From 372980d690f84aade1ebfd1a92750ed327ab1c8d Mon Sep 17 00:00:00 2001
From: Joseph Redmon <pjreddie@gmail.com>
Date: Mon, 04 May 2015 18:29:21 +0000
Subject: [PATCH] i don't know what's going on anymore
---
src/deconvolutional_layer.c | 36 ++++++++++++++++--------------------
1 files changed, 16 insertions(+), 20 deletions(-)
diff --git a/src/deconvolutional_layer.c b/src/deconvolutional_layer.c
index d4a8426..532045c 100644
--- a/src/deconvolutional_layer.c
+++ b/src/deconvolutional_layer.c
@@ -31,7 +31,7 @@
h = deconvolutional_out_height(layer);
w = deconvolutional_out_width(layer);
c = layer.n;
- return float_to_image(h,w,c,layer.output);
+ return float_to_image(w,h,c,layer.output);
}
image get_deconvolutional_delta(deconvolutional_layer layer)
@@ -40,18 +40,14 @@
h = deconvolutional_out_height(layer);
w = deconvolutional_out_width(layer);
c = layer.n;
- return float_to_image(h,w,c,layer.delta);
+ return float_to_image(w,h,c,layer.delta);
}
-deconvolutional_layer *make_deconvolutional_layer(int batch, int h, int w, int c, int n, int size, int stride, ACTIVATION activation, float learning_rate, float momentum, float decay)
+deconvolutional_layer *make_deconvolutional_layer(int batch, int h, int w, int c, int n, int size, int stride, ACTIVATION activation)
{
int i;
deconvolutional_layer *layer = calloc(1, sizeof(deconvolutional_layer));
- layer->learning_rate = learning_rate;
- layer->momentum = momentum;
- layer->decay = decay;
-
layer->h = h;
layer->w = w;
layer->c = c;
@@ -120,7 +116,7 @@
#endif
}
-void forward_deconvolutional_layer(const deconvolutional_layer layer, float *in)
+void forward_deconvolutional_layer(const deconvolutional_layer layer, network_state state)
{
int i;
int out_h = deconvolutional_out_height(layer);
@@ -135,7 +131,7 @@
for(i = 0; i < layer.batch; ++i){
float *a = layer.filters;
- float *b = in + i*layer.c*layer.h*layer.w;
+ float *b = state.input + i*layer.c*layer.h*layer.w;
float *c = layer.col_image;
gemm(1,0,m,n,k,1,a,m,b,n,0,c,n);
@@ -145,7 +141,7 @@
activate_array(layer.output, layer.batch*layer.n*size, layer.activation);
}
-void backward_deconvolutional_layer(deconvolutional_layer layer, float *in, float *delta)
+void backward_deconvolutional_layer(deconvolutional_layer layer, network_state state)
{
float alpha = 1./layer.batch;
int out_h = deconvolutional_out_height(layer);
@@ -156,14 +152,14 @@
gradient_array(layer.output, size*layer.n*layer.batch, layer.activation, layer.delta);
backward_bias(layer.bias_updates, layer.delta, layer.batch, layer.n, size);
- if(delta) memset(delta, 0, layer.batch*layer.h*layer.w*layer.c*sizeof(float));
+ if(state.delta) memset(state.delta, 0, layer.batch*layer.h*layer.w*layer.c*sizeof(float));
for(i = 0; i < layer.batch; ++i){
int m = layer.c;
int n = layer.size*layer.size*layer.n;
int k = layer.h*layer.w;
- float *a = in + i*m*n;
+ float *a = state.input + i*m*n;
float *b = layer.col_image;
float *c = layer.filter_updates;
@@ -171,29 +167,29 @@
layer.size, layer.stride, 0, b);
gemm(0,1,m,n,k,alpha,a,k,b,k,1,c,n);
- if(delta){
+ if(state.delta){
int m = layer.c;
int n = layer.h*layer.w;
int k = layer.size*layer.size*layer.n;
float *a = layer.filters;
float *b = layer.col_image;
- float *c = delta + i*n*m;
+ float *c = state.delta + i*n*m;
gemm(0,0,m,n,k,1,a,k,b,n,1,c,n);
}
}
}
-void update_deconvolutional_layer(deconvolutional_layer layer)
+void update_deconvolutional_layer(deconvolutional_layer layer, float learning_rate, float momentum, float decay)
{
int size = layer.size*layer.size*layer.c*layer.n;
- axpy_cpu(layer.n, layer.learning_rate, layer.bias_updates, 1, layer.biases, 1);
- scal_cpu(layer.n, layer.momentum, layer.bias_updates, 1);
+ axpy_cpu(layer.n, learning_rate, layer.bias_updates, 1, layer.biases, 1);
+ scal_cpu(layer.n, momentum, layer.bias_updates, 1);
- 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);
+ axpy_cpu(size, -decay, layer.filters, 1, layer.filter_updates, 1);
+ axpy_cpu(size, learning_rate, layer.filter_updates, 1, layer.filters, 1);
+ scal_cpu(size, momentum, layer.filter_updates, 1);
}
--
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