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https://github.com/marian-nmt/marian.git
synced 2024-09-17 09:47:34 +03:00
Replaced softmax with fast softmax in MNIST.
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parent
6d3f67e955
commit
61d8b3cb83
142
src/test.cu
142
src/test.cu
@ -9,6 +9,7 @@ int main(int argc, char** argv) {
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/*auto images = datasets::mnist::ReadImages("../examples/mnist/t10k-images-idx3-ubyte", numImg);*/
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/*auto labels = datasets::mnist::ReadLabels("../examples/mnist/t10k-labels-idx1-ubyte", numImg);*/
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#if 1
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using namespace marian;
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using namespace keywords;
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@ -41,75 +42,80 @@ int main(int argc, char** argv) {
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std::cerr << graph.val().Debug() << std::endl;
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std::cerr << w.grad().Debug() << std::endl;
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//std::cerr << b.grad().Debug() << std::endl;
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#else
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// using namespace marian;
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// using namespace keywords;
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//
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// const size_t BATCH_SIZE = 500;
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// const size_t IMAGE_SIZE = 784;
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// const size_t LABEL_SIZE = 10;
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//
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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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//
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// Expr w = param(shape={IMAGE_SIZE, LABEL_SIZE}, name="W0");
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// Expr b = param(shape={1, LABEL_SIZE}, name="b0");
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//
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// Expr z = dot(x, w) + b;
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// Expr lr = softmax(z, axis=1, name="pred");
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// Expr graph = -mean(sum(y * log(lr), axis=1), axis=0, name="cost");
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// //cerr << "x=" << Debug(lr.val().shape()) << endl;
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//
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// int numofdata;
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// //vector<float> images = datasets::mnist::ReadImages("../examples/mnist/t10k-images-idx3-ubyte", numofdata, IMAGE_SIZE);
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// //vector<float> labels = datasets::mnist::ReadLabels("../examples/mnist/t10k-labels-idx1-ubyte", numofdata, LABEL_SIZE);
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// vector<float> images = datasets::mnist::ReadImages("../examples/mnist/train-images-idx3-ubyte", numofdata, IMAGE_SIZE);
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// vector<float> labels = datasets::mnist::ReadLabels("../examples/mnist/train-labels-idx1-ubyte", numofdata, LABEL_SIZE);
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// cerr << "images=" << images.size() << " labels=" << labels.size() << endl;
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// cerr << "numofdata=" << numofdata << endl;
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//
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// size_t startInd = 0;
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// size_t startIndData = 0;
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// while (startInd < numofdata) {
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// size_t batchSize = (startInd + BATCH_SIZE < numofdata) ? BATCH_SIZE : numofdata - startInd;
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// cerr << "startInd=" << startInd
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// << " startIndData=" << startIndData
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// << " batchSize=" << batchSize << endl;
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//
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// Tensor tx({numofdata, IMAGE_SIZE}, 1);
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// Tensor ty({numofdata, LABEL_SIZE}, 1);
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//
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// tx.Load(images.begin() + startIndData, images.begin() + startIndData + batchSize * IMAGE_SIZE);
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// ty.Load(labels.begin() + startInd, labels.begin() + startInd + batchSize);
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//
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// //cerr << "tx=" << Debug(tx.shape()) << endl;
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// //cerr << "ty=" << Debug(ty.shape()) << endl;
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//
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// x = tx;
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// y = ty;
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//
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// cerr << "x=" << Debug(x.val().shape()) << endl;
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// cerr << "y=" << Debug(y.val().shape()) << endl;
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//
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//
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// graph.forward(batchSize);
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//
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// cerr << "w=" << Debug(w.val().shape()) << endl;
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// cerr << "b=" << Debug(b.val().shape()) << endl;
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// std::cerr << "z: " << Debug(z.val().shape()) << endl;
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// std::cerr << "lr: " << Debug(lr.val().shape()) << endl;
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// std::cerr << "Log-likelihood: " << Debug(graph.val().shape()) << endl ;
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//
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// //std::cerr << "scores=" << scores.val().Debug() << endl;
