From aa5996d58e68edfbefe51061856aecd549dd09c4 Mon Sep 17 00:00:00 2001
From: Joseph Redmon <pjreddie@gmail.com>
Date: Tue, 13 Jan 2015 01:27:08 +0000
Subject: [PATCH] Faster
---
src/convolutional_layer.c | 528 ++++++++++++++++++++++++++++++++++++----------------------
1 files changed, 328 insertions(+), 200 deletions(-)
diff --git a/src/convolutional_layer.c b/src/convolutional_layer.c
index 6a103f6..fc5cb0e 100644
--- a/src/convolutional_layer.c
+++ b/src/convolutional_layer.c
@@ -2,12 +2,29 @@
#include "utils.h"
#include "mini_blas.h"
#include <stdio.h>
+#include <time.h>
+
+int convolutional_out_height(convolutional_layer layer)
+{
+ int h = layer.h;
+ if (!layer.pad) h -= layer.size;
+ else h -= 1;
+ return h/layer.stride + 1;
+}
+
+int convolutional_out_width(convolutional_layer layer)
+{
+ int w = layer.w;
+ if (!layer.pad) w -= layer.size;
+ else w -= 1;
+ return w/layer.stride + 1;
+}
image get_convolutional_image(convolutional_layer layer)
{
int h,w,c;
- h = layer.out_h;
- w = layer.out_w;
+ h = convolutional_out_height(layer);
+ w = convolutional_out_width(layer);
c = layer.n;
return float_to_image(h,w,c,layer.output);
}
@@ -15,230 +32,186 @@
image get_convolutional_delta(convolutional_layer layer)
{
int h,w,c;
- h = layer.out_h;
- w = layer.out_w;
+ h = convolutional_out_height(layer);
+ w = convolutional_out_width(layer);
c = layer.n;
return float_to_image(h,w,c,layer.delta);
}
-convolutional_layer *make_convolutional_layer(int h, int w, int c, int n, int size, int stride, ACTIVATION activation)
+convolutional_layer *make_convolutional_layer(int batch, int h, int w, int c, int n, int size, int stride, int pad, ACTIVATION activation, float learning_rate, float momentum, float decay)
{
int i;
- int out_h,out_w;
size = 2*(size/2)+1; //HA! And you thought you'd use an even sized filter...
convolutional_layer *layer = calloc(1, sizeof(convolutional_layer));
+
+ layer->learning_rate = learning_rate;
+ layer->momentum = momentum;
+ layer->decay = decay;
+
layer->h = h;
layer->w = w;
layer->c = c;
layer->n = n;
+ layer->batch = batch;
layer->stride = stride;
layer->size = size;
+ layer->pad = pad;
layer->filters = calloc(c*n*size*size, sizeof(float));
layer->filter_updates = calloc(c*n*size*size, sizeof(float));
- layer->filter_momentum = calloc(c*n*size*size, sizeof(float));
layer->biases = calloc(n, sizeof(float));
layer->bias_updates = calloc(n, sizeof(float));
- layer->bias_momentum = calloc(n, sizeof(float));
- float scale = 1./(size*size*c);
- for(i = 0; i < c*n*size*size; ++i) layer->filters[i] = scale*(rand_uniform());
+ float scale = 1./sqrt(size*size*c);
+ //scale = .05;
+ for(i = 0; i < c*n*size*size; ++i) layer->filters[i] = scale*rand_normal();
for(i = 0; i < n; ++i){
//layer->biases[i] = rand_normal()*scale + scale;
- layer->biases[i] = 0;
+ layer->biases[i] = scale;
}
- out_h = (h-size)/stride + 1;
- out_w = (w-size)/stride + 1;
+ int out_h = convolutional_out_height(*layer);
+ int out_w = convolutional_out_width(*layer);
layer->col_image = calloc(out_h*out_w*size*size*c, sizeof(float));
- layer->output = calloc(out_h * out_w * n, sizeof(float));
- layer->delta = calloc(out_h * out_w * n, sizeof(float));
+ layer->output = calloc(layer->batch*out_h * out_w * n, sizeof(float));
+ layer->delta = calloc(layer->batch*out_h * out_w * n, sizeof(float));
