From a723e1c62a27aeb39aaf7fcdeb3beb4e89fba32d Mon Sep 17 00:00:00 2001
From: Alexey <AlexeyAB@users.noreply.github.com>
Date: Wed, 15 Aug 2018 20:52:09 +0000
Subject: [PATCH] Merge pull request #766 from HotChick91/AlexeyAB-mask
---
src/convolutional_kernels.cu | 383 +++++++++++++++++++++++++++++++++++++++++++++++------
1 files changed, 335 insertions(+), 48 deletions(-)
diff --git a/src/convolutional_kernels.cu b/src/convolutional_kernels.cu
index 1de9dc0..5389828 100644
--- a/src/convolutional_kernels.cu
+++ b/src/convolutional_kernels.cu
@@ -2,6 +2,10 @@
#include "curand.h"
#include "cublas_v2.h"
+#ifdef CUDNN
+#pragma comment(lib, "cudnn.lib")
+#endif
+
extern "C" {
#include "convolutional_layer.h"
#include "batchnorm_layer.h"
@@ -17,7 +21,7 @@
{
int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x;
if (i >= n) return;
- binary[i] = (x[i] > 0) ? 1 : -1;
+ binary[i] = (x[i] >= 0) ? 1 : -1;
}
void binarize_gpu(float *x, int n, float *binary)
@@ -33,7 +37,7 @@
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){
@@ -48,58 +52,177 @@
}
-__global__ void binarize_filters_kernel(float *filters, int n, int size, float *binary)
+__global__ void binarize_weights_kernel(float *weights, int n, int size, float *binary)
{
int f = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x;
if (f >= n) return;
int i = 0;
float mean = 0;
for(i = 0; i < size; ++i){
- mean += abs(filters[f*size + i]);
+ mean += fabs(weights[f*size + i]);
}
mean = mean / size;
for(i = 0; i < size; ++i){
- binary[f*size + i] = (filters[f*size + i] > 0) ? mean : -mean;
+ binary[f*size + i] = (weights[f*size + i] > 0) ? mean : -mean;
+ //binary[f*size + i] = weights[f*size + i];
}
}
-void binarize_filters_gpu(float *filters, int n, int size, float *binary)
+void binarize_weights_gpu(float *weights, int n, int size, float *binary)
{
- binarize_filters_kernel<<<cuda_gridsize(n), BLOCK>>>(filters, n, size, binary);
+ binarize_weights_kernel<<<cuda_gridsize(n), BLOCK>>>(weights, n, size, binary);
check_error(cudaPeekAtLastError());
}
+__global__ void cuda_f32_to_f16(float* input_f32, size_t size, half *output_f16)
+{
+ int idx = blockIdx.x * blockDim.x + threadIdx.x;
+ if (idx < size) output_f16[idx] = __float2half(input_f32[idx]);
+ //if (idx < size) *((unsigned short *)output_f16 + idx) = __float2half(input_f32[idx]);
+}
+
+void cuda_convert_f32_to_f16(float* input_f32, size_t size, float *output_f16) {
+ cuda_f32_to_f16 <<< size / BLOCK + 1, BLOCK, 0, get_cuda_stream() >>> (input_f32, size, (half *)output_f16);
+}
+
+__global__ void cuda_f16_to_f32(half* input_f16, size_t size, float *output_f32)
+{
+ int idx = blockIdx.x * blockDim.x + threadIdx.x;
+ if (idx < size) output_f32[idx] = __half2float(input_f16[idx]);
+ //if (idx < size) output_f32[idx] = __half2float(*((unsigned short *)input_f16 + idx));
+}
+
+void cuda_convert_f16_to_f32(float* input_f16, size_t size, float *output_f32) {
+ cuda_f16_to_f32 <<< size / BLOCK + 1, BLOCK, 0, get_cuda_stream() >>> ((half *)input_f16, size, output_f32);
+}
+
+half *cuda_make_f16_from_f32_array(float *src, size_t n)
+{
+ half *dst16;
+ size_t size = sizeof(half)*n;
+ check_error(cudaMalloc((void **)&dst16, size));
+ if (src) {
+ cuda_convert_f32_to_f16(src, n, (float *)dst16);
