initial commit

src/Dense.cpp

@@ -0,0 +1,54 @@
+#include <iostream>
+
+#include "Layer.h"
+
+namespace tkDNN {
+
+Dense::Dense(Network *net, dataDim_t in_dim, 
+    int out_ch, const char* fname_weights, const char* fname_bias) : 
+    LayerWgs(net, in_dim, in_dim.tot(), out_ch, 1, 1, 1, fname_weights, fname_bias) {
+
+    this->out_ch = out_ch;
+    //allocate data for infer result
+    checkCuda( cudaMalloc(&dstData, outputs*sizeof(value_type)) );
+}
+
+Dense::~Dense() {
+
+    checkCuda( cudaFree(dstData) );
+}
+
+value_type* Dense::infer(dataDim_t &dim, value_type* srcData) {
+
+    if (dim.n != 1)
+        FatalError("Not Implemented"); 
+    
+    int dim_x = dim.tot();
+    int dim_y = outputs;
+
+    if (dim_x != inputs)
+        FatalError("Input mismatch");
+
+    value_type alpha = value_type(1), beta = value_type(1);
+    // place bias into dstData
+    checkCuda( cudaMemcpy(dstData, bias_d, dim_y*sizeof(value_type), cudaMemcpyDeviceToDevice) );
+    
+    //do matrix moltiplication
+    checkERROR( cublasSgemv(net->cublasHandle, CUBLAS_OP_T,
+                            dim_x, dim_y,
+                            &alpha,
+                            data_d, dim_x,
+                            srcData, 1,
+                            &beta,
+                            dstData, 1) );
+
+    //update data dimensions    
+    dim.h = 1;
+    dim.w = 1;
+    dim.l = 1;
+    dim.c = dim_y;
+
+    return dstData;
+}
+
+}