Epoch 1/5
1/1 [==============================] - ETA: 0s - loss: 0.2380␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
1/1 [==============================] - 0s 491ms/step - loss: 0.2380
Epoch 2/5
1/1 [==============================] - ETA: 0s - loss: 0.2301␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
1/1 [==============================] - 0s 3ms/step - loss: 0.2301
Epoch 3/5
1/1 [==============================] - ETA: 0s - loss: 0.2232␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
1/1 [==============================] - 0s 3ms/step - loss: 0.2232
Epoch 4/5
1/1 [==============================] - ETA: 0s - loss: 0.2172␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
1/1 [==============================] - 0s 3ms/step - loss: 0.2172
Epoch 5/5
1/1 [==============================] - ETA: 0s - loss: 0.2119␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
1/1 [==============================] - 0s 3ms/step - loss: 0.2119
Model requires following inputs:
Fully Specified Tensor name: input1 type: float shape: [4,64]
Model initialized the following tensors:
Tensor name: "dense3bias0" type: float shape: [4]
Tensor name: "dense3kernel0" type: float shape: [8,4]
Tensor name: "dense2bias0" type: float shape: [8]
Tensor name: "dense2kernel0" type: float shape: [16,8]
Tensor name: "dense1bias0" type: float shape: [16]
Tensor name: "dense1kernel0" type: float shape: [32,16]
Tensor name: "densebias0" type: float shape: [32]
Tensor name: "densekernel0" type: float shape: [64,32]
Model specify the following intermediate tensors:
Tensor name: "reluRelu0" type: float shape: [4,4]
Tensor name: "dense3BiasAdd0" type: float shape: [4,4]
Tensor name: "dense2Dense" type: float shape: [4,8]
Tensor name: "dense2Relu0" type: float shape: [4,8]
Tensor name: "dense1Dense" type: float shape: [4,16]
Tensor name: "dense1bias0bcast" type: float shape: [4,16]
Tensor name: "dense3bias0bcast" type: float shape: [4,4]
Tensor name: "dense1Relu0" type: float shape: [4,16]
Tensor name: "activationRelu0" type: float shape: [4,32]
Tensor name: "denseBiasAdd0" type: float shape: [4,32]
Tensor name: "dense2bias0bcast" type: float shape: [4,8]
Tensor name: "densebias0bcast" type: float shape: [4,32]
Tensor "dense2bias0" already exist: true
Shape of tensor "dense2bias0": 8,
Data type of tensor "dense2bias0": float
//Code generated automatically by TMVA for Inference of Model file [KerasModel.h5] at [Thu Oct 10 01:41:25 2024]
#ifndef TMVA_SOFIE_KERASMODEL
#define TMVA_SOFIE_KERASMODEL
#include<algorithm>
#include<vector>
#include "TMVA/SOFIE_common.hxx"
#include <fstream>
namespace TMVA_SOFIE_KerasModel{
namespace BLAS{
extern "C" void sgemv_(const char * trans, const int * m, const int * n, const float * alpha, const float * A,
const int * lda, const float * X, const int * incx, const float * beta, const float * Y, const int * incy);
extern "C" void sgemm_(const char * transa, const char * transb, const int * m, const int * n, const int * k,
const float * alpha, const float * A, const int * lda, const float * B, const int * ldb,
const float * beta, float * C, const int * ldc);
}//BLAS
struct Session {
std::vector<float> fTensor_dense3bias0 = std::vector<float>(4);
float * tensor_dense3bias0 = fTensor_dense3bias0.data();
std::vector<float> fTensor_dense3kernel0 = std::vector<float>(32);