+ }
+ if (!dst16) error("Cuda malloc failed\n");
+ return dst16;
+}
+
void forward_convolutional_layer_gpu(convolutional_layer l, network_state state)
{
- int i;
- int m = l.n;
- int k = l.size*l.size*l.c;
- int n = convolutional_out_height(l)*
- convolutional_out_width(l);
-
fill_ongpu(l.outputs*l.batch, 0, l.output_gpu, 1);
if(l.binary){
- binarize_filters_gpu(l.filters_gpu, l.n, l.c*l.size*l.size, l.binary_filters_gpu);
+ binarize_weights_gpu(l.weights_gpu, l.n, l.c*l.size*l.size, l.binary_weights_gpu);
swap_binary(&l);
}
if(l.xnor){
- binarize_filters_gpu(l.filters_gpu, l.n, l.c*l.size*l.size, l.binary_filters_gpu);
+ binarize_weights_gpu(l.weights_gpu, l.n, l.c*l.size*l.size, l.binary_weights_gpu);
swap_binary(&l);
- for(i = 0; i < l.batch; ++i){
- binarize_input_gpu(state.input + i*l.inputs, l.c, l.h*l.w, l.binary_input_gpu + i*l.inputs);
- }
+ binarize_gpu(state.input, l.c*l.h*l.w*l.batch, l.binary_input_gpu);
state.input = l.binary_input_gpu;
}
#ifdef CUDNN
- float one = 1;
+ float one = 1; // alpha[0], beta[0] is float for HALF and FLOAT
+ float alpha = 1, beta = 0;
+
+#ifdef CUDNN_HALF
+ // Note: For improved performance it is advised to use beta[0] = 0.0.
+ // For Tensor Core: cudnnSetConvolutionMathType() where cudnnMathType_t mathType = CUDNN_TENSOR_OP_MATH;
+ // 1. or CUDNN_CONVOLUTION_FWD_ALGO_IMPLICIT_PRECOMP_GEMM and use CUDNN_DATA_HALF
+ // 2. or CUDNN_CONVOLUTION_FWD_ALGO_WINOGRAD_NONFUSED
+ // More: http://docs.nvidia.com/deeplearning/sdk/cudnn-developer-guide/index.html#tensor_ops
+
+ const size_t input16_size = l.batch*l.c*l.w*l.h;
+ const size_t output16_size = l.batch*l.out_c*l.out_h*l.out_w;
+
+ if (*state.net.max_input16_size < input16_size) {
+ //printf("\n input16_size: cur = %zu \t max = %zu \n", input16_size, *state.net.max_input16_size);
+ *state.net.max_input16_size = input16_size;
+ if (*state.net.input16_gpu) cuda_free(*state.net.input16_gpu);
+ *state.net.input16_gpu = (float *)cuda_make_f16_from_f32_array(NULL, *state.net.max_input16_size);
+ }
+ float *input16 = *state.net.input16_gpu;
+
+ if (*state.net.max_output16_size < output16_size) {
+ *state.net.max_output16_size = output16_size;
+ if (*state.net.output16_gpu) cuda_free(*state.net.output16_gpu);
+ *state.net.output16_gpu = (float *)cuda_make_f16_from_f32_array(NULL, *state.net.max_output16_size);
+ }
+ float *output16 = *state.net.output16_gpu;
+
+ cuda_convert_f32_to_f16(state.input, input16_size, input16);
+
+ //fill_ongpu(output16_size / 2, 0, (float *)output16, 1);
+ cudnnConvolutionForward(cudnn_handle(),
+ &alpha,
+ l.srcTensorDesc,
+ input16,
+ l.weightDesc,
+ l.weights_gpu16,
+ l.convDesc,
+ l.fw_algo,
+ state.workspace,
+ l.workspace_size,
+ &beta,
+ l.dstTensorDesc,
+ output16);
+
+
+ if (l.batch_normalize)
+ {
+ if (state.train) // Training
+ {
+ copy_ongpu(l.outputs*l.batch / 2, output16, 1, l.x_gpu, 1);
+ //cudaMemcpyAsync(l.x_gpu, output16, l.outputs*l.batch*sizeof(half), cudaMemcpyDefault, get_cuda_stream());
+ float one = 1;
+ float zero = 0;
+ // Batch-normalization can still take FP16 inputs and outputs, saving half the bandwidth
+ // compared to FP32, it�s just that the statistics and value adjustment should be done in FP32.
