I would like to synthesize a three-layer fully coupled neural network at a high level.
I tried to use HLScompiler to high-level synthesize the C++ code, but I got an error.
If anyone can tell me how to solve this error, I would appreciate it.
cppcode
#include <algorithm>
#include <iostream>
#include <vector>
#include <math.h>
#include <fstream>
#include <sstream>
#include <string>
#include <random>
#include "HLS/hls.h"
using namespace std;
void print ( const vector <float>& m, int n_rows, int n_columns ) {
/* "Couts" the input vector as n_rows x n_columns matrix.
Inputs:
m: vector, matrix of size n_rows x n_columns
n_rows: int, number of rows in the left matrix m1
n_columns: int, number of columns in the left matrix m1
*/
for( int i = 0; i != n_rows; ++i ) {
for( int j = 0; j != n_columns; ++j ) {
cout << m[ i * n_columns + j ] << " ";
}
cout << '\n';
}
cout << endl;
}
int argmax ( const vector <float>& m ) {
return distance(m.begin(), max_element(m.begin(), m.end()));
}
vector <float> relu(const vector <float>& z){
int size = z.size();
vector <float> output;
for( int i = 0; i < size; ++i ) {
if (z[i] < 0){
output.push_back(0.0);
}
else output.push_back(z[i]);
}
return output;
}
vector <float> reluPrime (const vector <float>& z) {
int size = z.size();
vector <float> output;
for( int i = 0; i < size; ++i ) {
if (z[i] <= 0){
output.push_back(0.0);
}
else output.push_back(1.0);
}
return output;
}
vector <float> softmax (const vector <float>& z, const int dim) {
const int zsize = static_cast<int>(z.size());
vector <float> out;
for (unsigned i = 0; i != zsize; i += dim) {
vector <float> foo;
for (unsigned j = 0; j != dim; ++j) {
foo.push_back(z[i + j]);
}
float max_foo = *max_element(foo.begin(), foo.end());
for (unsigned j = 0; j != dim; ++j) {
foo[j] = exp(foo[j] - max_foo);
}
float sum_of_elems = 0.0;
for (unsigned j = 0; j != dim; ++j) {
sum_of_elems = sum_of_elems + foo[j];
}
for (unsigned j = 0; j != dim; ++j) {
out.push_back(foo[j]/sum_of_elems);
}
}
return out;
}
vector <float> sigmoid_d (const vector <float>& m1) {
/* Returns the value of the sigmoid function derivative f'(x) = f(x)(1 - f(x)),
where f(x) is sigmoid function.
Input: m1, a vector.
Output: x(1 - x) for every element of the input matrix m1.
*/
const unsigned long VECTOR_SIZE = m1.size();
vector <float> output (VECTOR_SIZE);
for( unsigned i = 0; i != VECTOR_SIZE; ++i ) {
output[ i ] = m1[ i ] * (1 - m1[ i ]);
}
return output;
}
vector <float> sigmoid (const vector <float>& m1) {
/* Returns the value of the sigmoid function f(x) = 1/(1 + e^-x).
Input: m1, a vector.
Output: 1/(1 + e^-x) for every element of the input matrix m1.
*/
const unsigned long VECTOR_SIZE = m1.size();
vector <float> output (VECTOR_SIZE);
for( unsigned i = 0; i != VECTOR_SIZE; ++i ) {
output[ i ] = 1 / (1 + exp(-m1[ i ]));
}
return output;
}
vector <float> operator+(const vector <float>& m1, const vector <float>& m2){
/* Returns the elementwise sum of two vectors.
Inputs:
m1: a vector
m2: a vector
Output: a vector, sum of the vectors m1 and m2.
*/
const unsigned long VECTOR_SIZE = m1.size();
vector <float> sum (VECTOR_SIZE);
for (unsigned i = 0; i != VECTOR_SIZE; ++i){
sum[i] = m1[i] + m2[i];
};
return sum;
}
vector <float> operator-(const vector <float>& m1, const vector <float>& m2){
/* Returns the difference between two vectors.
Inputs:
m1: vector
m2: vector
Output: vector, m1 - m2, difference between two vectors m1 and m2.
*/
const unsigned long VECTOR_SIZE = m1.size();
vector <float> difference (VECTOR_SIZE);
for (unsigned i = 0; i != VECTOR_SIZE; ++i){
difference[i] = m1[i] - m2[i];
};
return difference;
}
vector <float> operator*(const vector <float>& m1, const vector <float>& m2){
/* Returns the product of two vectors (elementwise multiplication).
Inputs:
m1: vector
m2: vector
Output: vector, m1 * m2, product of two vectors m1 and m2
*/
const unsigned long VECTOR_SIZE = m1.size();
vector <float> product (VECTOR_SIZE);
for (unsigned i = 0; i != VECTOR_SIZE; ++i){
product[i] = m1[i] * m2[i];
};
return product;
}
vector <float> operator*(const float m1, const vector <float>& m2){
/* Returns the product of a float and a vectors (elementwise multiplication).