float * tensor_dense3kernel0 = fTensor_dense3kernel0.data();
std::vector<float> fTensor_dense2bias0 = std::vector<float>(8);
float * tensor_dense2bias0 = fTensor_dense2bias0.data();
std::vector<float> fTensor_dense2kernel0 = std::vector<float>(128);
float * tensor_dense2kernel0 = fTensor_dense2kernel0.data();
std::vector<float> fTensor_dense1bias0 = std::vector<float>(16);
float * tensor_dense1bias0 = fTensor_dense1bias0.data();
std::vector<float> fTensor_dense1kernel0 = std::vector<float>(512);
float * tensor_dense1kernel0 = fTensor_dense1kernel0.data();
std::vector<float> fTensor_densebias0 = std::vector<float>(32);
float * tensor_densebias0 = fTensor_densebias0.data();
std::vector<float> fTensor_densekernel0 = std::vector<float>(2048);
float * tensor_densekernel0 = fTensor_densekernel0.data();
std::vector<float> fTensor_reluRelu0 = std::vector<float>(16);
float * tensor_reluRelu0 = fTensor_reluRelu0.data();
std::vector<float> fTensor_dense3BiasAdd0 = std::vector<float>(16);
float * tensor_dense3BiasAdd0 = fTensor_dense3BiasAdd0.data();
std::vector<float> fTensor_dense2Dense = std::vector<float>(32);
float * tensor_dense2Dense = fTensor_dense2Dense.data();
std::vector<float> fTensor_dense2Relu0 = std::vector<float>(32);
float * tensor_dense2Relu0 = fTensor_dense2Relu0.data();
std::vector<float> fTensor_dense1Dense = std::vector<float>(64);
float * tensor_dense1Dense = fTensor_dense1Dense.data();
std::vector<float> fTensor_dense1bias0bcast = std::vector<float>(64);
float * tensor_dense1bias0bcast = fTensor_dense1bias0bcast.data();
std::vector<float> fTensor_dense3bias0bcast = std::vector<float>(16);
float * tensor_dense3bias0bcast = fTensor_dense3bias0bcast.data();
std::vector<float> fTensor_dense1Relu0 = std::vector<float>(64);
float * tensor_dense1Relu0 = fTensor_dense1Relu0.data();
std::vector<float> fTensor_activationRelu0 = std::vector<float>(128);
float * tensor_activationRelu0 = fTensor_activationRelu0.data();
std::vector<float> fTensor_denseBiasAdd0 = std::vector<float>(128);
float * tensor_denseBiasAdd0 = fTensor_denseBiasAdd0.data();
std::vector<float> fTensor_dense2bias0bcast = std::vector<float>(32);
float * tensor_dense2bias0bcast = fTensor_dense2bias0bcast.data();
std::vector<float> fTensor_densebias0bcast = std::vector<float>(128);
float * tensor_densebias0bcast = fTensor_densebias0bcast.data();
Session(std::string filename ="") {
if (filename.empty()) filename = "KerasModel.dat";
std::ifstream f;
f.open(filename);
if (!f.is_open()){
throw std::runtime_error("tmva-sofie failed to open file for input weights");
}
std::string tensor_name;
int length;
f >> tensor_name >> length;
if (tensor_name != "tensor_dense3bias0" ) {
std::string err_msg = "TMVA-SOFIE failed to read the correct tensor name; expected name is tensor_dense3bias0 , read " + tensor_name;
throw std::runtime_error(err_msg);
}
if (length != 4) {
std::string err_msg = "TMVA-SOFIE failed to read the correct tensor size; expected size is 4 , read " + std::to_string(length) ;
throw std::runtime_error(err_msg);
}
for (int i =0; i < length; ++i)
f >> tensor_dense3bias0[i];
f >> tensor_name >> length;
if (tensor_name != "tensor_dense3kernel0" ) {
std::string err_msg = "TMVA-SOFIE failed to read the correct tensor name; expected name is tensor_dense3kernel0 , read " + tensor_name;