+ cudnnBatchNormalizationForwardTraining(cudnn_handle(),
+ CUDNN_BATCHNORM_SPATIAL,
+ &one,
+ &zero,
+ l.normDstTensorDescF16,
+ l.x_gpu, // input
+ l.normDstTensorDescF16,
+ output16, // output
+ l.normTensorDesc,
+ l.scales_gpu,
+ l.biases_gpu,
+ .01,
+ l.rolling_mean_gpu, // output (should be FP32)
+ l.rolling_variance_gpu, // output (should be FP32)
+ .00001,
+ l.mean_gpu, // output (should be FP32)
+ l.variance_gpu); // output (should be FP32)
+
+ cuda_convert_f16_to_f32(output16, output16_size, l.output_gpu);
+ //forward_batchnorm_layer_gpu(l, state);
+ }
+ else // Detection
+ {
+ cuda_convert_f16_to_f32(output16, output16_size, l.output_gpu);
+ normalize_gpu(l.output_gpu, l.rolling_mean_gpu, l.rolling_variance_gpu, l.batch, l.out_c, l.out_h*l.out_w);
+ scale_bias_gpu(l.output_gpu, l.scales_gpu, l.batch, l.out_c, l.out_h*l.out_w);
+ add_bias_gpu(l.output_gpu, l.biases_gpu, l.batch, l.out_c, l.out_w*l.out_h);
+ }
+ }
+ else // BIAS only
+ {
+ cuda_convert_f16_to_f32(output16, output16_size, l.output_gpu);
+ add_bias_gpu(l.output_gpu, l.biases_gpu, l.batch, l.n, l.out_w*l.out_h);
+ }
+
+#else
+
cudnnConvolutionForward(cudnn_handle(),
&one,
l.srcTensorDesc,
state.input,
- l.filterDesc,
- l.filters_gpu,
+ l.weightDesc,
+ l.weights_gpu,
l.convDesc,
l.fw_algo,
state.workspace,
@@ -107,46 +230,168 @@
&one,
l.dstTensorDesc,
l.output_gpu);
+#endif // CUDNN_HALF
+
#else
+ int i;
+ int m = l.n;
+ int k = l.size*l.size*l.c;
+ int n = l.out_w*l.out_h;
for(i = 0; i < l.batch; ++i){
im2col_ongpu(state.input + i*l.c*l.h*l.w, l.c, l.h, l.w, l.size, l.stride, l.pad, state.workspace);
- float * a = l.filters_gpu;
+ float * a = l.weights_gpu;
float * b = state.workspace;
float * c = l.output_gpu;
gemm_ongpu(0,0,m,n,k,1.,a,k,b,n,1.,c+i*m*n,n);
}
#endif
+#ifndef CUDNN_HALF
if (l.batch_normalize) {
forward_batchnorm_layer_gpu(l, state);
}
- add_bias_gpu(l.output_gpu, l.biases_gpu, l.batch, l.n, n);
+ else {
+ add_bias_gpu(l.output_gpu, l.biases_gpu, l.batch, l.n, l.out_w*l.out_h);
+ }
+#endif // no CUDNN_HALF
- activate_array_ongpu(l.output_gpu, m*n*l.batch, l.activation);
+ activate_array_ongpu(l.output_gpu, l.outputs*l.batch, l.activation);
//if(l.dot > 0) dot_error_gpu(l);
if(l.binary || l.xnor) swap_binary(&l);
+ //cudaDeviceSynchronize(); // for correct profiling of performance
}
void backward_convolutional_layer_gpu(convolutional_layer l, network_state state)
{
- int m = l.n;
- int n = l.size*l.size*l.c;
- int k = convolutional_out_height(l)*
- convolutional_out_width(l);
+ gradient_array_ongpu(l.output_gpu, l.outputs*l.batch, l.activation, l.delta_gpu);
- gradient_array_ongpu(l.output_gpu, m*k*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);
- backward_bias_gpu(l.bias_updates_gpu, l.delta_gpu, l.batch, l.n, k);
-
+#ifndef CUDNN_HALF
if(l.batch_normalize){
backward_batchnorm_layer_gpu(l, state);
+ } else {