+
+ #ifdef GPU
+ layer->filters_cl = cl_make_array(layer->filters, c*n*size*size);
+ layer->filter_updates_cl = cl_make_array(layer->filter_updates, c*n*size*size);
+
+ layer->biases_cl = cl_make_array(layer->biases, n);
+ layer->bias_updates_cl = cl_make_array(layer->bias_updates, n);
+
+ layer->col_image_cl = cl_make_array(layer->col_image, out_h*out_w*size*size*c);
+ layer->delta_cl = cl_make_array(layer->delta, layer->batch*out_h*out_w*n);
+ layer->output_cl = cl_make_array(layer->output, layer->batch*out_h*out_w*n);
+ #endif
layer->activation = activation;
- layer->out_h = out_h;
- layer->out_w = out_w;
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);
- srand(0);
return layer;
}
+void resize_convolutional_layer(convolutional_layer *layer, int h, int w, int c)
+{
+ layer->h = h;
+ layer->w = w;
+ layer->c = c;
+ int out_h = convolutional_out_height(*layer);
+ int out_w = convolutional_out_width(*layer);
+
+ layer->col_image = realloc(layer->col_image,
+ out_h*out_w*layer->size*layer->size*layer->c*sizeof(float));
+ layer->output = realloc(layer->output,
+ layer->batch*out_h * out_w * layer->n*sizeof(float));
+ layer->delta = realloc(layer->delta,
+ layer->batch*out_h * out_w * layer->n*sizeof(float));
+}
+
+void bias_output(const convolutional_layer layer)
+{
+ int i,j,b;
+ int out_h = convolutional_out_height(layer);
+ int out_w = convolutional_out_width(layer);
+ for(b = 0; b < layer.batch; ++b){
+ for(i = 0; i < layer.n; ++i){
+ for(j = 0; j < out_h*out_w; ++j){
+ layer.output[(b*layer.n + i)*out_h*out_w + j] = layer.biases[i];
+ }
+ }
+ }
+}
+
void forward_convolutional_layer(const convolutional_layer layer, float *in)
{
+ int out_h = convolutional_out_height(layer);
+ int out_w = convolutional_out_width(layer);
int i;
+
+ bias_output(layer);
+
int m = layer.n;
int k = layer.size*layer.size*layer.c;
- int n = ((layer.h-layer.size)/layer.stride + 1)*
- ((layer.w-layer.size)/layer.stride + 1);
-
- memset(layer.output, 0, m*n*sizeof(float));
+ int n = out_h*out_w;
float *a = layer.filters;
float *b = layer.col_image;
float *c = layer.output;
- im2col_cpu(in, layer.c, layer.h, layer.w, layer.size, layer.stride, b);
- gemm(0,0,m,n,k,1,a,k,b,n,1,c,n);
- for(i = 0; i < m*n; ++i){
- layer.output[i] = activate(layer.output[i], layer.activation);
+ for(i = 0; i < layer.batch; ++i){
+ im2col_cpu(in, layer.c, layer.h, layer.w,
+ layer.size, layer.stride, layer.pad, b);
+ gemm(0,0,m,n,k,1,a,k,b,n,1,c,n);
+ c += n*m;
+ in += layer.c*layer.h*layer.w;
}
- //for(i = 0; i < m*n; ++i) if(i%(m*n/10+1)==0) printf("%f, ", layer.output[i]); printf("\n");
-
-}
-
-void gradient_delta_convolutional_layer(convolutional_layer layer)
-{
- int i;
- for(i = 0; i < layer.out_h*layer.out_w*layer.n; ++i){
- layer.delta[i] *= gradient(layer.output[i], layer.activation);
- }
+ activate_array(layer.output, m*n*layer.batch, layer.activation);
}
void learn_bias_convolutional_layer(convolutional_layer layer)
{
- int i,j;
- int size = layer.out_h*layer.out_w;
- for(i = 0; i < layer.n; ++i){
- float sum = 0;
- for(j = 0; j < size; ++j){
- sum += layer.delta[j+i*size];
+ int i,b;
+ int size = convolutional_out_height(layer)
+ *convolutional_out_width(layer);
+ for(b = 0; b < layer.batch; ++b){
+ for(i = 0; i < layer.n; ++i){
