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.gitignore
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.gitignore
vendored
@ -39,3 +39,4 @@ build
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# Examples
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examples/*/*.gz
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examples/mnist/*ubyte
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@ -2,10 +2,13 @@
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all: download
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download: train-images-idx3-ubyte.gz train-labels-idx1-ubyte.gz t10k-images-idx3-ubyte.gz t10k-labels-idx3-ubyte.gz
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download: train-images-idx3-ubyte train-labels-idx1-ubyte t10k-images-idx3-ubyte t10k-labels-idx1-ubyte
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%.gz:
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wget http://yann.lecun.com/exdb/mnist/$*.gz -O $@
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%-ubyte: %-ubyte.gz
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gzip -d < $^ > $@
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%-ubyte.gz:
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wget http://yann.lecun.com/exdb/mnist/$*-ubyte.gz -O $@
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clean:
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rm -f *.gz
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rm -f *.gz *-ubyte
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@ -134,6 +134,7 @@ inline Expr sum(Expr a, Args ...args) {
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else if(ax == 1) {
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auto lshape = [n]() -> Shape {
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int cols = n->val().shape()[1];
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//std::cerr << "Shape will be " << cols << " by 1." << std::endl;
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return {cols, 1};
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};
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Expr one = ones(shape={n->shape()[1], 1},
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@ -153,6 +154,20 @@ inline Expr sum(Expr a, Args ...args) {
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template <typename ...Args>
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inline Expr softmax(Expr a, Args ...args) {
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Expr e = exp(a);
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#if 0
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ChainPtr n = a.node();
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auto print_shape = [n]() -> Shape {
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std::cerr << "Shape: ";
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for (auto val : n->val().shape()) {
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std::cerr << val << " ";
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}
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std::cerr << std::endl;
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return {1,1};
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};
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using namespace keywords;
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Expr one = ones(shape={1, 1}, lazy_shape=print_shape);
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#endif
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return e / sum(e, args...);
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}
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@ -187,4 +202,4 @@ inline Expr mean(Expr a, Args ...args) {
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}
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}
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}
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}
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@ -118,9 +118,15 @@ struct LogNodeOp : public UnaryNodeOp {
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struct ExpNodeOp : public UnaryNodeOp {
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template <typename ...Args>
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ExpNodeOp(Args ...args)
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: UnaryNodeOp(args...) { }
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ExpNodeOp(ChainPtr a, Args ...args)
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: UnaryNodeOp(a, keywords::shape=newShape(a),
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args...) { }
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Shape newShape(ChainPtr a) {
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Shape shape = a->shape();
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return shape;
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}
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void forward() {
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Element(_1 = Exp(_2), val_, a_->val());
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}
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@ -289,4 +295,4 @@ struct DivNodeOp : public BroadcastingNodeOp {
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}
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};
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}
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}
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94
src/mnist.h
Normal file
94
src/mnist.h
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@ -0,0 +1,94 @@
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#pragma once
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#include <string>
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#include <iostream>
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#include <fstream>
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#include <vector>
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namespace datasets {
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namespace mnist {
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typedef unsigned char uchar;
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auto reverseInt = [](int i) {
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unsigned char c1, c2, c3, c4;
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c1 = i & 255, c2 = (i >> 8) & 255, c3 = (i >> 16) & 255, c4 = (i >> 24) & 255;
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return ((int)c1 << 24) + ((int)c2 << 16) + ((int)c3 << 8) + c4;
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};
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std::vector<std::vector<float>> ReadImages(const std::string& full_path) {
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std::ifstream file(full_path);
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if (! file.is_open())
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throw std::runtime_error("Cannot open file `" + full_path + "`!");
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int magic_number = 0, n_rows = 0, n_cols = 0;
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file.read((char *)&magic_number, sizeof(magic_number));
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magic_number = reverseInt(magic_number);
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if (magic_number != 2051)
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throw std::runtime_error("Invalid MNIST image file!");
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int number_of_images = 0;
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file.read((char *)&number_of_images, sizeof(number_of_images)), number_of_images = reverseInt(number_of_images);
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file.read((char *)&n_rows, sizeof(n_rows)), n_rows = reverseInt(n_rows);
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file.read((char *)&n_cols, sizeof(n_cols)), n_cols = reverseInt(n_cols);
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int image_size = n_rows * n_cols;
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std::vector<std::vector<float>> _dataset(number_of_images, std::vector<float>(image_size));
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unsigned char pixel = 0;
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for (int i = 0; i < number_of_images; i++) {
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for (int j = 0; j < image_size; j++) {
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file.read((char*)&pixel, sizeof(pixel));
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_dataset[i][j] = pixel / 255.0f;
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}
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}
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return _dataset;
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}
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std::vector<int> ReadLabels(const std::string& full_path) {
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std::ifstream file(full_path);
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if (! file.is_open())
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throw std::runtime_error("Cannot open file `" + full_path + "`!");
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int magic_number = 0;
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file.read((char *)&magic_number, sizeof(magic_number));
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magic_number = reverseInt(magic_number);
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if (magic_number != 2049)
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throw std::runtime_error("Invalid MNIST label file!");
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int number_of_labels = 0;
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file.read((char *)&number_of_labels, sizeof(number_of_labels)), number_of_labels = reverseInt(number_of_labels);
