| | |
| | | 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; |
| | | |
| | | axpy_ongpu(layer.n, learning_rate/batch, layer.bias_updates_gpu, 1, layer.biases_gpu, 1); |
| | | scal_ongpu(layer.n, momentum, layer.bias_updates_gpu, 1); |
| | | |
| | |
| | | scal_ongpu(layer.n, momentum, layer.scale_updates_gpu, 1); |
| | | } |
| | | |
| | | axpy_ongpu(size, -decay*batch, layer.weights_gpu, 1, layer.weight_updates_gpu, 1); |
| | | axpy_ongpu(size, learning_rate/batch, layer.weight_updates_gpu, 1, layer.weights_gpu, 1); |
| | | scal_ongpu(size, momentum, layer.weight_updates_gpu, 1); |
| | | if(layer.adam){ |
| | | scal_ongpu(size, layer.B1, layer.m_gpu, 1); |
| | | scal_ongpu(size, layer.B2, layer.v_gpu, 1); |
| | | |
| | | axpy_ongpu(size, -decay*batch, layer.weights_gpu, 1, layer.weight_updates_gpu, 1); |
| | | |
| | | axpy_ongpu(size, -(1-layer.B1), layer.weight_updates_gpu, 1, layer.m_gpu, 1); |
| | | mul_ongpu(size, layer.weight_updates_gpu, 1, layer.weight_updates_gpu, 1); |
| | | axpy_ongpu(size, (1-layer.B2), layer.weight_updates_gpu, 1, layer.v_gpu, 1); |
| | | |
| | | adam_gpu(size, layer.weights_gpu, layer.m_gpu, layer.v_gpu, layer.B1, layer.B2, learning_rate/batch, layer.eps, layer.t+1); |
| | | fill_ongpu(size, 0, layer.weight_updates_gpu, 1); |
| | | }else{ |
| | | axpy_ongpu(size, -decay*batch, layer.weights_gpu, 1, layer.weight_updates_gpu, 1); |
| | | axpy_ongpu(size, learning_rate/batch, layer.weight_updates_gpu, 1, layer.weights_gpu, 1); |
| | | scal_ongpu(size, momentum, layer.weight_updates_gpu, 1); |
| | | } |
| | | } |
| | | |
| | | |