+ layer.bias_updates[i] += sum_array(layer.delta+size*(i+b*layer.n), size);
}
- layer.bias_updates[i] += sum/size;
}
}
-void learn_convolutional_layer(convolutional_layer layer)
+void backward_convolutional_layer(convolutional_layer layer, float *in, float *delta)
{
- gradient_delta_convolutional_layer(layer);
- learn_bias_convolutional_layer(layer);
+ int i;
int m = layer.n;
int n = layer.size*layer.size*layer.c;
- int k = ((layer.h-layer.size)/layer.stride + 1)*
- ((layer.w-layer.size)/layer.stride + 1);
+ int k = convolutional_out_height(layer)*
+ convolutional_out_width(layer);
- float *a = layer.delta;
- float *b = layer.col_image;
- float *c = layer.filter_updates;
+ gradient_array(layer.output, m*k*layer.batch, layer.activation, layer.delta);
- gemm(0,1,m,n,k,1,a,k,b,k,1,c,n);
+ learn_bias_convolutional_layer(layer);
+
+ if(delta) memset(delta, 0, layer.batch*layer.h*layer.w*layer.c*sizeof(float));
+
+ for(i = 0; i < layer.batch; ++i){
+ float *a = layer.delta + i*m*k;
+ float *b = layer.col_image;
+ float *c = layer.filter_updates;
+
+ float *im = in+i*layer.c*layer.h*layer.w;
+
+ im2col_cpu(im, layer.c, layer.h, layer.w,
+ layer.size, layer.stride, layer.pad, b);
+ gemm(0,1,m,n,k,1,a,k,b,k,1,c,n);
+
+ if(delta){
+ a = layer.filters;
+ b = layer.delta + i*m*k;
+ c = layer.col_image;
+
+ gemm(1,0,n,k,m,1,a,n,b,k,0,c,k);
+
+ col2im_cpu(layer.col_image, layer.c, layer.h, layer.w, layer.size, layer.stride, layer.pad, delta+i*layer.c*layer.h*layer.w);
+ }
+ }
}
-void backward_convolutional_layer(convolutional_layer layer, float *delta)
+void update_convolutional_layer(convolutional_layer layer)
{
- int m = layer.size*layer.size*layer.c;
- int k = layer.n;
- int n = ((layer.h-layer.size)/layer.stride + 1)*
- ((layer.w-layer.size)/layer.stride + 1);
-
- float *a = layer.filters;
- float *b = layer.delta;
- float *c = layer.col_image;
-
-
- memset(c, 0, m*n*sizeof(float));
- gemm(1,0,m,n,k,1,a,m,b,n,1,c,n);
-
- memset(delta, 0, layer.h*layer.w*layer.c*sizeof(float));
- col2im_cpu(c, layer.c, layer.h, layer.w, layer.size, layer.stride, delta);
-}
-
-void update_convolutional_layer(convolutional_layer layer, float step, float momentum, float decay)
-{
- int i;
int size = layer.size*layer.size*layer.c*layer.n;
- for(i = 0; i < layer.n; ++i){
- layer.biases[i] += step*layer.bias_updates[i];
- layer.bias_updates[i] *= momentum;
- }
- for(i = 0; i < size; ++i){
- layer.filters[i] += step*(layer.filter_updates[i] - decay*layer.filters[i]);
- layer.filter_updates[i] *= momentum;
- }
-}
-/*
+ axpy_cpu(layer.n, layer.learning_rate, layer.bias_updates, 1, layer.biases, 1);
+ scal_cpu(layer.n, layer.momentum, layer.bias_updates, 1);
-void backward_convolutional_layer2(convolutional_layer layer, float *input, float *delta)
-{
- image in_delta = float_to_image(layer.h, layer.w, layer.c, delta);
- image out_delta = get_convolutional_delta(layer);
- int i,j;
- for(i = 0; i < layer.n; ++i){
- rotate_image(layer.kernels[i]);
- }
-
- zero_image(in_delta);
- upsample_image(out_delta, layer.stride, layer.upsampled);
- for(j = 0; j < in_delta.c; ++j){
- for(i = 0; i < layer.n; ++i){
- two_d_convolve(layer.upsampled, i, layer.kernels[i], j, 1, in_delta, j, layer.edge);
- }
- }
-
- for(i = 0; i < layer.n; ++i){
- rotate_image(layer.kernels[i]);
- }
+ axpy_cpu(size, -layer.decay, layer.filters, 1, layer.filter_updates, 1);
