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Added mini-batch training to the encoder-decoder benchmark.

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This commit is contained in:
Andre Martins 2016-09-21 12:21:14 +01:00
parent 828a0db8bc
commit 26ffc8644e
1 changed files with 63 additions and 2 deletions
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65
src/validate_encoder_decoder.cu
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@ -19,6 +19,9 @@
// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
// SOFTWARE.
#include <chrono>
#include <boost/timer/timer.hpp>
#include "marian.h"
#include "mnist.h"
#include "vocab.h"
@ -125,7 +128,7 @@ int main(int argc, char** argv) {
// Right now we're only reading the first few sentence pairs, and defining
// that as the step size.
int batch_size = 64;
int batch_size = 20;
int num_source_tokens = -1;
int num_target_tokens = -1;
std::vector<std::vector<size_t> > source_sentences, target_sentences;
@ -142,7 +145,7 @@ int main(int argc, char** argv) {
if (num_target_tokens < 0 || target_ids.size() > num_target_tokens) {
num_target_tokens = target_ids.size();
}
if (source_sentences.size() == batch_size) break;
if (source_sentences.size() == 1000) break;
}
std::cerr << "Done." << std::endl;
std::cerr << source_sentences.size()
@ -214,6 +217,63 @@ int main(int argc, char** argv) {
std::cout << g.graphviz() << std::endl;
std::cerr << "Training..." << std::endl;
int num_training_examples = source_sentences.size();
std::cerr << num_training_examples << " training examples." << std::endl;
boost::timer::cpu_timer total;
Adam opt;
int num_epochs = 20;
for(int epoch = 1; epoch <= num_epochs; ++epoch) {
boost::timer::cpu_timer timer;
// TODO: shuffle the data.
// shuffle(trainImages, trainLabels, IMAGE_SIZE, LABEL_SIZE);
float cost = 0;
for(int j = 0; j < num_training_examples / batch_size; j++) {
// Attaching the data to the computation graph...
// Convert the data to dense one-hot vectors.
// TODO: make the graph handle sparse indices with a proper lookup layer.
// TODO: use different sentence lengths for the batches.
for (int t = 0; t < num_source_tokens; ++t) {
Tensor Xt({batch_size, static_cast<int>(source_vocab.Size())});
std::vector<float> values(batch_size * source_vocab.Size(), 0.0);
int k = 0;
for (int i = 0; i < batch_size; ++i) {
values[k + source_sentences[i + j*batch_size][t]] = 1.0;
k += source_vocab.Size();
}
thrust::copy(values.begin(), values.end(), Xt.begin());
// Attach this slice to the graph.
std::stringstream ss;
ss << "X" << t;
g[ss.str()] = Xt;
}
for (int t = 0; t < num_target_tokens; ++t) {
Tensor Yt({batch_size, static_cast<int>(target_vocab.Size())});
std::vector<float> values(batch_size * target_vocab.Size(), 0.0);
int k = 0;
for (int i = 0; i < batch_size; ++i) {
values[k + target_sentences[i + j*batch_size][t]] = 1.0;
k += target_vocab.Size();
}
thrust::copy(values.begin(), values.end(), Yt.begin());
// Attach this slice to the graph.
std::stringstream ss;
ss << "Y" << t;
g[ss.str()] = Yt;
}
opt(g, batch_size);
cost += g["cost"].val()[0];
}
std::cerr << "Epoch: " << epoch << " - Cost: "
<< cost / num_training_examples * batch_size
<< " - " << timer.format(3, "%ws") << std::endl;
}
std::cerr << "Total: " << total.format(3, "%ws") << std::endl;
#if 0
Adam opt;
int num_epochs = 20;
for(size_t epoch = 0; epoch < num_epochs; ++epoch) {
@ -222,6 +282,7 @@ int main(int argc, char** argv) {
<< "Loss = " << g["cost"].val()[0]
<< std::endl;
}
#endif
return 0;
}