+ //backward_bias_gpu(l.bias_updates_gpu, l.delta_gpu, l.batch, l.n, l.out_w*l.out_h);
}
+#endif // no CUDNN_HALF
float *original_input = state.input;
if(l.xnor) state.input = l.binary_input_gpu;
#ifdef CUDNN
float one = 1;
+ float alpha = 1, beta = 0;
+
+#ifdef CUDNN_HALF
+
+ const size_t input16_size = l.batch*l.c*l.w*l.h;
+ const size_t delta16_size = l.batch*l.n*l.out_w*l.out_h;
+
+ if (*state.net.max_input16_size < input16_size) {
+ *state.net.max_input16_size = input16_size;
+ if(*state.net.input16_gpu) cuda_free(*state.net.input16_gpu);
+ *state.net.input16_gpu = (float *)cuda_make_f16_from_f32_array(NULL, *state.net.max_input16_size);
+ }
+ float *input16 = *state.net.input16_gpu;
+
+ if (*state.net.max_output16_size < delta16_size) {
+ *state.net.max_output16_size = delta16_size;
+ if(*state.net.output16_gpu) cuda_free(*state.net.output16_gpu);
+ *state.net.output16_gpu = (float *)cuda_make_f16_from_f32_array(NULL, *state.net.max_output16_size);
+ }
+ float *delta16 = *state.net.output16_gpu;
+
+ cuda_convert_f32_to_f16(state.input, input16_size, input16);
+ cuda_convert_f32_to_f16(l.delta_gpu, delta16_size, delta16);
+
+ if (l.batch_normalize) {
+ //if (!state.train) {
+ // l.mean_gpu = l.rolling_mean_gpu;
+ // l.variance_gpu = l.rolling_variance_gpu;
+ //}
+ float one = 1;
+ float zero = 0;
+ cudnnBatchNormalizationBackward(cudnn_handle(),
+ CUDNN_BATCHNORM_SPATIAL,
+ &one,
+ &zero,
+ &one,
+ &one,
+ l.normDstTensorDescF16,
+ l.x_gpu, // input
+ l.normDstTensorDescF16,
+ delta16, // input
+ l.normDstTensorDescF16,
+ l.x_norm_gpu, // output
+ l.normTensorDesc,
+ l.scales_gpu, // output (should be FP32)
+ l.scale_updates_gpu, // output (should be FP32)
+ l.bias_updates_gpu, // output (should be FP32)
+ .00001,
+ l.mean_gpu, // input (should be FP32)
+ l.variance_gpu); // input (should be FP32)
+ copy_ongpu(l.outputs*l.batch / 2, l.x_norm_gpu, 1, delta16, 1);
+ //cudaMemcpyAsync(delta16, l.x_norm_gpu, l.outputs*l.batch * sizeof(half), cudaMemcpyDefault, get_cuda_stream());
+ }
+ else
+ {
+ //backward_bias_gpu(l.bias_updates_gpu, l.delta_gpu, l.batch, l.n, l.out_w*l.out_h);
+ }
+
+ // convert input: state.input (x), l.delta_gpu (y) from fp32 to fp16
+ // get output: l.weight_updates_gpu (dw) and convert it to fp32 (ONLY if it is fp16)
+
+ // calculate conv weight updates
+ // Already: l.weight_updates_gpu = (l.weight_updates_gpu - l.weight*decay*batch*subdivision)*momentum
+ // so we should copy f32 to f16, or compute: f16=(w_up - w*d*b*s)*m
+ cuda_convert_f32_to_f16(l.weight_updates_gpu, l.c*l.n*l.size*l.size, l.weight_updates_gpu16);
+
+ cudnnConvolutionBackwardFilter(cudnn_handle(),
+ &one,
+ l.srcTensorDesc,
+ input16, //state.input,
+ l.ddstTensorDesc,
+ delta16, //l.delta_gpu,
+ l.convDesc,
+ l.bf_algo,
+ state.workspace,
+ l.workspace_size,
+ &one,
+ l.dweightDesc,
+ l.weight_updates_gpu16); // l.weight_updates_gpu);
+