+ axpy_cpu(size, layer.learning_rate, layer.filter_updates, 1, layer.filters, 1);
+ scal_cpu(size, layer.momentum, layer.filter_updates, 1);
}
-void learn_convolutional_layer(convolutional_layer layer, float *input)
-{
- int i;
- image in_image = float_to_image(layer.h, layer.w, layer.c, input);
- image out_delta = get_convolutional_delta(layer);
- gradient_delta_convolutional_layer(layer);
- for(i = 0; i < layer.n; ++i){
- kernel_update(in_image, layer.kernel_updates[i], layer.stride, i, out_delta, layer.edge);
- layer.bias_updates[i] += avg_image_layer(out_delta, i);
- }
-}
-
-void update_convolutional_layer(convolutional_layer layer, float step, float momentum, float decay)
-{
- int i,j;
- for(i = 0; i < layer.n; ++i){
- layer.bias_momentum[i] = step*(layer.bias_updates[i])
- + momentum*layer.bias_momentum[i];
- layer.biases[i] += layer.bias_momentum[i];
- layer.bias_updates[i] = 0;
- int pixels = layer.kernels[i].h*layer.kernels[i].w*layer.kernels[i].c;
- for(j = 0; j < pixels; ++j){
- layer.kernel_momentum[i].data[j] = step*(layer.kernel_updates[i].data[j] - decay*layer.kernels[i].data[j])
- + momentum*layer.kernel_momentum[i].data[j];
- layer.kernels[i].data[j] += layer.kernel_momentum[i].data[j];
- }
- zero_image(layer.kernel_updates[i]);
- }
-}
-*/
-
-void test_convolutional_layer()
-{
- convolutional_layer l = *make_convolutional_layer(4,4,1,1,3,1,LINEAR);
- float input[] = {1,2,3,4,
- 5,6,7,8,
- 9,10,11,12,
- 13,14,15,16};
- float filter[] = {.5, 0, .3,
- 0 , 1, 0,
- .2 , 0, 1};
- float delta[] = {1, 2,
- 3, 4};
- float in_delta[] = {.5,1,.3,.6,
- 5,6,7,8,
- 9,10,11,12,
- 13,14,15,16};
- l.filters = filter;
- forward_convolutional_layer(l, input);
- l.delta = delta;
- learn_convolutional_layer(l);
- image filter_updates = float_to_image(3,3,1,l.filter_updates);
- print_image(filter_updates);
- printf("Delta:\n");
- backward_convolutional_layer(l, in_delta);
- pm(4,4,in_delta);
-}
-
image get_convolutional_filter(convolutional_layer layer, int i)
{
int h = layer.size;
@@ -247,52 +220,207 @@
return float_to_image(h,w,c,layer.filters+i*h*w*c);
}
-void visualize_convolutional_layer(convolutional_layer layer, char *window)
+image *weighted_sum_filters(convolutional_layer layer, image *prev_filters)
{
- int color = 1;
- int border = 1;
- int h,w,c;
- int size = layer.size;
- h = size;
- w = (size + border) * layer.n - border;
- c = layer.c;
- if(c != 3 || !color){
- h = (h+border)*c - border;
- c = 1;
+ image *filters = calloc(layer.n, sizeof(image));
+ int i,j,k,c;
+ if(!prev_filters){
+ for(i = 0; i < layer.n; ++i){
+ filters[i] = copy_image(get_convolutional_filter(layer, i));
+ }
}
-
- image filters = make_image(h,w,c);
- int i,j;
- for(i = 0; i < layer.n; ++i){
- int w_offset = i*(size+border);
- image k = get_convolutional_filter(layer, i);
- //printf("%f ** ", layer.biases[i]);
- //print_image(k);
- image copy = copy_image(k);
- normalize_image(copy);
- for(j = 0; j < k.c; ++j){
- //set_pixel(copy,0,0,j,layer.biases[i]);
- }
- if(c == 3 && color){
- embed_image(copy, filters, 0, w_offset);
- }
- else{
- for(j = 0; j < k.c; ++j){
- int h_offset = j*(size+border);
- image layer = get_image_layer(k, j);
- embed_image(layer, filters, h_offset, w_offset);
- free_image(layer);
+ else{
+ image base = prev_filters[0];