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std::vector<int> _dataset(number_of_labels);
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for (int i = 0; i < number_of_labels; i++) {
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file.read((char*)&_dataset[i], 1);
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}
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return _dataset;
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}
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} // namespace mnist
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} // namespace datasets
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//int main(int argc, const char *argv[]) {
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//auto images = datasets::mnist::ReadImages("t10k-images-idx3-ubyte");
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//auto labels = datasets::mnist::ReadLabels("t10k-labels-idx1-ubyte");
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//std::cout
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//<< "Number of images: " << images.size() << std::endl
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//<< "Image size: " << images[0].size() << std::endl;
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//for (int i = 0; i < 3; i++) {
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//for (int j = 0; j < images[i].size(); j++) {
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//std::cout << images[i][j] << ",";
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//}
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//std::cout << " label=" << (int)labels[i] << std::endl;
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//}
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//return 0;
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//}
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94
src/test.cu
94
src/test.cu
@ -1,26 +1,28 @@
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#include "marian.h"
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#include "mnist.h"
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using namespace std;
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int main(int argc, char** argv) {
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/*auto images = datasets::mnist::ReadImages("../examples/mnist/t10k-images-idx3-ubyte");*/
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/*auto labels = datasets::mnist::ReadLabels("../examples/mnist/t10k-labels-idx1-ubyte");*/
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/*std::cerr << images.size() << " " << images[0].size() << std::endl;*/
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using namespace marian;
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using namespace keywords;
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/*
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Expr x = input(shape={whatevs, 784}, name="X");
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Expr y = input(shape={whatevs, 10}, name="Y");
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Expr w = param(shape={784, 10}, name="W0");
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Expr b = param(shape={1, 10}, name="b0");
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Expr n5 = dot(x, w);
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Expr n6 = n5 + b;
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Expr lr = softmax(n6, axis=1, name="pred");
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auto scores = dot(x, w) + b;
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auto lr = softmax(scores, axis=1, name="pred");
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auto graph = -mean(sum(y * log(lr), axis=1), axis=0, name="cost");
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cerr << "lr=" << lr.Debug() << endl;
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Expr graph = -mean(sum(y * log(lr), axis=1), axis=0, name="cost");
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Tensor tx({500, 784}, 1);
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Tensor ty({500, 10}, 1);
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@ -31,51 +33,47 @@ int main(int argc, char** argv) {
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y = ty;
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graph.forward(500);
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std::cerr << "Result: ";
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for (auto val : scores.val().shape()) {
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std::cerr << val << " ";
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}
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std::cerr << std::endl;
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std::cerr << "Result: ";
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for (auto val : lr.val().shape()) {
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std::cerr << val << " ";
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}
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std::cerr << std::endl;
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std::cerr << "Log-likelihood: ";
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for (auto val : graph.val().shape()) {
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std::cerr << val << " ";
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}
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std::cerr << std::endl;
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graph.backward();
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//std::cerr << graph["pred"].val()[0] << std::endl;
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*/
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Expr x = input(shape={whatevs, 2}, name="X");
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Expr y = input(shape={whatevs, 2}, name="Y");
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Expr w = param(shape={2, 1}, name="W0");
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Expr b = param(shape={1, 1}, name="b0");
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Expr n5 = dot(x, w);
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Expr n6 = n5 + b;
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Expr lr = softmax(n6, axis=1, name="pred");
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cerr << "lr=" << lr.Debug() << endl;
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Expr graph = -mean(sum(y * log(lr), axis=1), axis=0, name="cost");
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Tensor tx({4, 2}, 1);
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Tensor ty({4, 1}, 1);
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cerr << "tx=" << tx.Debug() << endl;
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cerr << "ty=" << ty.Debug() << endl;
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tx.Load("../examples/xor/train.txt");
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ty.Load("../examples/xor/label.txt");
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//hook0(graph);
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//graph.autodiff();
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//std::cerr << graph["cost"].val()[0] << std::endl;
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#if 0
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hook0(graph);
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graph.autodiff();
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std::cerr << graph["cost"].val()[0] << std::endl;
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//hook1(graph);
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//for(auto p : graph.params()) {
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// auto update = _1 = _1 - alpha * _2;
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// Element(update, p.val(), p.grad());
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//}
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//hook2(graph);
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//
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//auto opt = adadelta(cost_function=cost,
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// eta=0.9, gamma=0.1,
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// set_batch=set,
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// before_update=before,
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// after_update=after,
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// set_valid=valid,
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// validation_freq=100,
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// verbose=1, epochs=3, early_stopping=10);
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//opt.run();
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for(auto p : graph.params()) {
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auto update = _1 = _1 - alpha * _2;
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Element(update, p.val(), p.grad());
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}
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hook2(graph);
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auto opt = adadelta(cost_function=cost,
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eta=0.9, gamma=0.1,
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set_batch=set,
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before_update=before,
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after_update=after,
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set_valid=valid,
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validation_freq=100,
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verbose=1, epochs=3, early_stopping=10);
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opt.run();
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#endif
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return 0;
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}
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