connected_layer(layer, step, .9, 0);
+ update_connected_layer(layer);
}
}
}
-double *get_network_output_layer(network net, int i)
+float *get_network_output_layer(network net, int i)
{
if(net.types[i] == CONVOLUTIONAL){
convolutional_layer layer = *(convolutional_layer *)net.layers[i];
@@ -77,18 +144,23 @@
} else if(net.types[i] == SOFTMAX){
softmax_layer layer = *(softmax_layer *)net.layers[i];
return layer.output;
+ } else if(net.types[i] == DROPOUT){
+ return get_network_output_layer(net, i-1);
} else if(net.types[i] == CONNECTED){
connected_layer layer = *(connected_layer *)net.layers[i];
return layer.output;
+ } else if(net.types[i] == NORMALIZATION){
+ normalization_layer layer = *(normalization_layer *)net.layers[i];
+ return layer.output;
}
return 0;
}
-double *get_network_output(network net)
+float *get_network_output(network net)
{
return get_network_output_layer(net, net.n-1);
}
-double *get_network_delta_layer(network net, int i)
+float *get_network_delta_layer(network net, int i)
{
if(net.types[i] == CONVOLUTIONAL){
convolutional_layer layer = *(convolutional_layer *)net.layers[i];
@@ -99,6 +171,8 @@
} else if(net.types[i] == SOFTMAX){
softmax_layer layer = *(softmax_layer *)net.layers[i];
return layer.delta;
+ } else if(net.types[i] == DROPOUT){
+ return get_network_delta_layer(net, i-1);
} else if(net.types[i] == CONNECTED){
connected_layer layer = *(connected_layer *)net.layers[i];
return layer.delta;
@@ -106,16 +180,42 @@
return 0;
}
-double *get_network_delta(network net)
+float *get_network_delta(network net)
{
return get_network_delta_layer(net, net.n-1);
}
-void learn_network(network net, double *input)
+float calculate_error_network(network net, float *truth)
{
+ float sum = 0;
+ float *delta = get_network_delta(net);
+ float *out = get_network_output(net);
int i;
- double *prev_input;
- double *prev_delta;
+ for(i = 0; i < get_network_output_size(net)*net.batch; ++i){
+ //if(i %get_network_output_size(net) == 0) printf("\n");
+ //printf("%5.2f %5.2f, ", out[i], truth[i]);
+ //if(i == get_network_output_size(net)) printf("\n");
+ delta[i] = truth[i] - out[i];
+ //printf("%.10f, ", out[i]);
+ sum += delta[i]*delta[i];
+ }
+ //printf("\n");
+ return sum;
+}
+
+int get_predicted_class_network(network net)
+{
+ float *out = get_network_output(net);
+ int k = get_network_output_size(net);
+ return max_index(out, k);
+}
+
+float backward_network(network net, float *input, float *truth)
+{
+ float error = calculate_error_network(net, truth);
+ int i;
+ float *prev_input;
+ float *prev_delta;
for(i = net.n-1; i >= 0; --i){
if(i == 0){
prev_input = input;
@@ -126,59 +226,141 @@
}
if(net.types[i] == CONVOLUTIONAL){
convolutional_layer layer = *(convolutional_layer *)net.layers[i];
- learn_convolutional_layer(layer, prev_input);
- if(i != 0) backward_convolutional_layer(layer, prev_input, prev_delta);
+ backward_convolutional_layer(layer, prev_delta);
}
else if(net.types[i] == MAXPOOL){
maxpool_layer layer = *(maxpool_layer *)net.layers[i];
if(i != 0) backward_maxpool_layer(layer, prev_input, prev_delta);
}
+ else if(net.types[i] == NORMALIZATION){
+ normalization_layer layer = *(normalization_layer *)net.layers[i];
+ if(i != 0) backward_normalization_layer(layer, prev_input, prev_delta);
+ }
else if(net.types[i] == SOFTMAX){
softmax_layer layer = *(softmax_layer *)net.layers[i];
if(i != 0) backward_softmax_layer(layer, prev_input, prev_delta);
}
else if(net.types[i] == CONNECTED){
connected_layer layer = *(connected_layer *)net.layers[i];
- learn_connected_layer(layer, prev_input);
- if(i != 0) backward_connected_layer(layer, prev_input, prev_delta);
+ backward_connected_layer(layer, prev_input, prev_delta);
}
}
+ return error;
}
-void train_network_batch(network net, batch b)
+float train_network_datum(network net, float *x, float *y)
+{
+ forward_network(net, x, 1);
