From cc06817efa24f20811ef6b32143c6700a91c5f2a Mon Sep 17 00:00:00 2001
From: Joseph Redmon <pjreddie@gmail.com>
Date: Fri, 11 Apr 2014 08:00:27 +0000
Subject: [PATCH] Attempt at visualizing ImageNet Features
---
src/network.c | 473 +++++++++++++++++++++++++++++++++++++++++++++++++++--------
1 files changed, 409 insertions(+), 64 deletions(-)
diff --git a/src/network.c b/src/network.c
index 53184d9..edae3c7 100644
--- a/src/network.c
+++ b/src/network.c
@@ -1,165 +1,510 @@
+#include <stdio.h>
#include "network.h"
#include "image.h"
+#include "data.h"
+#include "utils.h"
#include "connected_layer.h"
#include "convolutional_layer.h"
+//#include "old_conv.h"
#include "maxpool_layer.h"
+#include "softmax_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;
return net;
}
-void run_network(image input, network net)
+void print_convolutional_cfg(FILE *fp, convolutional_layer *l, int first)
{
int i;
- double *input_d = input.data;
+ fprintf(fp, "[convolutional]\n");
+ if(first) fprintf(fp, "batch=%d\n"
+ "height=%d\n"
+ "width=%d\n"
+ "channels=%d\n",
+ l->batch,l->h, l->w, l->c);
+ fprintf(fp, "filters=%d\n"
+ "size=%d\n"
+ "stride=%d\n"
+ "activation=%s\n",
+ l->n, l->size, l->stride,
+ get_activation_string(l->activation));
+ fprintf(fp, "data=");
+ for(i = 0; i < l->n; ++i) fprintf(fp, "%g,", l->biases[i]);
+ for(i = 0; i < l->n*l->c*l->size*l->size; ++i) fprintf(fp, "%g,", l->filters[i]);
+ fprintf(fp, "\n\n");
+}
+void print_connected_cfg(FILE *fp, connected_layer *l, int first)
+{
+ int i;
+ fprintf(fp, "[connected]\n");
+ if(first) fprintf(fp, "batch=%d\ninput=%d\n", l->batch, l->inputs);
+ fprintf(fp, "output=%d\n"
+ "activation=%s\n",
+ l->outputs,
+ get_activation_string(l->activation));
+ fprintf(fp, "data=");
+ for(i = 0; i < l->outputs; ++i) fprintf(fp, "%g,", l->biases[i]);
+ for(i = 0; i < l->inputs*l->outputs; ++i) fprintf(fp, "%g,", l->weights[i]);
+ fprintf(fp, "\n\n");
+}
+
+void print_maxpool_cfg(FILE *fp, maxpool_layer *l, int first)
+{
+ fprintf(fp, "[maxpool]\n");
+ if(first) fprintf(fp, "batch=%d\n"
+ "height=%d\n"
+ "width=%d\n"
+ "channels=%d\n",
+ l->batch,l->h, l->w, l->c);
+ fprintf(fp, "stride=%d\n\n", l->stride);
+}
+
+void print_softmax_cfg(FILE *fp, softmax_layer *l, int first)
+{
+ fprintf(fp, "[softmax]\n");
+ if(first) fprintf(fp, "batch=%d\ninput=%d\n", l->batch, l->inputs);
+ fprintf(fp, "\n");
+}
+
+void save_network(network net, char *filename)
+{
+ FILE *fp = fopen(filename, "w");
+ if(!fp) file_error(filename);
+ int i;
+ for(i = 0; i < net.n; ++i)
+ {
+ if(net.types[i] == CONVOLUTIONAL)
+ print_convolutional_cfg(fp, (convolutional_layer *)net.layers[i], i==0);
+ else if(net.types[i] == CONNECTED)
+ print_connected_cfg(fp, (connected_layer *)net.layers[i], i==0);
+ else if(net.types[i] == MAXPOOL)
+ print_maxpool_cfg(fp, (maxpool_layer *)net.layers[i], i==0);
+ else if(net.types[i] == SOFTMAX)
+ print_softmax_cfg(fp, (softmax_layer *)net.layers[i], i==0);
+ }
+ fclose(fp);
+}
+
+void forward_network(network net, float *input)
+{
+ int i;
for(i = 0; i < net.n; ++i){
if(net.types[i] == CONVOLUTIONAL){
convolutional_layer layer = *(convolutional_layer *)net.layers[i];
- run_convolutional_layer(input, layer);
+ forward_convolutional_layer(layer, input);
input = layer.output;
- input_d = layer.output.data;
}
else if(net.types[i] == CONNECTED){
connected_layer layer = *(connected_layer *)net.layers[i];
- run_connected_layer(input_d, layer);
- input_d = layer.output;
+ forward_connected_layer(layer, input);
+ 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];
- run_maxpool_layer(input, layer);
+ forward_maxpool_layer(layer, input);
input = layer.output;
- input_d = layer.output.data;
}
}
}
-void update_network(network net, double step)
+void update_network(network net, float step, float momentum, float decay)
{
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);
+ update_convolutional_layer(layer, step, momentum, decay);
}
else if(net.types[i] == MAXPOOL){
//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);
- }
- }
-}
-
-void learn_network(image input, network net)
-{
- int i;
- image prev;
- double *prev_p;
- for(i = net.n-1; i >= 0; --i){