+ cuda_convert_f16_to_f32(l.weight_updates_gpu16, l.c*l.n*l.size*l.size, l.weight_updates_gpu);
+
+ if (state.delta) {
+ if (l.binary || l.xnor) swap_binary(&l);
+
+ // http://docs.nvidia.com/deeplearning/sdk/cudnn-developer-guide/index.html#cudnnConvolutionBackwardData
+ // calculate delta for the next layer
+ // convert input: l.weights_gpu (w), l.delta_gpu (dy) from fp32 to fp16
+ // get output: state.delta (dx) and convert it to fp32 (ONLY if it is fp16)
+ cudnnConvolutionBackwardData(cudnn_handle(),
+ &alpha,
+ l.weightDesc,
+ l.weights_gpu16, //l.weights_gpu,
+ l.ddstTensorDesc,
+ delta16, //l.delta_gpu,
+ l.convDesc,
+ l.bd_algo,
+ state.workspace,
+ l.workspace_size,
+ &beta,
+ l.dsrcTensorDesc,
+ input16); // state.delta);
+
+ cuda_convert_f16_to_f32(input16, input16_size, state.delta);
+
+ if (l.binary || l.xnor) swap_binary(&l);
+ if (l.xnor) gradient_array_ongpu(original_input, l.batch*l.c*l.h*l.w, HARDTAN, state.delta);
+ }
+#else // CUDNN_HALF
+
+ // calculate conv weight updates
+ // if used: beta=1 then loss decreases faster
cudnnConvolutionBackwardFilter(cudnn_handle(),
&one,
l.srcTensorDesc,
@@ -158,15 +403,17 @@
state.workspace,
l.workspace_size,
&one,
- l.dfilterDesc,
- l.filter_updates_gpu);
+ l.dweightDesc,
+ l.weight_updates_gpu);
if(state.delta){
if(l.binary || l.xnor) swap_binary(&l);
+ // http://docs.nvidia.com/deeplearning/sdk/cudnn-developer-guide/index.html#cudnnConvolutionBackwardData
+ // calculate delta for the next layer
cudnnConvolutionBackwardData(cudnn_handle(),
&one,
- l.filterDesc,
- l.filters_gpu,
+ l.weightDesc,
+ l.weights_gpu,
l.ddstTensorDesc,
l.delta_gpu,
l.convDesc,
@@ -180,19 +427,25 @@
if(l.xnor) gradient_array_ongpu(original_input, l.batch*l.c*l.h*l.w, HARDTAN, state.delta);
}
-#else
+#endif // CUDNN_HALF
+
+#else // CUDNN
+ int m = l.n;
+ int n = l.size*l.size*l.c;
+ int k = l.out_w*l.out_h;
+
int i;
for(i = 0; i < l.batch; ++i){
float * a = l.delta_gpu;
float * b = state.workspace;
- float * c = l.filter_updates_gpu;
+ float * c = l.weight_updates_gpu;
im2col_ongpu(state.input + i*l.c*l.h*l.w, l.c, l.h, l.w, l.size, l.stride, l.pad, state.workspace);
gemm_ongpu(0,1,m,n,k,1,a + i*m*k,k,b,k,1,c,n);
if(state.delta){
if(l.binary || l.xnor) swap_binary(&l);
- float * a = l.filters_gpu;
+ float * a = l.weights_gpu;
float * b = l.delta_gpu;
float * c = state.workspace;
@@ -210,43 +463,77 @@
void pull_convolutional_layer(convolutional_layer layer)
{
- cuda_pull_array(layer.filters_gpu, layer.filters, layer.c*layer.n*layer.size*layer.size);
+ cuda_pull_array(layer.weights_gpu, layer.weights, layer.c*layer.n*layer.size*layer.size);
cuda_pull_array(layer.biases_gpu, layer.biases, layer.n);
- cuda_pull_array(layer.filter_updates_gpu, layer.filter_updates, layer.c*layer.n*layer.size*layer.size);
+ cuda_pull_array(layer.weight_updates_gpu, layer.weight_updates, layer.c*layer.n*layer.size*layer.size);