throw std::runtime_error(err_msg);
}
if (length != 32) {
std::string err_msg = "TMVA-SOFIE failed to read the correct tensor size; expected size is 32 , read " + std::to_string(length) ;
throw std::runtime_error(err_msg);
}
for (int i =0; i < length; ++i)
f >> tensor_dense3kernel0[i];
f >> tensor_name >> length;
if (tensor_name != "tensor_dense2bias0" ) {
std::string err_msg = "TMVA-SOFIE failed to read the correct tensor name; expected name is tensor_dense2bias0 , read " + tensor_name;
throw std::runtime_error(err_msg);
}
if (length != 8) {
std::string err_msg = "TMVA-SOFIE failed to read the correct tensor size; expected size is 8 , read " + std::to_string(length) ;
throw std::runtime_error(err_msg);
}
for (int i =0; i < length; ++i)
f >> tensor_dense2bias0[i];
f >> tensor_name >> length;
if (tensor_name != "tensor_dense2kernel0" ) {
std::string err_msg = "TMVA-SOFIE failed to read the correct tensor name; expected name is tensor_dense2kernel0 , read " + tensor_name;
throw std::runtime_error(err_msg);
}
if (length != 128) {
std::string err_msg = "TMVA-SOFIE failed to read the correct tensor size; expected size is 128 , read " + std::to_string(length) ;
throw std::runtime_error(err_msg);
}
for (int i =0; i < length; ++i)
f >> tensor_dense2kernel0[i];
f >> tensor_name >> length;
if (tensor_name != "tensor_dense1bias0" ) {
std::string err_msg = "TMVA-SOFIE failed to read the correct tensor name; expected name is tensor_dense1bias0 , read " + tensor_name;
throw std::runtime_error(err_msg);
}
if (length != 16) {
std::string err_msg = "TMVA-SOFIE failed to read the correct tensor size; expected size is 16 , read " + std::to_string(length) ;
throw std::runtime_error(err_msg);
}
for (int i =0; i < length; ++i)
f >> tensor_dense1bias0[i];
f >> tensor_name >> length;
if (tensor_name != "tensor_dense1kernel0" ) {
std::string err_msg = "TMVA-SOFIE failed to read the correct tensor name; expected name is tensor_dense1kernel0 , read " + tensor_name;
throw std::runtime_error(err_msg);
}
if (length != 512) {
std::string err_msg = "TMVA-SOFIE failed to read the correct tensor size; expected size is 512 , read " + std::to_string(length) ;
throw std::runtime_error(err_msg);
}
for (int i =0; i < length; ++i)
f >> tensor_dense1kernel0[i];
f >> tensor_name >> length;
if (tensor_name != "tensor_densebias0" ) {
std::string err_msg = "TMVA-SOFIE failed to read the correct tensor name; expected name is tensor_densebias0 , read " + tensor_name;
throw std::runtime_error(err_msg);
}
if (length != 32) {
std::string err_msg = "TMVA-SOFIE failed to read the correct tensor size; expected size is 32 , read " + std::to_string(length) ;
throw std::runtime_error(err_msg);
}
for (int i =0; i < length; ++i)
f >> tensor_densebias0[i];
f >> tensor_name >> length;
if (tensor_name != "tensor_densekernel0" ) {
std::string err_msg = "TMVA-SOFIE failed to read the correct tensor name; expected name is tensor_densekernel0 , read " + tensor_name;
throw std::runtime_error(err_msg);
}
if (length != 2048) {
std::string err_msg = "TMVA-SOFIE failed to read the correct tensor size; expected size is 2048 , read " + std::to_string(length) ;
throw std::runtime_error(err_msg);
}
for (int i =0; i < length; ++i)
f >> tensor_densekernel0[i];
f.close();
{
float * data = TMVA::Experimental::SOFIE::UTILITY::UnidirectionalBroadcast<float>(tensor_densebias0,{ 32 }, { 4 , 32 });