Inputs:
m1: float
m2: vector
Output: vector, m1 * m2, product of two vectors m1 and m2
*/
const unsigned long VECTOR_SIZE = m2.size();
vector <float> product (VECTOR_SIZE);
for (unsigned i = 0; i != VECTOR_SIZE; ++i){
product[i] = m1 * m2[i];
};
return product;
}
vector <float> operator/(const vector <float>& m2, const float m1){
/* Returns the product of a float and a vectors (elementwise multiplication).
Inputs:
m1: float
m2: vector
Output: vector, m1 * m2, product of two vectors m1 and m2
*/
const unsigned long VECTOR_SIZE = m2.size();
vector <float> product (VECTOR_SIZE);
for (unsigned i = 0; i != VECTOR_SIZE; ++i){
product[i] = m2[i] / m1;
};
return product;
}
vector <float> transpose (float *m, const int C, const int R) {
/* Returns a transpose matrix of input matrix.
Inputs:
m: vector, input matrix
C: int, number of columns in the input matrix
R: int, number of rows in the input matrix
Output: vector, transpose matrix mT of input matrix m
*/
vector <float> mT (C*R);
for(unsigned n = 0; n != C*R; n++) {
unsigned i = n/C;
unsigned j = n%C;
mT[n] = m[R*j + i];
}
return mT;
}
vector <float> dot (const vector <float>& m1, const vector <float>& m2, const int m1_rows, const int m1_columns, const int m2_columns) {
/* Returns the product of two matrices: m1 x m2.
Inputs:
m1: vector, left matrix of size m1_rows x m1_columns
m2: vector, right matrix of size m1_columns x m2_columns (the number of rows in the right matrix
must be equal to the number of the columns in the left one)
m1_rows: int, number of rows in the left matrix m1
m1_columns: int, number of columns in the left matrix m1
m2_columns: int, number of columns in the right matrix m2
Output: vector, m1 * m2, product of two vectors m1 and m2, a matrix of size m1_rows x m2_columns
*/
vector <float> output (m1_rows*m2_columns);
for( int row = 0; row != m1_rows; ++row ) {
for( int col = 0; col != m2_columns; ++col ) {
output[ row * m2_columns + col ] = 0.f;
for( int k = 0; k != m1_columns; ++k ) {
output[ row * m2_columns + col ] += m1[ row * m1_columns + k ] * m2[ k * m2_columns + col ];
}
}
}
return output;
}
vector<string> split(const string &s, char delim) {
stringstream ss(s);
string item;
vector<string> tokens;
while (getline(ss, item, delim)) {
tokens.push_back(item);
}
return tokens;
}
int main(int argc, const char * argv[]) {
string line;
vector<string> line_v;
cout << "inference ...\n";
cout << "Loading data ...\n";
vector<float> X_test;
vector<float> y_test;
ifstream myfile ("train.txt");
if (myfile.is_open())
{
while ( getline (myfile,line) )
{
line_v = split(line, '\t');
int digit = strtof((line_v[0]).c_str(),0);
for (unsigned i = 0; i < 10; ++i) {
if (i == digit)
{
y_test.push_back(1.);
}
else y_test.push_back(0.);
}
int size = static_cast<int>(line_v.size());
for (unsigned i = 1; i < size; ++i) {
X_test.push_back(strtof((line_v[i]).c_str(),0));
}
}
X_test = X_test/255.0;
myfile.close();
}
else cout << "Unable to open file" << '\n';
int xsize = static_cast<int>(X_test.size());
int ysize = static_cast<int>(y_test.size());
int num_data = ysize / 10;
// Random initialization of the weights(乱数初期化)
//vector <float> W1 = random_vector(784*128);
//vector <float> W2 = random_vector(128*10);
//学習データをロードする場合
vector <float> W1;
vector <float> W2;
int tmp;
ifstream infile1("W1.txt");
if (infile1.is_open()){
while ( getline (infile1,line) ){
line_v = split(line, ' ');
//for (auto it:line_v) {
for (vector<string>::const_iterator it = line_v.begin(); it != line_v.end(); it++) {
//W1.emplace_back(stof(it));
W1.push_back(stof(*it));
}
}
}
infile1.close();
ifstream infile2("W2.txt");
if (infile2.is_open()){
while ( getline (infile2,line) ){
line_v = split(line, ' ');
//for (auto it:line_v) {
for (vector<string>::const_iterator it = line_v.begin(); it != line_v.end(); it++) {
//W2.emplace_back(stof(*it));
W2.push_back(stof(*it));
}
}
}
infile2.close();
//vector <float> W3 = random_vector(64*10);
int correct_cnt = 0;
for(int i=0;i<num_data;++i) {
vector<float> b_X;
vector<float> b_y;
for (unsigned j = i*784; j < (i+1)*784; ++j){
b_X.push_back(X_test[j]);
}
for (unsigned k = i*10; k < (i+1)*10; ++k){
b_y.push_back(y_test[k]);
}
vector<float> a1 = relu(dot( b_X, W1, 1, 784, 128 ));
//vector<float> a2 = relu(dot( a1, W2, 1, 128, 64 ));
vector<float> yhat = softmax(dot( a1, W2, 1, 128, 10 ), 10);
if(argmax(yhat) == argmax(b_y)) {
++correct_cnt;
}
}
cout<< "correct: " << correct_cnt <<" total: "<< num_data << endl;
cout<< "accuracy: "<< (double) correct_cnt / num_data <<endl;
}
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