cuda_pull_array(layer.bias_updates_gpu, layer.bias_updates, layer.n);
if (layer.batch_normalize){
cuda_pull_array(layer.scales_gpu, layer.scales, layer.n);
cuda_pull_array(layer.rolling_mean_gpu, layer.rolling_mean, layer.n);
cuda_pull_array(layer.rolling_variance_gpu, layer.rolling_variance, layer.n);
}
+ if (layer.adam){
+ cuda_pull_array(layer.m_gpu, layer.m, layer.c*layer.n*layer.size*layer.size);
+ cuda_pull_array(layer.v_gpu, layer.v, layer.c*layer.n*layer.size*layer.size);
+ }
}
void push_convolutional_layer(convolutional_layer layer)
{
- cuda_push_array(layer.filters_gpu, layer.filters, layer.c*layer.n*layer.size*layer.size);
+ cuda_push_array(layer.weights_gpu, layer.weights, layer.c*layer.n*layer.size*layer.size);
+#ifdef CUDNN_HALF
+ cuda_convert_f32_to_f16(layer.weights_gpu, layer.c*layer.n*layer.size*layer.size, layer.weights_gpu16);
+#endif
cuda_push_array(layer.biases_gpu, layer.biases, layer.n);
- cuda_push_array(layer.filter_updates_gpu, layer.filter_updates, layer.c*layer.n*layer.size*layer.size);
+ cuda_push_array(layer.weight_updates_gpu, layer.weight_updates, layer.c*layer.n*layer.size*layer.size);
cuda_push_array(layer.bias_updates_gpu, layer.bias_updates, layer.n);
if (layer.batch_normalize){
cuda_push_array(layer.scales_gpu, layer.scales, layer.n);
cuda_push_array(layer.rolling_mean_gpu, layer.rolling_mean, layer.n);
cuda_push_array(layer.rolling_variance_gpu, layer.rolling_variance, layer.n);
}
+ if (layer.adam){
+ cuda_push_array(layer.m_gpu, layer.m, layer.c*layer.n*layer.size*layer.size);
+ cuda_push_array(layer.v_gpu, layer.v, layer.c*layer.n*layer.size*layer.size);
+ }
}
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);
- axpy_ongpu(layer.n, learning_rate/batch, layer.scale_updates_gpu, 1, layer.scales_gpu, 1);
- scal_ongpu(layer.n, momentum, layer.scale_updates_gpu, 1);
+ if(layer.scales_gpu){
+ axpy_ongpu(layer.n, learning_rate/batch, layer.scale_updates_gpu, 1, layer.scales_gpu, 1);
+ scal_ongpu(layer.n, momentum, layer.scale_updates_gpu, 1);
+ }
- axpy_ongpu(size, -decay*batch, layer.filters_gpu, 1, layer.filter_updates_gpu, 1);
- axpy_ongpu(size, learning_rate/batch, layer.filter_updates_gpu, 1, layer.filters_gpu, 1);
- scal_ongpu(size, momentum, layer.filter_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{
+ // update weights:
+ // weights_gpu = weights_gpu*(1 - decay*lr) + weight_updates_gpu*lr / (batch*subdivision) =
+ // weights_gpu*(1 - 0.0005*0.001) + weight_updates_gpu*0.001/(64*8) =
+ // weights_gpu * 0.999 999 5 + weight_updates_gpu * 0.000 001 953125
+ //
+ // weight_updates_gpu = (weight_updates_gpu - weights_gpu*decay*batch*subdivision)*momentum =
+ // (weight_updates_gpu - weights_gpu * 0.0005 * 64 * 8) * 0.9 =
+ // weight_updates_gpu*0.9 - weights_gpu*0.2304
+ 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);
+ }
}
--
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