コード例 #1
0
ファイル: mnist.hpp プロジェクト: deeplearningais/cuvnet
        mnist_dataset(const std::string& path, const bool out_of_n_coding = true){
            std::cout << "Reading MNIST dataset..."<<std::flush;
            std::ifstream ftraind((path + "/train-images.idx3-ubyte").c_str(),std::ios::in | std::ios::binary); // image data
            std::ifstream ftrainl((path + "/train-labels.idx1-ubyte").c_str(),std::ios::in | std::ios::binary); // label data
            std::ifstream ftestd ((path + "/t10k-images.idx3-ubyte").c_str(),std::ios::in | std::ios::binary); // image data
            std::ifstream ftestl ((path + "/t10k-labels.idx1-ubyte").c_str(),std::ios::in | std::ios::binary); // label data
            assert(ftraind.is_open());
            assert(ftrainl.is_open());
            assert(ftestd.is_open());
            assert(ftestl.is_open());

            char buf[16];
            ftraind.read(buf,16);
            ftrainl.read(buf, 8);
            ftestd.read(buf,16);
            ftestl.read(buf, 8);
            cuv::tensor<unsigned char,cuv::host_memory_space> traind(cuv::extents[60000][784]);
            cuv::tensor<unsigned char,cuv::host_memory_space> trainl(cuv::extents[60000]);
            cuv::tensor<unsigned char,cuv::host_memory_space> testd(cuv::extents[10000][784]);
            cuv::tensor<unsigned char,cuv::host_memory_space> testl(cuv::extents[10000]);
            ftraind.read((char*)traind.ptr(), traind.size()); assert(ftraind.good());
            ftrainl.read((char*)trainl.ptr(), trainl.size()); assert(ftrainl.good());
            ftestd.read((char*)testd.ptr(), testd.size());    assert(ftestd.good());
            ftestl.read((char*)testl.ptr(), testl.size());    assert(ftestl.good());

            train_data.resize(traind.shape());
            test_data.resize(testd.shape());
            convert(train_data , traind); // convert data type
            convert(test_data  , testd); // convert data type

            if (out_of_n_coding){
                train_labels.resize(cuv::extents[60000][10]);
                test_labels.resize(cuv::extents[10000][10]);
                train_labels = 0.f;
                test_labels = 0.f;
                for (unsigned int i = 0; i < trainl.size(); ++i){
                    train_labels(i, trainl[i]) = 1.f;
                }
                for (unsigned int i = 0; i < testl.size(); ++i){
                    test_labels(i, testl[i]) = 1.f;
                }
            } else {
                train_labels.resize(cuv::extents[60000]);
                test_labels.resize(cuv::extents[10000]);
                for (unsigned int i = 0; i < trainl.size(); ++i){
                    train_labels(i) = trainl[i];
                }
                for (unsigned int i = 0; i < testl.size(); ++i){
                    test_labels(i) = testl[i];
                }
            }

            //train_data = train_data[cuv::indices[cuv::index_range(0,5000)][cuv::index_range()]];
            //train_labels = train_labels[cuv::indices[cuv::index_range(0,5000)][cuv::index_range()]];

            binary = true;
            channels = 1;
            image_size = 28;
            std::cout << "done."<<std::endl;
        }
コード例 #2
0
ファイル: MnistDataSet.cpp プロジェクト: cvejoski/CUDALab
void MnistDataSet<M>::read() {

	std::string path = "/home/local/datasets/MNIST";
	ifstream ftraind((path + "/train-images.idx3-ubyte").c_str());
	ifstream ftrainl((path + "/train-labels.idx1-ubyte").c_str());
	ifstream ftestd ((path + "/t10k-images.idx3-ubyte").c_str());
	ifstream ftestl ((path + "/t10k-labels.idx1-ubyte").c_str());

	char buf[16];
	ftraind.read(buf,16); ftrainl.read(buf, 8);
	ftestd.read(buf,16); ftestl.read(buf, 8);
	tensor<unsigned char, host_memory_space> traind(extents[n_trainData][n_dim]);
	tensor<unsigned char, host_memory_space> trainl(extents[n_trainData]);
	tensor<unsigned char, host_memory_space> testd(extents[n_testData][n_dim]);
	tensor<unsigned char, host_memory_space> testl(extents[n_testData]);
	ftraind.read((char*)traind.ptr(), traind.size());
	assert(ftraind.good());
	ftrainl.read((char*)trainl.ptr(), trainl.size());
	assert(ftrainl.good());
	ftestd.read((char*)testd.ptr(), testd.size());
	assert(ftestd.good());
	ftestl.read((char*)testl.ptr(), testl.size());
	assert(ftestl.good());

	tensor<unsigned char, M> train_d(extents[n_trainData][n_dim]);
	tensor<unsigned char, M> train_l(extents[n_trainData]);
	tensor<unsigned char, M> test_d(extents[n_testData][n_dim]);
	tensor<unsigned char, M> test_l(extents[n_testData]);

	train_d = traind;
	train_l = trainl;
	test_d = testd;
	test_l = testl;

	// conversion to float:
	convert(this->X_train, train_d);
	convert(this->Y_train, train_l);
	convert(this->X_test, test_d);
	convert(this->Y_test, test_l);

}