+ for(i = 0; i < layer.n; ++i){
+ image filter = get_convolutional_filter(layer, i);
+ filters[i] = make_image(base.h, base.w, base.c);
+ for(j = 0; j < layer.size; ++j){
+ for(k = 0; k < layer.size; ++k){
+ for(c = 0; c < layer.c; ++c){
+ float weight = get_pixel(filter, j, k, c);
+ image prev_filter = copy_image(prev_filters[c]);
+ scale_image(prev_filter, weight);
+ add_into_image(prev_filter, filters[i], 0,0);
+ free_image(prev_filter);
+ }
+ }
}
}
- free_image(copy);
}
- image delta = get_convolutional_delta(layer);
+ return filters;
+}
+
+image *visualize_convolutional_layer(convolutional_layer layer, char *window, image *prev_filters)
+{
+ image *single_filters = weighted_sum_filters(layer, 0);
+ show_images(single_filters, layer.n, window);
+
+ image delta = get_convolutional_image(layer);
image dc = collapse_image_layers(delta, 1);
char buff[256];
- sprintf(buff, "%s: Delta", window);
- show_image(dc, buff);
+ sprintf(buff, "%s: Output", window);
+ //show_image(dc, buff);
+ //save_image(dc, buff);
free_image(dc);
- show_image(filters, window);
- free_image(filters);
+ return single_filters;
}
+#ifdef GPU
+#define BLOCK 32
+
+#define STR_HELPER(x) #x
+#define STR(x) STR_HELPER(x)
+
+
+cl_kernel get_convolutional_learn_bias_kernel()
+{
+ static int init = 0;
+ static cl_kernel kernel;
+ if(!init){
+ kernel = get_kernel("src/convolutional_layer.cl", "learn_bias", "-D BLOCK=" STR(BLOCK));
+ init = 1;
+ }
+ return kernel;
+}
+
+void learn_bias_convolutional_layer_ongpu(convolutional_layer layer)
+{
+ int size = convolutional_out_height(layer) * convolutional_out_width(layer);
+
+ cl_kernel kernel = get_convolutional_learn_bias_kernel();
+ cl_command_queue queue = cl.queue;
+
+ cl_uint i = 0;
+ cl.error = clSetKernelArg(kernel, i++, sizeof(layer.batch), (void*) &layer.batch);
+ cl.error = clSetKernelArg(kernel, i++, sizeof(layer.n), (void*) &layer.n);
+ cl.error = clSetKernelArg(kernel, i++, sizeof(size), (void*) &size);
+ cl.error = clSetKernelArg(kernel, i++, sizeof(layer.delta_cl), (void*) &layer.delta_cl);
+ cl.error = clSetKernelArg(kernel, i++, sizeof(layer.bias_updates_cl), (void*) &layer.bias_updates_cl);
+ check_error(cl);
+
+ const size_t global_size[] = {layer.n*BLOCK};
+ const size_t local_size[] = {BLOCK};
+
+ cl.error = clEnqueueNDRangeKernel(queue, kernel, 1, 0, global_size, local_size, 0, 0, 0);
+ check_error(cl);
+}
+
+cl_kernel get_convolutional_bias_kernel()
+{
+ static int init = 0;
+ static cl_kernel kernel;
+ if(!init){
+ kernel = get_kernel("src/convolutional_layer.cl", "bias", "-D BLOCK=" STR(BLOCK));
+ init = 1;
+ }
+ return kernel;
+}
+
+void bias_output_gpu(const convolutional_layer layer)
+{
+ int out_h = convolutional_out_height(layer);
+ int out_w = convolutional_out_width(layer);
+ int size = out_h*out_w;
+
+ cl_kernel kernel = get_convolutional_bias_kernel();
+ cl_command_queue queue = cl.queue;
+
+ cl_uint i = 0;
+ cl.error = clSetKernelArg(kernel, i++, sizeof(layer.n), (void*) &layer.n);
+ cl.error = clSetKernelArg(kernel, i++, sizeof(size), (void*) &size);
+ cl.error = clSetKernelArg(kernel, i++, sizeof(layer.biases_cl), (void*) &layer.biases_cl);
+ cl.error = clSetKernelArg(kernel, i++, sizeof(layer.output_cl), (void*) &layer.output_cl);
+ check_error(cl);
+
+ const size_t global_size[] = {layer.n*size, layer.batch};
