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faster set/get
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5439e52abf
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0be1b07308
@ -60,8 +60,8 @@ class SGD {
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std::vector<float> y(yData_.begin() + startId * numClasses_,
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yData_.begin() + endId * numClasses_);
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xt.Load(x);
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yt.Load(y);
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xt.set(x);
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yt.set(y);
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}
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void UpdateModel() {
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@ -5,91 +5,12 @@ using namespace std;
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namespace marian {
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inline std::vector<std::string> Tokenize(const std::string& str,
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const std::string& delimiters = " \t")
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{
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std::vector<std::string> tokens;
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// Skip delimiters at beginning.
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std::string::size_type lastPos = str.find_first_not_of(delimiters, 0);
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// Find first "non-delimiter".
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std::string::size_type pos = str.find_first_of(delimiters, lastPos);
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while (std::string::npos != pos || std::string::npos != lastPos) {
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// Found a token, add it to the vector.
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tokens.push_back(str.substr(lastPos, pos - lastPos));
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// Skip delimiters. Note the "not_of"
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lastPos = str.find_first_not_of(delimiters, pos);
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// Find next "non-delimiter"
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pos = str.find_first_of(delimiters, lastPos);
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}
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return tokens;
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}
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//! convert string to variable of type T. Used to reading floats, int etc from files
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template<typename T>
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T Scan(const std::string &input)
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{
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std::stringstream stream(input);
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T ret;
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stream >> ret;
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return ret;
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}
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//! convert vectors of string to vectors of type T variables
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template<typename T>
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inline std::vector<T> Scan(const std::vector< std::string > &input)
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{
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std::vector<T> output(input.size());
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for (size_t i = 0 ; i < input.size() ; i++) {
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output[i] = Scan<T>( input[i] );
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}
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return output;
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}
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//! tokenise input string to vector of type T
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template<typename T>
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inline std::vector<T> Tokenize( const std::string &input
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, const std::string& delimiters = " \t")
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{
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std::vector<std::string> stringVector = Tokenize(input, delimiters);
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return Scan<T>( stringVector );
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}
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void Tensor::Load(const std::string &path)
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{
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size_t totSize = GetTotalSize(pimpl_->shape());
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cerr << "totSize=" << totSize << endl;
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std::vector<float> hostData(totSize);
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fstream strm;
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strm.open(path.c_str());
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string line;
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size_t ind = 0;
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while ( getline (strm, line) )
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{
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cerr << line << '\n';
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vector<Float> toks = Tokenize<Float>(line);
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for (size_t i = 0; i < toks.size(); ++i) {
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hostData[ind] = toks[i];
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}
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++ind;
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}
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strm.close();
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Load(hostData.begin(), hostData.begin());
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}
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void Tensor::Load(const std::vector<float>& data)
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void Tensor::set(const std::vector<float>& data)
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{
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pimpl_->set(data.begin(), data.end());
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}
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void Tensor::Load(const std::vector<float>::const_iterator &begin, const std::vector<float>::const_iterator &end)
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void Tensor::set(const std::vector<float>::const_iterator &begin, const std::vector<float>::const_iterator &end)
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{
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pimpl_->set(begin, end);
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}
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80
src/tensor.h
80
src/tensor.h
@ -12,30 +12,6 @@
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namespace marian {
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//struct Handles {
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// //cudnnHandle_t cudnnHandle;
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// //cublasHandle_t cublasHandle;
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//
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// //cudnnOpTensorDescriptor_t add;
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//
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// Handles() {
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// cudnnCreate(&cudnnHandle);
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// cublasCreate(&cublasHandle);
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// cudnnCreateOpTensorDescriptor(&add);
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// cudnnSetOpTensorDescriptor(add, CUDNN_OP_TENSOR_ADD, CUDNN_DATA_FLOAT, CUDNN_NOT_PROPAGATE_NAN);
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// }
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//
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// ~Handles() {
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// cudnnDestroy(cudnnHandle);
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// cublasDestroy(cublasHandle);
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// cudnnDestroyOpTensorDescriptor(add);
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// }
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//};
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//
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//const Handles handles;
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// typedef std::vector<int> Shape;
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inline std::string Debug(const Shape &shape)
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{
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std::stringstream strm;
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@ -59,18 +35,9 @@ class TensorImpl {
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private:
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Shape shape_;
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thrust::device_vector<Float> data_;
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//cudnnTensorDescriptor_t desc_;
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size_t tno_;
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static size_t tensorCounter;
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//cudnnDataType_t dataType() {
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// switch(sizeof(Float)) {
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// case 2: return CUDNN_DATA_HALF;
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// case 8: return CUDNN_DATA_DOUBLE;
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// default: return CUDNN_DATA_FLOAT;
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// }
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//}
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public:
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typedef Float value_type;
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@ -89,30 +56,11 @@ class TensorImpl {
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int size = GetTotalSize(shape_);
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data_.resize(size, value);
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//cudnnCreateTensorDescriptor(&desc_);
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//switch (shape_.size()) {
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// case 1:
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// cudnnSetTensor4dDescriptor(desc_, CUDNN_TENSOR_NCHW, dataType(),
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// shape_[0], 1, 1, 1); break;
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// case 2:
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// cudnnSetTensor4dDescriptor(desc_, CUDNN_TENSOR_NCHW, dataType(),
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// shape_[0], shape_[1], 1, 1); break;
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// case 3:
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// cudnnSetTensor4dDescriptor(desc_, CUDNN_TENSOR_NCHW, dataType(),
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// shape_[0], shape_[1], shape_[2], 1); break;
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// case 4:
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// cudnnSetTensor4dDescriptor(desc_, CUDNN_TENSOR_NCHW, dataType(),
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// shape_[0], shape_[1], shape_[2], shape_[3]); break;
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//}
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}
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TensorImpl(const TensorImpl&) = delete;
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TensorImpl(TensorImpl&&) = delete;
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~TensorImpl() {
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//cudnnDestroyTensorDescriptor(desc_);
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}
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value_type operator[](size_t i) const {
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return data_[i];
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}
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@ -145,10 +93,6 @@ class TensorImpl {
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return thrust::raw_pointer_cast(data_.data());
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}
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//cudnnTensorDescriptor_t desc() const {
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// return desc_;
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//}
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size_t id() const {
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return tno_;
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}
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@ -158,12 +102,13 @@ class TensorImpl {
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}
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void set(const std::vector<float>::const_iterator &begin, const std::vector<float>::const_iterator &end) {
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size_t totSize = GetTotalSize(shape());
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//std::cerr << "tensor size=" << totSize << " vector size=" << values.size() << std::endl;
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//assert(totSize == values.size());
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thrust::copy(begin, end, data_.begin());
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}
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void get(std::vector<float>::iterator out) {
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thrust::copy(data_.begin(), data_.end(), out);
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}
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std::string Debug() const
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{
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std::stringstream strm;
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@ -245,10 +190,6 @@ class Tensor {
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return pimpl_->shape();
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}
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//cudnnTensorDescriptor_t desc() const {
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// return pimpl_->desc();
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//}
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void set(value_type value) {
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pimpl_->set(value);
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}
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@ -273,10 +214,17 @@ class Tensor {
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std::cerr << std::endl;
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}
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void Load(const std::string &path);
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void Load(const std::vector<float>& data);
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void Load(const std::vector<float>::const_iterator &begin, const std::vector<float>::const_iterator &end);
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//void Load(const std::string &path);
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void set(const std::vector<float>& data);
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void set(const std::vector<float>::const_iterator &begin, const std::vector<float>::const_iterator &end);
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void get(std::vector<float>::iterator out) {
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pimpl_->get(out);
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}
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void get(std::vector<float> &vout) {
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pimpl_->get(vout.begin());
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}
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};
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}
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@ -31,21 +31,21 @@ int main(int argc, char** argv) {
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converter.Load("bias", bData, bShape);
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auto initW = [wData](Tensor t) {
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thrust::copy(wData.begin(), wData.end(), t.begin());
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t.set(wData.begin(), wData.end());
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};
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auto initB = [bData](Tensor t) {
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thrust::copy(bData.begin(), bData.end(), t.begin());
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t.set(bData.begin(), bData.end());
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};
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std::cerr << "\tDone." << std::endl;
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Expr x = input(shape={whatevs, IMAGE_SIZE}, name="X");
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Expr y = input(shape={whatevs, LABEL_SIZE}, name="Y");
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auto x = input(shape={whatevs, IMAGE_SIZE}, name="X");
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auto y = input(shape={whatevs, LABEL_SIZE}, name="Y");
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Expr w = param(shape={IMAGE_SIZE, LABEL_SIZE}, name="W0", init=initW);
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Expr b = param(shape={1, LABEL_SIZE}, name="b0", init=initB);
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auto w = param(shape={IMAGE_SIZE, LABEL_SIZE}, name="W0", init=initW);
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auto b = param(shape={1, LABEL_SIZE}, name="b0", init=initB);
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std::cerr << "Building model...";
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auto predict = softmax(dot(x, w) + b,
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@ -53,13 +53,13 @@ int main(int argc, char** argv) {
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auto graph = -mean(sum(y * log(predict), axis=1),
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axis=0, name="cost");
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std::cerr << "\tDone." << std::endl;
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std::cerr << "Done." << std::endl;
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Tensor xt({numofdata, IMAGE_SIZE});
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xt.Load(testImages);
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xt.set(testImages);
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Tensor yt({numofdata, LABEL_SIZE});
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yt.Load(testLabels);
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yt.set(testLabels);
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x = xt;
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y = yt;
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@ -68,6 +68,9 @@ int main(int argc, char** argv) {
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auto results = predict.val();
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graph.backward();
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std::vector<float> resultsv(results.size());
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results.get(resultsv);
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std::cerr << b.grad().Debug() << std::endl;
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size_t acc = 0;
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@ -76,14 +79,14 @@ int main(int argc, char** argv) {
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size_t predicted = 0;
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for (size_t j = 0; j < LABEL_SIZE; ++j) {
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if (testLabels[i+j]) correct = j;
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if (results[i + j] > results[i + predicted]) predicted = j;
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if (resultsv[i + j] > resultsv[i + predicted]) predicted = j;
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}
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acc += (correct == predicted);
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//std::cerr << "corect: " << correct << " | " << predicted << "(";
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//std::cerr << correct << " | " << predicted << " ( ";
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//for (size_t j = 0; j < LABEL_SIZE; ++j) {
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// std::cerr << results[i+j] << " ";
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// std::cerr << resultsv[i+j] << " ";
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//}
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//std::cerr << std::endl;
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//std::cerr << ")" << std::endl;
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}
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std::cerr << "ACC: " << float(acc)/numofdata << std::endl;
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