+ //int class = get_predicted_class_network(net);
+ float error = backward_network(net, x, y);
+ update_network(net);
+ //return (y[class]?1:0);
+ return error;
+}
+
+float train_network_sgd(network net, data d, int n)
+{
+ int batch = net.batch;
+ float *X = calloc(batch*d.X.cols, sizeof(float));
+ float *y = calloc(batch*d.y.cols, sizeof(float));
+
+ int i,j;
+ float sum = 0;
+ for(i = 0; i < n; ++i){
+ for(j = 0; j < batch; ++j){
+ int index = rand()%d.X.rows;
+ memcpy(X+j*d.X.cols, d.X.vals[index], d.X.cols*sizeof(float));
+ memcpy(y+j*d.y.cols, d.y.vals[index], d.y.cols*sizeof(float));
+ }
+ float err = train_network_datum(net, X, y);
+ sum += err;
+ //train_network_datum(net, X, y);
+ /*
+ float *y = d.y.vals[index];
+ int class = get_predicted_class_network(net);
+ correct += (y[class]?1:0);
+ */
+
+/*
+ for(j = 0; j < d.y.cols*batch; ++j){
+ printf("%6.3f ", y[j]);
+ }
+ printf("\n");
+ for(j = 0; j < d.y.cols*batch; ++j){
+ printf("%6.3f ", get_network_output(net)[j]);
+ }
+ printf("\n");
+ printf("\n");
+ */
+
+
+ //printf("%d %f %f\n", i,net.output[0], d.y.vals[index][0]);
+ //if((i+1)%10 == 0){
+ // printf("%d: %f\n", (i+1), (float)correct/(i+1));
+ //}
+ }
+ //printf("Accuracy: %f\n",(float) correct/n);
+ free(X);
+ free(y);
+ return (float)sum/(n*batch);
+}
+float train_network_batch(network net, data d, int n)
{
int i,j;
- int k = get_network_output_size(net);
- int correct = 0;
- for(i = 0; i < b.n; ++i){
- show_image(b.images[i], "Input");
- forward_network(net, b.images[i].data);
- image o = get_network_image(net);
- if(o.h) show_image_collapsed(o, "Output");
- double *output = get_network_output(net);
- double *delta = get_network_delta(net);
- int max_k = 0;
- double max = 0;
- for(j = 0; j < k; ++j){
- delta[j] = b.truth[i][j]-output[j];
- if(output[j] > max) {
- max = output[j];
- max_k = j;
- }
+ float sum = 0;
+ int batch = 2;
+ for(i = 0; i < n; ++i){
+ for(j = 0; j < batch; ++j){
+ int index = rand()%d.X.rows;
+ float *x = d.X.vals[index];
+ float *y = d.y.vals[index];
+ forward_network(net, x, 1);
+ sum += backward_network(net, x, y);
}
- if(b.truth[i][max_k]) ++correct;
- printf("%f\n", (double)correct/(i+1));
- learn_network(net, b.images[i].data);
- update_network(net, .001);
+ update_network(net);
+ }
+ return (float)sum/(n*batch);
+}
+
+
+void train_network(network net, data d)
+{
+ int i;
+ int correct = 0;
+ for(i = 0; i < d.X.rows; ++i){
+ correct += train_network_datum(net, d.X.vals[i], d.y.vals[i]);
if(i%100 == 0){
visualize_network(net);
- cvWaitKey(100);
+ cvWaitKey(10);
}
}
visualize_network(net);
- print_network(net);
cvWaitKey(100);
- printf("Accuracy: %f\n", (double)correct/b.n);
+ fprintf(stderr, "Accuracy: %f\n", (float)correct/d.X.rows);
+}
+
+int get_network_input_size_layer(network net, int i)
+{
+ if(net.types[i] == CONVOLUTIONAL){
+ convolutional_layer layer = *(convolutional_layer *)net.layers[i];
+ return layer.h*layer.w*layer.c;
+ }
+ else if(net.types[i] == MAXPOOL){
+ maxpool_layer layer = *(maxpool_layer *)net.layers[i];
+ return layer.h*layer.w*layer.c;
+ }
+ else if(net.types[i] == CONNECTED){
+ connected_layer layer = *(connected_layer *)net.layers[i];
+ return layer.inputs;
+ } else if(net.types[i] == DROPOUT){
+ dropout_layer layer = *(dropout_layer *) net.layers[i];
+ return layer.inputs;
+ }
+ else if(net.types[i] == SOFTMAX){
+ softmax_layer layer = *(softmax_layer *)net.layers[i];
+ return layer.inputs;
+ }
+ return 0;
}
int get_network_output_size_layer(network net, int i)
@@ -196,6 +378,9 @@
else if(net.types[i] == CONNECTED){
connected_layer layer = *(connected_layer *)net.layers[i];
return layer.outputs;
+ } else if(net.types[i] == DROPOUT){
+ dropout_layer layer = *(dropout_layer *) net.layers[i];
+ return layer.inputs;
}
else if(net.types[i] == SOFTMAX){
softmax_layer layer = *(softmax_layer *)net.layers[i];
@@ -204,12 +389,49 @@
return 0;