std::copy(data, data + 128, tensor_densebias0bcast);
delete [] data;
}
{
float * data = TMVA::Experimental::SOFIE::UTILITY::UnidirectionalBroadcast<float>(tensor_dense1bias0,{ 16 }, { 4 , 16 });
std::copy(data, data + 64, tensor_dense1bias0bcast);
delete [] data;
}
{
float * data = TMVA::Experimental::SOFIE::UTILITY::UnidirectionalBroadcast<float>(tensor_dense2bias0,{ 8 }, { 4 , 8 });
std::copy(data, data + 32, tensor_dense2bias0bcast);
delete [] data;
}
{
float * data = TMVA::Experimental::SOFIE::UTILITY::UnidirectionalBroadcast<float>(tensor_dense3bias0,{ 4 }, { 4 , 4 });
std::copy(data, data + 16, tensor_dense3bias0bcast);
delete [] data;
}
}
std::vector<float> infer(float* tensor_input1){
//--------- Gemm
char op_0_transA = 'n';
char op_0_transB = 'n';
int op_0_m = 4;
int op_0_n = 32;
int op_0_k = 64;
float op_0_alpha = 1;
float op_0_beta = 1;
int op_0_lda = 64;
int op_0_ldb = 32;
std::copy(tensor_densebias0bcast, tensor_densebias0bcast + 128, tensor_denseBiasAdd0);
BLAS::sgemm_(&op_0_transB, &op_0_transA, &op_0_n, &op_0_m, &op_0_k, &op_0_alpha, tensor_densekernel0, &op_0_ldb, tensor_input1, &op_0_lda, &op_0_beta, tensor_denseBiasAdd0, &op_0_n);
//------ RELU
for (int id = 0; id < 128 ; id++){
tensor_activationRelu0[id] = ((tensor_denseBiasAdd0[id] > 0 )? tensor_denseBiasAdd0[id] : 0);
}
//--------- Gemm
char op_2_transA = 'n';
char op_2_transB = 'n';
int op_2_m = 4;
int op_2_n = 16;
int op_2_k = 32;
float op_2_alpha = 1;
float op_2_beta = 1;
int op_2_lda = 32;
int op_2_ldb = 16;
std::copy(tensor_dense1bias0bcast, tensor_dense1bias0bcast + 64, tensor_dense1Dense);
BLAS::sgemm_(&op_2_transB, &op_2_transA, &op_2_n, &op_2_m, &op_2_k, &op_2_alpha, tensor_dense1kernel0, &op_2_ldb, tensor_activationRelu0, &op_2_lda, &op_2_beta, tensor_dense1Dense, &op_2_n);
//------ RELU
for (int id = 0; id < 64 ; id++){
tensor_dense1Relu0[id] = ((tensor_dense1Dense[id] > 0 )? tensor_dense1Dense[id] : 0);
}
//--------- Gemm
char op_4_transA = 'n';
char op_4_transB = 'n';
int op_4_m = 4;
int op_4_n = 8;
int op_4_k = 16;
float op_4_alpha = 1;
float op_4_beta = 1;
int op_4_lda = 16;
int op_4_ldb = 8;
std::copy(tensor_dense2bias0bcast, tensor_dense2bias0bcast + 32, tensor_dense2Dense);
BLAS::sgemm_(&op_4_transB, &op_4_transA, &op_4_n, &op_4_m, &op_4_k, &op_4_alpha, tensor_dense2kernel0, &op_4_ldb, tensor_dense1Relu0, &op_4_lda, &op_4_beta, tensor_dense2Dense, &op_4_n);
//------ RELU
for (int id = 0; id < 32 ; id++){
tensor_dense2Relu0[id] = ((tensor_dense2Dense[id] > 0 )? tensor_dense2Dense[id] : 0);
}
//--------- Gemm
char op_6_transA = 'n';
char op_6_transB = 'n';
int op_6_m = 4;
int op_6_n = 4;
int op_6_k = 8;
float op_6_alpha = 1;
float op_6_beta = 1;
int op_6_lda = 8;
int op_6_ldb = 4;
std::copy(tensor_dense3bias0bcast, tensor_dense3bias0bcast + 16, tensor_dense3BiasAdd0);
BLAS::sgemm_(&op_6_transB, &op_6_transA, &op_6_n, &op_6_m, &op_6_k, &op_6_alpha, tensor_dense3kernel0, &op_6_ldb, tensor_dense2Relu0, &op_6_lda, &op_6_beta, tensor_dense3BiasAdd0, &op_6_n);
//------ RELU
for (int id = 0; id < 16 ; id++){
tensor_reluRelu0[id] = ((tensor_dense3BiasAdd0[id] > 0 )? tensor_dense3BiasAdd0[id] : 0);
}
std::vector<float> ret (tensor_reluRelu0, tensor_reluRelu0 + 16);
return ret;
}
};
} //TMVA_SOFIE_KerasModel
#endif // TMVA_SOFIE_KERASMODEL