int main( int argc, char* argv[]) {
GFLAGS_NAMESPACE::SetUsageMessage(
"\n"
"Sentiment Analysis as Competition amongst Language Models\n"
"---------------------------------------------------------\n"
"\n"
"We present a dual formulation of the word sequence classification\n"
"task: we treat each label’s examples as originating from different\n"
"languages and we train language models for each label; at test\n"
"time we compare the likelihood of a sequence under each label’s\n"
"language model to find the most likely assignment.\n"
"\n"
" @author Jonathan Raiman\n"
" @date February 13th 2015"
);
GFLAGS_NAMESPACE::ParseCommandLineFlags(&argc, &argv, true);
auto epochs = FLAGS_epochs;
auto sentiment_treebank = SST::load(FLAGS_train);
auto word_vocab = SST::get_vocabulary(sentiment_treebank, FLAGS_min_occurence);
auto vocab_size = word_vocab.size();
// Load Dataset of Trees:
std::cout << "Unique Treees Loaded : " << sentiment_treebank.size() << std::endl
<< " Example tree : " << *sentiment_treebank[sentiment_treebank.size()-1] << std::endl
<< " Vocabulary size : " << vocab_size << std::endl;
// Put trees into matrices:
const int NUM_SENTIMENTS = 5;
vector<vector<Databatch>> datasets(NUM_SENTIMENTS);
vector<vector<Databatch>> validation_sets(NUM_SENTIMENTS);
{
vector<tokenized_uint_labeled_dataset> tree_types(NUM_SENTIMENTS);
vector<tokenized_uint_labeled_dataset> validation_tree_types(NUM_SENTIMENTS);
for (auto& tree : sentiment_treebank) {
if (((int) tree->label) > 4)
utils::exit_with_message("Error: One of the trees has a label other than 0-4");
tree_types[tree->label].emplace_back(tree->to_labeled_pair());
for (auto& child : tree->general_children) {
if (((int)child->label) > 4)
utils::exit_with_message("Error: One of the trees's children has a label other than 0-4");
tree_types[(int) child->label].emplace_back(child->to_labeled_pair());
}
}
auto validation_treebank = SST::load(FLAGS_validation);
for (auto& tree : validation_treebank) {
if (((int) tree->label) > 4)
utils::exit_with_message("Error: One of the trees has a label other than 0-4");
validation_tree_types[tree->label].emplace_back(tree->to_labeled_pair());
for (auto& child : tree->general_children) {
if (((int)child->label) > 4)
utils::exit_with_message("Error: One of the trees's children has a label other than 0-4");
validation_tree_types[(int) child->label].emplace_back(child->to_labeled_pair());
}
}
int i = 0;
for (auto& tree_type : tree_types)
std::cout << "Label type " << i++ << " has " << tree_type.size() << " different examples" << std::endl;
i = 0;
for (auto& tree_type : validation_tree_types) {
std::cout << "Label type " << i++ << " has " << tree_type.size() << " validation examples" << std::endl;
}
i = 0;
for (auto& tree_type : tree_types) {
datasets[i++] = Databatch::create_dataset(tree_type, word_vocab, FLAGS_minibatch, true);
}
i = 0;
for (auto& tree_type : validation_tree_types)
validation_sets[i++] = Databatch::create_dataset(tree_type, word_vocab, FLAGS_minibatch, true);
}
std::cout << " Max training epochs = " << FLAGS_epochs << std::endl;
std::cout << " Training cutoff = " << FLAGS_cutoff << std::endl;
std::cout << "Minibatches/label/x-val = " << FLAGS_epoch_batches << std::endl;
#ifdef USE_GATES
std::cout << " using gated model = true" << std::endl;
#else
std::cout << " using gated model = false" << std::endl;
#endif
std::cout << " Use Shortcut LSTMs = " << (FLAGS_shortcut ? "true" : "false") << std::endl;
std::cout << " Comparing models using = " << (FLAGS_use_surprise ? "surprise" : "log likelihood") << std::endl;
pool = new ThreadPool(FLAGS_j);
int patience = 0;
// with a rampup model we start with zero memory penalty and gradually increase the memory
// L1 penalty until it reaches the desired level.
