| | |
| | | void update_shortcut_layer_gpu(layer l, int batch, float learning_rate_init, float momentum, float decay, float loss_scale)
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| | | {
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| | | if (l.nweights > 0) {
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| | | float learning_rate = learning_rate_init*l.learning_rate_scale;
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| | | float learning_rate = learning_rate_init*l.learning_rate_scale / loss_scale; |
| | | //float momentum = a.momentum;
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| | | //float decay = a.decay;
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| | | //int batch = a.batch;
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| | |
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| | | // Loss scale for Mixed-Precision on Tensor-Cores
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| | | if (loss_scale != 1.0) {
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| | | if(l.weight_updates_gpu && l.nweights > 0) scal_ongpu(l.nweights, 1.0 / loss_scale, l.weight_updates_gpu, 1);
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| | | }
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| | |
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| | | reset_nan_and_inf(l.weight_updates_gpu, l.nweights);
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| | | fix_nan_and_inf(l.weights_gpu, l.nweights);
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