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Fixing up maxpool layer

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This commit is contained in:
Joseph Redmon
2014-10-16 15:17:23 -07:00
parent 7756cccb79
commit 9b3c7136f3
8 changed files with 173 additions and 71 deletions
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72
src/connected_layer.c
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@ -38,9 +38,17 @@ connected_layer *make_connected_layer(int batch, int inputs, int outputs, ACTIVA
for(i = 0; i < outputs; ++i){
//layer->biases[i] = rand_normal()*scale + scale;
layer->biases[i] = 1;
}
}
#ifdef GPU
layer->weights_cl = cl_make_array(layer->weights, inputs*outputs);
layer->biases_cl = cl_make_array(layer->biases, outputs);
layer->weight_updates_cl = cl_make_array(layer->weight_updates, inputs*outputs);
layer->bias_updates_cl = cl_make_array(layer->bias_updates, outputs);
layer->output_cl = cl_make_array(layer->output, outputs*batch);
layer->delta_cl = cl_make_array(layer->delta, outputs*batch);
#endif
layer->activation = activation;
return layer;
@ -76,8 +84,8 @@ void backward_connected_layer(connected_layer layer, float *input, float *delta)
{
int i;
gradient_array(layer.output, layer.outputs*layer.batch, layer.activation, layer.delta);
for(i = 0; i < layer.outputs*layer.batch; ++i){
layer.bias_updates[i%layer.outputs] += layer.delta[i];
for(i = 0; i < layer.batch; ++i){
axpy_cpu(layer.outputs, 1, layer.delta + i*layer.outputs, 1, layer.bias_updates, 1);
}
int m = layer.inputs;
int k = layer.batch;
@ -98,3 +106,61 @@ void backward_connected_layer(connected_layer layer, float *input, float *delta)
if(c) gemm(0,1,m,n,k,1,a,k,b,k,0,c,n);
}
#ifdef GPU
void update_connected_layer_gpu(connected_layer layer)
{
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.learning_rate, layer.weight_updates_cl, 1, layer.weights_cl, 1);
scal_ongpu(layer.inputs*layer.outputs, layer.momentum, layer.weight_updates_cl, 1);
}
void forward_connected_layer_gpu(connected_layer layer, cl_mem input)
{
int i;
for(i = 0; i < layer.batch; ++i){
cl_mem sub = cl_sub_array(layer.output_cl, i*layer.outputs, layer.outputs);
copy_ongpu(layer.outputs, layer.biases_cl, 1, sub, 1);
clReleaseMemObject(sub);
}
int m = layer.batch;
int k = layer.inputs;
int n = layer.outputs;
cl_mem a = input;
cl_mem b = layer.weights_cl;
cl_mem c = layer.output_cl;
gemm_ongpu(0,0,m,n,k,1,a,k,b,n,1,c,n);
activate_array_ongpu(layer.output_cl, layer.outputs*layer.batch, layer.activation);
}
void backward_connected_layer_gpu(connected_layer layer, cl_mem input, cl_mem delta)
{
int i;
gradient_array_ongpu(layer.output_cl, layer.outputs*layer.batch, layer.activation, layer.delta_cl);
for(i = 0; i < layer.batch; ++i){
cl_mem sub = cl_sub_array(layer.delta_cl, i*layer.outputs, layer.outputs);
axpy_ongpu(layer.outputs, 1, sub, 1, layer.bias_updates_cl, 1);
clReleaseMemObject(sub);
}
int m = layer.inputs;
int k = layer.batch;
int n = layer.outputs;
cl_mem a = input;
cl_mem b = layer.delta_cl;
cl_mem c = layer.weight_updates_cl;
gemm_ongpu(1,0,m,n,k,1,a,m,b,n,1,c,n);
m = layer.batch;
k = layer.outputs;
n = layer.inputs;
a = layer.delta_cl;
b = layer.weights_cl;
c = delta;
if(c) gemm_ongpu(0,1,m,n,k,1,a,k,b,k,0,c,n);
}
#endif