// this allows early exploration, but only later forces sparsity on the model
std::vector<MODEL_USED<REAL_t>> models;
vector<vector<MODEL_USED<REAL_t>>> thread_models;
vector<Solver::Adam<REAL_t>> solvers;
for (int sentiment = 0; sentiment < NUM_SENTIMENTS; sentiment++) {
if (!FLAGS_load.empty()) {
std::cout << "Loading model : \"" << FLAGS_load << sentiment << "\"" << std::endl;
models.emplace_back(MODEL_USED<REAL_t>::load(FLAGS_load + std::to_string(sentiment)));
} else {
models.emplace_back(
word_vocab.size(),
FLAGS_input_size,
FLAGS_hidden,
FLAGS_stack_size < 1 ? 1 : FLAGS_stack_size,
word_vocab.size(),
FLAGS_shortcut,
FLAGS_memory_feeds_gates
);
}
thread_models.emplace_back();
for (int thread_no = 0; thread_no < FLAGS_j; ++thread_no) {
thread_models[sentiment].push_back(models[sentiment].shallow_copy());
}
auto params = models[sentiment].parameters();
//solvers.emplace_back(params, FLAGS_rho);
solvers.emplace_back(params, 0.1, 0.001, 1e-9, 5.0);
}
int epoch = 0;
REAL_t accuracy = 0.0;
REAL_t new_accuracy;
Throttled t;
while (accuracy < FLAGS_cutoff && patience < FLAGS_patience) {
stringstream ss;
ss << "Epoch " << ++epoch;
atomic<int> batches_processed(0);
ReportProgress<double> journalist(ss.str(), NUM_SENTIMENTS * FLAGS_epoch_batches);
for (int sentiment = 0; sentiment < NUM_SENTIMENTS; sentiment++) {
for (int batch_id = 0; batch_id < FLAGS_epoch_batches; ++batch_id) {
pool->run([&thread_models, &journalist, &solvers, &datasets, sentiment, &epoch, &accuracy, &batches_processed]() {
auto& thread_model = thread_models[sentiment][ThreadPool::get_thread_number()];
auto& solver = solvers[sentiment];
auto thread_parameters = thread_model.parameters();
auto& minibatch = datasets[sentiment][utils::randint(0, datasets[sentiment].size()-1)];
#ifdef USE_GATES
thread_model.memory_penalty = (FLAGS_memory_penalty / minibatch.data->cols()) * std::min((REAL_t)1.0, ((REAL_t) (epoch*epoch) / ((REAL_t) FLAGS_memory_rampup * FLAGS_memory_rampup)));
#endif
thread_model.masked_predict_cost(
minibatch.data,
minibatch.data,
minibatch.mask,
FLAGS_dropout,
1,
0);
graph::backward(); // backpropagate
solver.step(thread_parameters); // One step of gradient descent
journalist.tick(++batches_processed, accuracy);
});
}
}
while(true) {
journalist.pause();
reconstruct_random_beams(models, datasets, word_vocab,
utils::randint(2, 6), // how many elements to use as a primer for beam
FLAGS_num_reconstructions, // how many beams
20 // max size of a sequence
);
journalist.resume();
// TODO(jonathan): reconstructions go here..
if (pool->wait_until_idle(seconds(20)))
break;
}
journalist.done();
new_accuracy = average_error(models, validation_sets);
if (new_accuracy < accuracy) {
patience +=1;
} else {
patience = 0;
}
accuracy = new_accuracy;
t.maybe_run(seconds(600), [&models]() {
int i = 0;
for (auto& model : models) {
model.save(FLAGS_save + std::to_string(i));
i++;
}
});
}
return 0;
}
int main( int argc, char* argv[]) {
GFLAGS_NAMESPACE::SetUsageMessage(
"\n"
"RNN Language Model using Stacked LSTMs\n"
"--------------------------------------\n"
"\n"
"Predict next word in sentence using Stacked LSTMs.\n"
"\n"
" @author Jonathan Raiman\n"
" @date February 15th 2015"
);
GFLAGS_NAMESPACE::ParseCommandLineFlags(&argc, &argv, true);
utils::update_device(FLAGS_device);
utils::Vocab word_vocab;
vector<LanguageBatch<REAL_t>> training;
vector<LanguageBatch<REAL_t>> validation;
Timer dl_timer("Dataset loading");
std::tie(word_vocab, training) = load_dataset_and_vocabulary<REAL_t>(
FLAGS_train,
FLAGS_min_occurence,
FLAGS_minibatch);
validation = load_dataset_with_vocabulary<REAL_t>(
FLAGS_validation,
word_vocab,
FLAGS_minibatch);
dl_timer.stop();
std::cout << " Vocabulary size = " << word_vocab.size() << " (occuring more than " << FLAGS_min_occurence << ")" << std::endl
<< "Max training epochs = " << FLAGS_epochs << std::endl
<< " Training cutoff = " << FLAGS_cutoff << std::endl
<< " Number of threads = " << FLAGS_j << std::endl
<< " minibatch size = " << FLAGS_minibatch << std::endl
<< " max_patience = " << FLAGS_patience << std::endl
<< " device = " << (FLAGS_device == -1 ? "cpu" : "gpu") << std::endl;
pool = new ThreadPool(FLAGS_j);
shared_ptr<Visualizer> visualizer;
if (!FLAGS_visualizer.empty()) {
try {
visualizer = make_shared<Visualizer>(FLAGS_visualizer, FLAGS_visualizer_hostname, FLAGS_visualizer_port);
} catch (std::runtime_error e) {
std::cout << e.what() << std::endl; // could not connect to redis.