- if(i == 0){
- prev = input;
- prev_p = prev.data;
- } else if(net.types[i-1] == CONVOLUTIONAL){
- convolutional_layer layer = *(convolutional_layer *)net.layers[i-1];
- prev = layer.output;
- prev_p = prev.data;
- } else if(net.types[i-1] == MAXPOOL){
- maxpool_layer layer = *(maxpool_layer *)net.layers[i-1];
- prev = layer.output;
- prev_p = prev.data;
- } else if(net.types[i-1] == CONNECTED){
- connected_layer layer = *(connected_layer *)net.layers[i-1];
- prev_p = layer.output;
- }
-
- if(net.types[i] == CONVOLUTIONAL){
- convolutional_layer layer = *(convolutional_layer *)net.layers[i];
- learn_convolutional_layer(prev, layer);
- }
- else if(net.types[i] == MAXPOOL){
+ else if(net.types[i] == SOFTMAX){
//maxpool_layer layer = *(maxpool_layer *)net.layers[i];
}
else if(net.types[i] == CONNECTED){
connected_layer layer = *(connected_layer *)net.layers[i];
- learn_connected_layer(prev_p, layer);
+ update_connected_layer(layer, step, momentum, decay);
}
}
}
-
-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];
- return layer.output.data;
- }
- else if(net.types[i] == MAXPOOL){
+ return layer.output;
+ } else if(net.types[i] == MAXPOOL){
maxpool_layer layer = *(maxpool_layer *)net.layers[i];
- return layer.output.data;
- }
- else if(net.types[i] == CONNECTED){
+ return layer.output;
+ } else if(net.types[i] == SOFTMAX){
+ softmax_layer layer = *(softmax_layer *)net.layers[i];
+ return layer.output;
+ } else if(net.types[i] == CONNECTED){
connected_layer layer = *(connected_layer *)net.layers[i];
return layer.output;
}
return 0;
}
+float *get_network_output(network net)
+{
+ return get_network_output_layer(net, net.n-1);
+}
+
+float *get_network_delta_layer(network net, int i)
+{
+ if(net.types[i] == CONVOLUTIONAL){
+ convolutional_layer layer = *(convolutional_layer *)net.layers[i];
+ return layer.delta;
+ } else if(net.types[i] == MAXPOOL){
+ maxpool_layer layer = *(maxpool_layer *)net.layers[i];
+ return layer.delta;
+ } else if(net.types[i] == SOFTMAX){
+ softmax_layer layer = *(softmax_layer *)net.layers[i];
+ return layer.delta;
+ } else if(net.types[i] == CONNECTED){
+ connected_layer layer = *(connected_layer *)net.layers[i];
+ return layer.delta;
+ }
+ return 0;
+}
+
+float *get_network_delta(network net)
+{
+ return get_network_delta_layer(net, net.n-1);
+}
+
+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, k = get_network_output_size(net);
+ for(i = 0; i < k; ++i){
+ //printf("%f, ", out[i]);
+ delta[i] = truth[i] - 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;
+ prev_delta = 0;
+ }else{
+ prev_input = get_network_output_layer(net, i-1);
+ prev_delta = get_network_delta_layer(net, i-1);
+ }
+ if(net.types[i] == CONVOLUTIONAL){
+ convolutional_layer layer = *(convolutional_layer *)net.layers[i];
+ learn_convolutional_layer(layer);
+ //learn_convolutional_layer(layer);
+ if(i != 0) 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] == 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);
+ }
+ }
+ return error;
+}
+
+float train_network_datum(network net, float *x, float *y, float step, float momentum, float decay)
+{
+ forward_network(net, x);
+ //int class = get_predicted_class_network(net);
+ float error = backward_network(net, x, y);
+ update_network(net, step, momentum, decay);
+ //return (y[class]?1:0);
+ return error;
+}
+
+float train_network_sgd(network net, data d, int n, float step, float momentum,float decay)
+{
+ int i;
+ float error = 0;
+ int correct = 0;
+ int pos = 0;
+ for(i = 0; i < n; ++i){
+ int index = rand()%d.X.rows;
+ float err = train_network_datum(net, d.X.vals[index], d.y.vals[index], step, momentum, decay);
+ float *y = d.y.vals[index];
+ int class = get_predicted_class_network(net);
+ correct += (y[class]?1:0);
+ if(y[1]){
+ error += err;
+ ++pos;
+ }
+
+
+ //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);
+ return error/pos;
+}
+float train_network_batch(network net, data d, int n, float step, float momentum,float decay)
+{
+ int i;
+ int correct = 0;
+ for(i = 0; i < n; ++i){
+ int index = rand()%d.X.rows;
+ float *x = d.X.vals[index];
+ float *y = d.y.vals[index];
+ forward_network(net, x);