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// //std::cerr << "lr=" << lr.val().Debug() << endl;
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//
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// //graph.backward();
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//
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// //std::cerr << graph["pred"].val()[0] << std::endl;
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//
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// startInd += batchSize;
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// startIndData += batchSize * IMAGE_SIZE;
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// }
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using namespace marian;
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using namespace keywords;
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using namespace std;
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const size_t BATCH_SIZE = 500;
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const size_t IMAGE_SIZE = 784;
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const size_t LABEL_SIZE = 10;
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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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Expr w = param(shape={IMAGE_SIZE, LABEL_SIZE}, name="W0");
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Expr b = param(shape={1, LABEL_SIZE}, name="b0");
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Expr z = dot(x, w) + b;
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Expr lr = softmax(z, axis=1, name="pred");
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Expr graph = -mean(sum(y * log(lr), axis=1), axis=0, name="cost");
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//cerr << "x=" << Debug(lr.val().shape()) << endl;
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int numofdata;
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//vector<float> images = datasets::mnist::ReadImages("../examples/mnist/t10k-images-idx3-ubyte", numofdata, IMAGE_SIZE);
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//vector<float> labels = datasets::mnist::ReadLabels("../examples/mnist/t10k-labels-idx1-ubyte", numofdata, LABEL_SIZE);
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vector<float> images = datasets::mnist::ReadImages("../examples/mnist/train-images-idx3-ubyte", numofdata, IMAGE_SIZE);
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vector<float> labels = datasets::mnist::ReadLabels("../examples/mnist/train-labels-idx1-ubyte", numofdata, LABEL_SIZE);
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cerr << "images=" << images.size() << " labels=" << labels.size() << endl;
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cerr << "numofdata=" << numofdata << endl;
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size_t startInd = 0;
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size_t startIndData = 0;
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while (startInd < numofdata) {
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size_t batchSize = (startInd + BATCH_SIZE < numofdata) ? BATCH_SIZE : numofdata - startInd;
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cerr << "startInd=" << startInd
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<< " startIndData=" << startIndData
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<< " batchSize=" << batchSize << endl;
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Tensor tx({numofdata, IMAGE_SIZE}, 1);
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Tensor ty({numofdata, LABEL_SIZE}, 1);
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tx.set(images.begin() + startIndData, images.begin() + startIndData + batchSize * IMAGE_SIZE);
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ty.set(labels.begin() + startInd, labels.begin() + startInd + batchSize);
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//cerr << "tx=" << Debug(tx.shape()) << endl;
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//cerr << "ty=" << Debug(ty.shape()) << endl;
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x = tx;
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y = ty;
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cerr << "x=" << Debug(x.val().shape()) << endl;
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cerr << "y=" << Debug(y.val().shape()) << endl;
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graph.forward(batchSize);
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cerr << "w=" << Debug(w.val().shape()) << endl;
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cerr << "b=" << Debug(b.val().shape()) << endl;
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std::cerr << "z: " << Debug(z.val().shape()) << endl;
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std::cerr << "lr: " << Debug(lr.val().shape()) << endl;
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std::cerr << "Log-likelihood: " << graph.val().Debug() << endl ;
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//std::cerr << "scores=" << scores.val().Debug() << endl;
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//std::cerr << "lr=" << lr.val().Debug() << endl;
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graph.backward();
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std::cerr << w.grad().Debug() << std::endl;
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//std::cerr << graph["pred"].val()[0] << std::endl;
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startInd += batchSize;
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startIndData += batchSize * IMAGE_SIZE;
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}
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#endif
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return 0;
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}
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@ -39,7 +39,7 @@ int main(int argc, char** argv) {
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auto b = param(shape={1, LABEL_SIZE},
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init=from_vector(bData));
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auto probs = softmax(dot(x, w) + b, axis=1);
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auto probs = softmax_fast(dot(x, w) + b, axis=1);
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auto cost = -mean(sum(y * log(probs), axis=1), axis=0);
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std::cerr << "Done." << std::endl;
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