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Fix training approach (convolutional layer)

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This commit is contained in:
AlexeyAB
2018-12-27 00:31:28 +03:00
parent dc827f4c1c
commit 64e478db07
4 changed files with 68 additions and 7 deletions
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44
src/convolutional_kernels.cu
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@ -457,7 +457,8 @@ void backward_convolutional_layer_gpu(convolutional_layer l, network_state state
{
gradient_array_ongpu(l.output_gpu, l.outputs*l.batch, l.activation, l.delta_gpu);
backward_bias_gpu(l.bias_updates_gpu, l.delta_gpu, l.batch, l.n, l.out_w*l.out_h);
if (!l.batch_normalize)
backward_bias_gpu(l.bias_updates_gpu, l.delta_gpu, l.batch, l.n, l.out_w*l.out_h);
//#ifndef CUDNN_HALF
//if(l.batch_normalize){
@ -703,6 +704,45 @@ void push_convolutional_layer(convolutional_layer layer)
}
}
void update_convolutional_layer_gpu(layer l, int batch, float learning_rate_init, float momentum, float decay)
{
float learning_rate = learning_rate_init*l.learning_rate_scale;
//float momentum = a.momentum;
//float decay = a.decay;
//int batch = a.batch;
int size = l.size*l.size*l.c*l.n; // old
if (l.adam) {
//adam_update_gpu(l.weights_gpu, l.weight_updates_gpu, l.m_gpu, l.v_gpu, a.B1, a.B2, a.eps, decay, learning_rate, l.nweights, batch, a.t);
adam_update_gpu(l.weights_gpu, l.weight_updates_gpu, l.m_gpu, l.v_gpu, l.B1, l.B2, l.eps, decay, learning_rate, size, batch, l.t);
adam_update_gpu(l.biases_gpu, l.bias_updates_gpu, l.bias_m_gpu, l.bias_v_gpu, l.B1, l.B2, l.eps, decay, learning_rate, l.n, batch, l.t);
if (l.scales_gpu) {
adam_update_gpu(l.scales_gpu, l.scale_updates_gpu, l.scale_m_gpu, l.scale_v_gpu, l.B1, l.B2, l.eps, decay, learning_rate, l.n, batch, l.t);
}
}
else {
//axpy_ongpu(l.nweights, -decay*batch, l.weights_gpu, 1, l.weight_updates_gpu, 1);
//axpy_ongpu(l.nweights, learning_rate / batch, l.weight_updates_gpu, 1, l.weights_gpu, 1);
//scal_ongpu(l.nweights, momentum, l.weight_updates_gpu, 1);
axpy_ongpu(size, -decay*batch, l.weights_gpu, 1, l.weight_updates_gpu, 1);
axpy_ongpu(size, learning_rate / batch, l.weight_updates_gpu, 1, l.weights_gpu, 1);
scal_ongpu(size, momentum, l.weight_updates_gpu, 1);
axpy_ongpu(l.n, learning_rate / batch, l.bias_updates_gpu, 1, l.biases_gpu, 1);
scal_ongpu(l.n, momentum, l.bias_updates_gpu, 1);
if (l.scales_gpu) {
axpy_ongpu(l.n, learning_rate / batch, l.scale_updates_gpu, 1, l.scales_gpu, 1);
scal_ongpu(l.n, momentum, l.scale_updates_gpu, 1);
}
}
//if (l.clip) {
// constrain_gpu(l.nweights, l.clip, l.weights_gpu, 1);
//}
}
/*
void update_convolutional_layer_gpu(convolutional_layer layer, int batch, float learning_rate, float momentum, float decay)
{
int size = layer.size*layer.size*layer.c*layer.n;
@ -753,5 +793,5 @@ void update_convolutional_layer_gpu(convolutional_layer layer, int batch, float
//-----------------------------------
}
}
*/