marian-nmt/marian
1
1
Fork 0
mirror of https://github.com/marian-nmt/marian.git synced 2024-11-04 14:04:24 +03:00
Code Issues Packages Projects Releases Wiki Activity

UnaryNodeOp inherits shape from child by default

Browse Source
This commit is contained in:
Marcin Junczys-Dowmunt 2016-09-15 08:15:46 +02:00
parent 4bafa6c360
commit 3ccdc15263
3 changed files with 27 additions and 31 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

12
src/graph_operators.h
View File

@ -71,7 +71,9 @@ struct UnaryNodeOp : public Node {
template <typename ...Args>
UnaryNodeOp(ChainPtr a, Args ...args)
: Node(args...), a_(a) {}
: Node(keywords::shape=a->shape(), //@TODO: Check keywords?
args...),
a_(a) {}
};
struct SigmoidNodeOp : public UnaryNodeOp {
@ -142,8 +144,7 @@ struct ArgmaxOp : public UnaryNodeOp {
struct SoftmaxNodeOp : public UnaryNodeOp {
template <typename ...Args>
SoftmaxNodeOp(ChainPtr a, Args ...args)
: UnaryNodeOp(a, keywords::shape=a->shape(),
args...) { }
: UnaryNodeOp(a, args...) { }
void forward() {
// B = softmax(A).
@ -166,7 +167,7 @@ struct SoftmaxNodeOp : public UnaryNodeOp {
struct LogNodeOp : public UnaryNodeOp {
template <typename ...Args>
LogNodeOp(ChainPtr a, Args ...args)
: UnaryNodeOp(a, keywords::shape=a->shape(), args...) {}
: UnaryNodeOp(a, args...) {}
void forward() {
Element(_1 = Log(_2), val_, a_->val());
@ -181,8 +182,7 @@ struct LogNodeOp : public UnaryNodeOp {
struct ExpNodeOp : public UnaryNodeOp {
template <typename ...Args>
ExpNodeOp(ChainPtr a, Args ...args)
: UnaryNodeOp(a, keywords::shape=a->shape(),
args...) { }
: UnaryNodeOp(a, args...) { }
void forward() {
Element(_1 = Exp(_2), val_, a_->val());

2
src/param_initializers.h
View File

@ -22,7 +22,7 @@ void ones(Tensor t) {
void randreal(Tensor t) {
std::random_device device;
std::default_random_engine engine(device());
std::uniform_real_distribution<> dist(0, 1);
std::uniform_real_distribution<> dist(0, 0.1);
auto gen = std::bind(dist, engine);
std::vector<float> vals(t.size());

42
src/validate_mnist.cu
View File

@ -2,6 +2,7 @@
#include "marian.h"
#include "mnist.h"
#include "npz_converter.h"
#include "param_initializers.h"
using namespace marian;
using namespace keywords;
@ -39,12 +40,8 @@ int main(int argc, char** argv) {
auto b = param(shape={1, LABEL_SIZE},
init=[bData](Tensor t) { t.set(bData); });
auto zd = dot(x, w);
auto z = zd + b;
auto predict = softmax(z, axis=1);
auto logp = log(predict);
auto cost = sum(y * logp, axis=1);
auto graph = -mean(cost, axis=0);
auto predict = softmax(dot(x, w) + b, axis=1);
auto graph = -mean(sum(y * log(predict), axis=1), axis=0);
std::cerr << "Done." << std::endl;
@ -56,34 +53,33 @@ int main(int argc, char** argv) {
graph.forward(BATCH_SIZE);
for (size_t j = 0; j < 10; ++j) {
float eta = 0.1;
for (size_t j = 0; j < 100; ++j) {
for(size_t i = 0; i < 60; ++i) {
graph.backward();
auto update_rule = _1 -= 0.1 * _2;
auto update_rule = _1 -= eta * _2;
Element(update_rule, w.val(), w.grad());
Element(update_rule, b.val(), b.grad());
graph.forward(BATCH_SIZE);
}
std::cerr << "Epoch: " << j << std::endl;
}
auto results = predict.val();
std::vector<float> resultsv(results.size());
resultsv << results;
auto results = predict.val();
std::vector<float> resultsv(results.size());
resultsv << results;
size_t acc = 0;
for (size_t i = 0; i < testLabels.size(); i += LABEL_SIZE) {
size_t correct = 0;
size_t predicted = 0;
for (size_t j = 0; j < LABEL_SIZE; ++j) {
if (testLabels[i+j]) correct = j;
if (resultsv[i + j] > resultsv[i + predicted]) predicted = j;
size_t acc = 0;
for (size_t i = 0; i < testLabels.size(); i += LABEL_SIZE) {
size_t correct = 0;
size_t predicted = 0;
for (size_t j = 0; j < LABEL_SIZE; ++j) {
if (testLabels[i+j]) correct = j;
if (resultsv[i + j] > resultsv[i + predicted]) predicted = j;
}
acc += (correct == predicted);
}
acc += (correct == predicted);
std::cerr << "Accuracy: " << float(acc) / BATCH_SIZE << std::endl;
}
std::cerr << "Accuracy: " << float(acc) / BATCH_SIZE << std::endl;
return 0;
}