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shortcut layers, msr networks

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This commit is contained in:
Joseph Redmon
2015-12-14 11:57:10 -08:00
parent 892923514f
commit db0397cfaa
35 changed files with 2635 additions and 56 deletions
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32
src/connected_layer.c
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@ -25,13 +25,13 @@ connected_layer make_connected_layer(int batch, int inputs, int outputs, ACTIVAT
l.weight_updates = calloc(inputs*outputs, sizeof(float));
l.bias_updates = calloc(outputs, sizeof(float));
l.weights = calloc(inputs*outputs, sizeof(float));
l.weights = calloc(outputs*inputs, sizeof(float));
l.biases = calloc(outputs, sizeof(float));
//float scale = 1./sqrt(inputs);
float scale = sqrt(2./inputs);
for(i = 0; i < inputs*outputs; ++i){
for(i = 0; i < outputs*inputs; ++i){
l.weights[i] = 2*scale*rand_uniform() - scale;
}
@ -40,10 +40,10 @@ connected_layer make_connected_layer(int batch, int inputs, int outputs, ACTIVAT
}
#ifdef GPU
l.weights_gpu = cuda_make_array(l.weights, inputs*outputs);
l.weights_gpu = cuda_make_array(l.weights, outputs*inputs);
l.biases_gpu = cuda_make_array(l.biases, outputs);
l.weight_updates_gpu = cuda_make_array(l.weight_updates, inputs*outputs);
l.weight_updates_gpu = cuda_make_array(l.weight_updates, outputs*inputs);
l.bias_updates_gpu = cuda_make_array(l.bias_updates, outputs);
l.output_gpu = cuda_make_array(l.output, outputs*batch);
@ -76,7 +76,7 @@ void forward_connected_layer(connected_layer l, network_state state)
float *a = state.input;
float *b = l.weights;
float *c = l.output;
gemm(0,0,m,n,k,1,a,k,b,n,1,c,n);
gemm(0,1,m,n,k,1,a,k,b,k,1,c,n);
activate_array(l.output, l.outputs*l.batch, l.activation);
}
@ -87,11 +87,11 @@ void backward_connected_layer(connected_layer l, network_state state)
for(i = 0; i < l.batch; ++i){
axpy_cpu(l.outputs, 1, l.delta + i*l.outputs, 1, l.bias_updates, 1);
}
int m = l.inputs;
int m = l.outputs;
int k = l.batch;
int n = l.outputs;
float *a = state.input;
float *b = l.delta;
int n = l.inputs;
float *a = l.delta;
float *b = state.input;
float *c = l.weight_updates;
gemm(1,0,m,n,k,1,a,m,b,n,1,c,n);
@ -103,7 +103,7 @@ void backward_connected_layer(connected_layer l, network_state state)
b = l.weights;
c = state.delta;
if(c) gemm(0,1,m,n,k,1,a,k,b,k,1,c,n);
if(c) gemm(0,0,m,n,k,1,a,k,b,n,1,c,n);
}
#ifdef GPU
@ -146,7 +146,7 @@ void forward_connected_layer_gpu(connected_layer l, network_state state)
float * a = state.input;
float * b = l.weights_gpu;
float * c = l.output_gpu;
gemm_ongpu(0,0,m,n,k,1,a,k,b,n,1,c,n);
gemm_ongpu(0,1,m,n,k,1,a,k,b,k,1,c,n);
activate_array_ongpu(l.output_gpu, l.outputs*l.batch, l.activation);
/*
@ -163,11 +163,11 @@ void backward_connected_layer_gpu(connected_layer l, network_state state)
for(i = 0; i < l.batch; ++i){
axpy_ongpu_offset(l.outputs, 1, l.delta_gpu, i*l.outputs, 1, l.bias_updates_gpu, 0, 1);
}
int m = l.inputs;
int m = l.outputs;
int k = l.batch;
int n = l.outputs;
float * a = state.input;
float * b = l.delta_gpu;
int n = l.inputs;
float * a = l.delta_gpu;
float * b = state.input;
float * c = l.weight_updates_gpu;
gemm_ongpu(1,0,m,n,k,1,a,m,b,n,1,c,n);
@ -179,6 +179,6 @@ void backward_connected_layer_gpu(connected_layer l, network_state state)
b = l.weights_gpu;
c = state.delta;
if(c) gemm_ongpu(0,1,m,n,k,1,a,k,b,k,1,c,n);
if(c) gemm_ongpu(0,0,m,n,k,1,a,k,b,n,1,c,n);
}
#endif