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Improve training performance - batch-norm using cuDNN.

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This commit is contained in:
AlexeyAB
2018-03-20 02:16:51 +03:00
parent 2f52cfeb07
commit 537d135feb
12 changed files with 193 additions and 42 deletions
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10
src/convolutional_kernels.cu
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@ -37,7 +37,7 @@ __global__ void binarize_input_kernel(float *input, int n, int size, float *bina
int i = 0;
float mean = 0;
for(i = 0; i < n; ++i){
mean += abs(input[i*size + s]);
mean += fabs(input[i*size + s]);
}
mean = mean / n;
for(i = 0; i < n; ++i){
@ -59,7 +59,7 @@ __global__ void binarize_weights_kernel(float *weights, int n, int size, float *
int i = 0;
float mean = 0;
for(i = 0; i < size; ++i){
mean += abs(weights[f*size + i]);
mean += fabs(weights[f*size + i]);
}
mean = mean / size;
for(i = 0; i < size; ++i){
@ -205,8 +205,10 @@ void forward_convolutional_layer_gpu(convolutional_layer l, network_state state)
if (l.batch_normalize) {
forward_batchnorm_layer_gpu(l, state);
}
add_bias_gpu(l.output_gpu, l.biases_gpu, l.batch, l.n, l.out_w*l.out_h);
}
else {
add_bias_gpu(l.output_gpu, l.biases_gpu, l.batch, l.n, l.out_w*l.out_h);
}
activate_array_ongpu(l.output_gpu, l.outputs*l.batch, l.activation);
//if(l.dot > 0) dot_error_gpu(l);