From 08b757a0bf76efe8c76b453063a1bb19315bcaa6 Mon Sep 17 00:00:00 2001
From: Joseph Redmon <pjreddie@gmail.com>
Date: Wed, 14 Jan 2015 20:18:57 +0000
Subject: [PATCH] Stable, needs to be way faster
---
src/connected_layer.c | 68 +++++++++++++++++++++++++--------
1 files changed, 51 insertions(+), 17 deletions(-)
diff --git a/src/connected_layer.c b/src/connected_layer.c
index 05d4a03..e29df77 100644
--- a/src/connected_layer.c
+++ b/src/connected_layer.c
@@ -24,22 +24,26 @@
layer->delta = calloc(batch*outputs, sizeof(float*));
layer->weight_updates = calloc(inputs*outputs, sizeof(float));
- //layer->weight_adapt = calloc(inputs*outputs, sizeof(float));
- layer->weights = calloc(inputs*outputs, sizeof(float));
- float scale = 1./inputs;
- scale = .01;
- for(i = 0; i < inputs*outputs; ++i)
- layer->weights[i] = scale*2*(rand_uniform()-.5);
-
layer->bias_updates = calloc(outputs, sizeof(float));
- //layer->bias_adapt = calloc(outputs, sizeof(float));
+
+ layer->weight_prev = calloc(inputs*outputs, sizeof(float));
+ layer->bias_prev = calloc(outputs, sizeof(float));
+
+ layer->weights = calloc(inputs*outputs, sizeof(float));
layer->biases = calloc(outputs, sizeof(float));
- for(i = 0; i < outputs; ++i){
- //layer->biases[i] = rand_normal()*scale + scale;
- layer->biases[i] = 1;
+
+
+ float scale = 1./sqrt(inputs);
+ //scale = .01;
+ for(i = 0; i < inputs*outputs; ++i){
+ layer->weights[i] = scale*rand_normal();
}
- #ifdef GPU
+ for(i = 0; i < outputs; ++i){
+ layer->biases[i] = scale;
+ }
+
+#ifdef GPU
layer->weights_cl = cl_make_array(layer->weights, inputs*outputs);
layer->biases_cl = cl_make_array(layer->biases, outputs);
@@ -48,18 +52,44 @@
layer->output_cl = cl_make_array(layer->output, outputs*batch);
layer->delta_cl = cl_make_array(layer->delta, outputs*batch);
- #endif
+#endif
layer->activation = activation;
fprintf(stderr, "Connected Layer: %d inputs, %d outputs\n", inputs, outputs);
return layer;
}
+void secret_update_connected_layer(connected_layer *layer)
+{
+ int n = layer->outputs*layer->inputs;
+ float dot = dot_cpu(n, layer->weight_updates, 1, layer->weight_prev, 1);
+ float mag = sqrt(dot_cpu(n, layer->weight_updates, 1, layer->weight_updates, 1))
+ * sqrt(dot_cpu(n, layer->weight_prev, 1, layer->weight_prev, 1));
+ float cos = dot/mag;
+ if(cos > .3) layer->learning_rate *= 1.1;
+ else if (cos < -.3) layer-> learning_rate /= 1.1;
+
+ scal_cpu(n, layer->momentum, layer->weight_prev, 1);
+ axpy_cpu(n, 1, layer->weight_updates, 1, layer->weight_prev, 1);
+ scal_cpu(n, 0, layer->weight_updates, 1);
+
+ scal_cpu(layer->outputs, layer->momentum, layer->bias_prev, 1);
+ axpy_cpu(layer->outputs, 1, layer->bias_updates, 1, layer->bias_prev, 1);
+ scal_cpu(layer->outputs, 0, layer->bias_updates, 1);
+
+ //printf("rate: %f\n", layer->learning_rate);
+
+ axpy_cpu(layer->outputs, layer->learning_rate, layer->bias_prev, 1, layer->biases, 1);
+
+ axpy_cpu(layer->inputs*layer->outputs, -layer->decay, layer->weights, 1, layer->weight_prev, 1);
+ axpy_cpu(layer->inputs*layer->outputs, layer->learning_rate, layer->weight_prev, 1, layer->weights, 1);
+}
+
void update_connected_layer(connected_layer layer)
{
axpy_cpu(layer.outputs, layer.learning_rate, layer.bias_updates, 1, layer.biases, 1);
scal_cpu(layer.outputs, layer.momentum, layer.bias_updates, 1);
- scal_cpu(layer.inputs*layer.outputs, 1.-layer.learning_rate*layer.decay, layer.weights, 1);
+ axpy_cpu(layer.inputs*layer.outputs, -layer.decay, layer.weights, 1, layer.weight_updates, 1);
axpy_cpu(layer.inputs*layer.outputs, layer.learning_rate, layer.weight_updates, 1, layer.weights, 1);
scal_cpu(layer.inputs*layer.outputs, layer.momentum, layer.weight_updates, 1);
}
@@ -112,12 +142,16 @@
{
cl_read_array(layer.weights_cl, layer.weights, layer.inputs*layer.outputs);
cl_read_array(layer.biases_cl, layer.biases, layer.outputs);
+ cl_read_array(layer.weight_updates_cl, layer.weight_updates, layer.inputs*layer.outputs);
+ cl_read_array(layer.bias_updates_cl, layer.bias_updates, layer.outputs);
}
void push_connected_layer(connected_layer layer)
{
cl_write_array(layer.weights_cl, layer.weights, layer.inputs*layer.outputs);
cl_write_array(layer.biases_cl, layer.biases, layer.outputs);
+ cl_write_array(layer.weight_updates_cl, layer.weight_updates, layer.inputs*layer.outputs);
+ cl_write_array(layer.bias_updates_cl, layer.bias_updates, layer.outputs);
}
void update_connected_layer_gpu(connected_layer layer)
@@ -125,10 +159,10 @@
axpy_ongpu(layer.outputs, layer.learning_rate, layer.bias_updates_cl, 1, layer.biases_cl, 1);
scal_ongpu(layer.outputs, layer.momentum, layer.bias_updates_cl, 1);
- scal_ongpu(layer.inputs*layer.outputs, 1.-layer.learning_rate*layer.decay, layer.weights_cl, 1);
+ axpy_ongpu(layer.inputs*layer.outputs, -layer.decay, layer.weights_cl, 1, layer.weight_updates_cl, 1);
axpy_ongpu(layer.inputs*layer.outputs, layer.learning_rate, layer.weight_updates_cl, 1, layer.weights_cl, 1);
scal_ongpu(layer.inputs*layer.outputs, layer.momentum, layer.weight_updates_cl, 1);
- pull_connected_layer(layer);
+ //pull_connected_layer(layer);
}
void forward_connected_layer_gpu(connected_layer layer, cl_mem input)
@@ -172,4 +206,4 @@
if(c) gemm_ongpu(0,1,m,n,k,1,a,k,b,k,0,c,n);
}
- #endif
+#endif
--
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