+ int class = get_predicted_class_network(net);
+ backward_network(net, x, y);
+ correct += (y[class]?1:0);
+ }
+ update_network(net, step, momentum, decay);
+ return (float)correct/n;
+
+}
+
+
+void train_network(network net, data d, float step, float momentum, float decay)
+{
+ 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], step, momentum, decay);
+ if(i%100 == 0){
+ visualize_network(net);
+ cvWaitKey(10);
+ }
+ }
+ visualize_network(net);
+ cvWaitKey(100);
+ fprintf(stderr, "Accuracy: %f\n", (float)correct/d.X.rows);
+}
int get_network_output_size_layer(network net, int i)
{
if(net.types[i] == CONVOLUTIONAL){
convolutional_layer layer = *(convolutional_layer *)net.layers[i];
- return layer.output.h*layer.output.w*layer.output.c;
+ image output = get_convolutional_image(layer);
+ return output.h*output.w*output.c;
}
else if(net.types[i] == MAXPOOL){
maxpool_layer layer = *(maxpool_layer *)net.layers[i];
- return layer.output.h*layer.output.w*layer.output.c;
+ image output = get_maxpool_image(layer);
+ return output.h*output.w*output.c;
}
else if(net.types[i] == CONNECTED){
connected_layer layer = *(connected_layer *)net.layers[i];
return layer.outputs;
}
+ else if(net.types[i] == SOFTMAX){
+ softmax_layer layer = *(softmax_layer *)net.layers[i];
+ return layer.inputs;
+ }
return 0;
}
-double *get_network_output(network net)
+/*
+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];
+ layer->h = h;
+ layer->w = w;
+ layer->c = 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];
+ layer->h = h;
+ layer->w = w;
+ layer->c = c;
+ image output = get_maxpool_image(*layer);
+ h = output.h;
+ w = output.w;
+ c = output.c;
+ }
+ }
+ 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{
+ 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_layer(net, i);
+ return get_network_output_size_layer(net, i);
}
image get_network_image_layer(network net, int i)
{
if(net.types[i] == CONVOLUTIONAL){
convolutional_layer layer = *(convolutional_layer *)net.layers[i];
- return layer.output;
+ return get_convolutional_image(layer);
}
else if(net.types[i] == MAXPOOL){
maxpool_layer layer = *(maxpool_layer *)net.layers[i];
- return layer.output;
+ return get_maxpool_image(layer);
}
- return make_image(0,0,0);
+ return make_empty_image(0,0,0);
}
image get_network_image(network net)
{
int i;
for(i = net.n-1; i >= 0; --i){
+ image m = get_network_image_layer(net, i);
+ if(m.h != 0) return m;
+ }
+ return make_empty_image(0,0,0);
+}
+
+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];
- return layer.output;
+ prev = visualize_convolutional_layer(layer, buff, prev);
+ }
+ }
+}
+
+float *network_predict(network net, float *input)
+{
+ forward_network(net, input);
+ float *out = get_network_output(net);
+ return out;
+}
+
+matrix network_predict_data(network net, data test)
+{
+ int i,j;
+ int k = get_network_output_size(net);
+ matrix pred = make_matrix(test.X.rows, k);
+ for(i = 0; i < test.X.rows; ++i){
+ float *out = network_predict(net, test.X.vals[i]);
+ for(j = 0; j < k; ++j){
+ pred.vals[i][j] = out[j];
+ }
+ }
+ return pred;
+}
+
+void print_network(network net)
+{
+ int i,j;
+ for(i = 0; i < net.n; ++i){
+ float *output = 0;
+ int n = 0;
+ if(net.types[i] == CONVOLUTIONAL){
+ convolutional_layer layer = *(convolutional_layer *)net.layers[i];
+ output = layer.output;
+ image m = get_convolutional_image(layer);
+ n = m.h*m.w*m.c;
}
else if(net.types[i] == MAXPOOL){
maxpool_layer layer = *(maxpool_layer *)net.layers[i];
- return layer.output;
+ output = layer.output;
+ image m = get_maxpool_image(layer);
+ n = m.h*m.w*m.c;
}
+ else if(net.types[i] == CONNECTED){
+ connected_layer layer = *(connected_layer *)net.layers[i];
+ output = layer.output;
+ n = layer.outputs;
+ }
+ else if(net.types[i] == SOFTMAX){
+ softmax_layer layer = *(softmax_layer *)net.layers[i];
+ output = layer.output;
+ n = layer.inputs;
+ }
+ 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) fprintf(stderr, "%f, ", output[j]);
+ if(n == 100)fprintf(stderr,".....\n");
+ fprintf(stderr, "\n");
}
- return make_image(1,1,1);
}
+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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