}
+int resize_network(network net, int h, int w, int c)
+{
+ int i;
+ for (i = 0; i < net.n; ++i){
+ if(net.types[i] == CONVOLUTIONAL){
+ convolutional_layer *layer = (convolutional_layer *)net.layers[i];
+ resize_convolutional_layer(layer, h, w, c);
+ image output = get_convolutional_image(*layer);
+ h = output.h;
+ w = output.w;
+ c = output.c;
+ }else if(net.types[i] == MAXPOOL){
+ maxpool_layer *layer = (maxpool_layer *)net.layers[i];
+ resize_maxpool_layer(layer, h, w, c);
+ image output = get_maxpool_image(*layer);
+ h = output.h;
+ w = output.w;
+ c = output.c;
+ }else if(net.types[i] == NORMALIZATION){
+ normalization_layer *layer = (normalization_layer *)net.layers[i];
+ resize_normalization_layer(layer, h, w, c);
+ image output = get_normalization_image(*layer);
+ h = output.h;
+ w = output.w;
+ c = output.c;
+ }else{
+ error("Cannot resize this type of layer");
+ }
+ }
+ return 0;
+}
+
int get_network_output_size(network net)
{
int i = net.n-1;
return get_network_output_size_layer(net, i);
}
+int get_network_input_size(network net)
+{
+ return get_network_input_size_layer(net, 0);
+}
+
image get_network_image_layer(network net, int i)
{
if(net.types[i] == CONVOLUTIONAL){
@@ -220,6 +442,10 @@
maxpool_layer layer = *(maxpool_layer *)net.layers[i];
return get_maxpool_image(layer);
}
+ else if(net.types[i] == NORMALIZATION){
+ normalization_layer layer = *(normalization_layer *)net.layers[i];
+ return get_normalization_image(layer);
+ }
return make_empty_image(0,0,0);
}
@@ -235,22 +461,57 @@
void visualize_network(network net)
{
+ image *prev = 0;
int i;
char buff[256];
for(i = 0; i < net.n; ++i){
sprintf(buff, "Layer %d", i);
if(net.types[i] == CONVOLUTIONAL){
convolutional_layer layer = *(convolutional_layer *)net.layers[i];
- visualize_convolutional_filters(layer, buff);
+ prev = visualize_convolutional_layer(layer, buff, prev);
+ }
+ if(net.types[i] == NORMALIZATION){
+ normalization_layer layer = *(normalization_layer *)net.layers[i];
+ visualize_normalization_layer(layer, buff);
}
}
}
+float *network_predict(network net, float *input)
+{
+ forward_network(net, input, 0);
+ float *out = get_network_output(net);
+ return out;
+}
+
+matrix network_predict_data(network net, data test)
+{
+ int i,j,b;
+ int k = get_network_output_size(net);
+ matrix pred = make_matrix(test.X.rows, k);
+ float *X = calloc(net.batch*test.X.rows, sizeof(float));
+ for(i = 0; i < test.X.rows; i += net.batch){
+ for(b = 0; b < net.batch; ++b){
+ if(i+b == test.X.rows) break;
+ memcpy(X+b*test.X.cols, test.X.vals[i+b], test.X.cols*sizeof(float));
+ }
+ float *out = network_predict(net, X);
+ for(b = 0; b < net.batch; ++b){
+ if(i+b == test.X.rows) break;
+ for(j = 0; j < k; ++j){
+ pred.vals[i+b][j] = out[j+b*k];
+ }
+ }
+ }
+ free(X);
+ return pred;
+}
+
void print_network(network net)
{
int i,j;
for(i = 0; i < net.n; ++i){
- double *output;
+ float *output = 0;
int n = 0;
if(net.types[i] == CONVOLUTIONAL){
convolutional_layer layer = *(convolutional_layer *)net.layers[i];
@@ -274,12 +535,22 @@
output = layer.output;
n = layer.inputs;
}
- double mean = mean_array(output, n);
- double vari = variance_array(output, n);
- printf("Layer %d - Mean: %f, Variance: %f\n",i,mean, vari);
+ float mean = mean_array(output, n);
+ float vari = variance_array(output, n);
+ fprintf(stderr, "Layer %d - Mean: %f, Variance: %f\n",i,mean, vari);
if(n > 100) n = 100;
- for(j = 0; j < n; ++j) printf("%f, ", output[j]);
- if(n == 100)printf(".....\n");
- printf("\n");
+ for(j = 0; j < n; ++j) fprintf(stderr, "%f, ", output[j]);
+ if(n == 100)fprintf(stderr,".....\n");
+ fprintf(stderr, "\n");
}
}
+
+float network_accuracy(network net, data d)
+{
+ matrix guess = network_predict_data(net, d);
+ float acc = matrix_accuracy(d.y, guess);
+ free_matrix(guess);
+ return acc;
+}
+
+
--
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