+
+ cl.error = clEnqueueNDRangeKernel(queue, kernel, 2, 0, global_size, 0, 0, 0, 0);
+ check_error(cl);
+}
+
+//#define TIMEIT
+
+void forward_convolutional_layer_gpu(convolutional_layer layer, cl_mem in)
+{
+ int i;
+ int m = layer.n;
+ int k = layer.size*layer.size*layer.c;
+ int n = convolutional_out_height(layer)*
+ convolutional_out_width(layer);
+
+ bias_output_gpu(layer);
+
+ for(i = 0; i < layer.batch; ++i){
+ im2col_ongpu(in, i*layer.c*layer.h*layer.w, layer.c, layer.h, layer.w, layer.size, layer.stride, layer.pad, layer.col_image_cl);
+ cl_mem a = layer.filters_cl;
+ cl_mem b = layer.col_image_cl;
+ cl_mem c = layer.output_cl;
+ gemm_ongpu_offset(0,0,m,n,k,1.,a,0,k,b,0,n,1.,c,i*m*n,n);
+ }
+ activate_array_ongpu(layer.output_cl, m*n*layer.batch, layer.activation);
+}
+
+void backward_convolutional_layer_gpu(convolutional_layer layer, cl_mem in, cl_mem delta_cl)
+{
+ int i;
+ int m = layer.n;
+ int n = layer.size*layer.size*layer.c;
+ int k = convolutional_out_height(layer)*
+ convolutional_out_width(layer);
+ gradient_array_ongpu(layer.output_cl, m*k*layer.batch, layer.activation, layer.delta_cl);
+ learn_bias_convolutional_layer_ongpu(layer);
+
+ if(delta_cl) scal_ongpu(layer.batch*layer.h*layer.w*layer.c, 0, delta_cl, 1);
+
+ for(i = 0; i < layer.batch; ++i){
+ cl_mem a = layer.delta_cl;
+ cl_mem b = layer.col_image_cl;
+ cl_mem c = layer.filter_updates_cl;
+
+ im2col_ongpu(in, i*layer.c*layer.h*layer.w, layer.c, layer.h, layer.w, layer.size, layer.stride, layer.pad, layer.col_image_cl);
+ gemm_ongpu_offset(0,1,m,n,k,1,a,i*m*k,k,b,0,k,1,c,0,n);
+
+ if(delta_cl){
+
+ cl_mem a = layer.filters_cl;
+ cl_mem b = layer.delta_cl;
+ cl_mem c = layer.col_image_cl;
+
+ gemm_ongpu_offset(1,0,n,k,m,1,a,0,n,b,i*k*m,k,0,c,0,k);
+
+ col2im_ongpu(layer.col_image_cl, i*layer.c*layer.h*layer.w, layer.c, layer.h, layer.w, layer.size, layer.stride, layer.pad, delta_cl);
+ }
+ }
+}
+
+void pull_convolutional_layer(convolutional_layer layer)
+{
+ cl_read_array(layer.filters_cl, layer.filters, layer.c*layer.n*layer.size*layer.size);
+ cl_read_array(layer.biases_cl, layer.biases, layer.n);
+ cl_read_array(layer.filter_updates_cl, layer.filter_updates, layer.c*layer.n*layer.size*layer.size);
+ cl_read_array(layer.bias_updates_cl, layer.bias_updates, layer.n);
+}
+
+void push_convolutional_layer(convolutional_layer layer)
+{
+ cl_write_array(layer.filters_cl, layer.filters, layer.c*layer.n*layer.size*layer.size);
+ cl_write_array(layer.biases_cl, layer.biases, layer.n);
+ cl_write_array(layer.filter_updates_cl, layer.filter_updates, layer.c*layer.n*layer.size*layer.size);
+ cl_write_array(layer.bias_updates_cl, layer.bias_updates, layer.n);
+}
+
+void update_convolutional_layer_gpu(convolutional_layer layer)
+{
+ int size = layer.size*layer.size*layer.c*layer.n;
+ axpy_ongpu(layer.n, layer.learning_rate, layer.bias_updates_cl, 1, layer.biases_cl, 1);
+ scal_ongpu(layer.n,layer.momentum, layer.bias_updates_cl, 1);
+
+ axpy_ongpu(size, -layer.decay, layer.filters_cl, 1, layer.filter_updates_cl, 1);
+ axpy_ongpu(size, layer.learning_rate, layer.filter_updates_cl, 1, layer.filters_cl, 1);
+ scal_ongpu(size, layer.momentum, layer.filter_updates_cl, 1);
+ //pull_convolutional_layer(layer);
+}
+
+
+#endif
+
--
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