}
}
auto model = stacked_model_from_CLI<REAL_t>(
FLAGS_load,
word_vocab.size(),
word_vocab.size(),
true);
auto parameters = model.parameters();
auto solver = Solver::construct(FLAGS_solver, parameters, (REAL_t) FLAGS_learning_rate);
// replicate model for each thread:
vector<StackedModel<REAL_t>> thread_models;
if (FLAGS_j == 1) {
thread_models.emplace_back(model, false, false);
} else {
for (int i = 0; i < FLAGS_j; ++i)
thread_models.emplace_back(model, false, true);
}
Throttled throttled;
Throttled throttled_wps;
int epoch = 0;
auto cost = std::numeric_limits<REAL_t>::infinity();
double new_cost = 0.0;
int patience = 0;
double average_words_per_second = 0;
int word_done_in_past_second = 0;
utils::ThreadAverage avg_error(FLAGS_j);
while (cost > FLAGS_cutoff && epoch < FLAGS_epochs && patience < FLAGS_patience) {
std::atomic<int> full_code_size(0);
auto random_batch_order = utils::random_arange(training.size());
std::atomic<int> batches_processed(0);
ReportProgress<double> journalist(utils::MS() << "Training epoch " << epoch, random_batch_order.size());
for (auto batch_id : random_batch_order) {
pool->run([&, solver, batch_id]() {
auto& thread_model = thread_models[ThreadPool::get_thread_number()];
auto thread_parameters = thread_model.parameters();
auto& minibatch = training[batch_id];
auto error = thread_model.masked_predict_cost(
minibatch, FLAGS_dropout,
1 // sequence forecasting problem - predict target one step ahead
);
error.grad();
graph::backward(); // backpropagate
solver->step(thread_parameters);
// word_done_in_past_second += minibatch.total_codes;
word_done_in_past_second += (minibatch.data.dims(0)-1) * (minibatch.data.dims(1));
throttled_wps.maybe_run(seconds(1), [&]() {
average_words_per_second = 0.5 * average_words_per_second + 0.5 * word_done_in_past_second;
word_done_in_past_second = 0;
});
if (FLAGS_show_wps) {
journalist.tick(++batches_processed, average_words_per_second);
} else {
avg_error.update(error.sum().w(0) / minibatch.total_codes);
journalist.tick(++batches_processed, avg_error.average());
}
if (FLAGS_show_reconstructions) {
throttled.maybe_run(seconds(10), [&]() {
// Tell the journalist the news can wait
journalist.pause();
graph::NoBackprop nb;
auto& random_batch = training[utils::randint(0, training.size() - 1)];
auto random_example_index = utils::randint(0, random_batch.data.dims(1) - 1);
std::cout << random_batch.code_lengths[random_example_index] << std::endl;
int priming_size = utils::randint(1, std::min(6, random_batch.code_lengths[random_example_index]));
vector<uint> priming;
for (int i = 0; i < priming_size; ++i) {
priming.push_back(random_batch.data.w(i, random_example_index));
}
auto beams = the_beam_search(model, word_vocab, &priming);
vector<uint> priming_no_start(priming.begin() + 1, priming.end());
std::cout << "Reconstructions: " << std::endl;
for (auto& beam : beams) {
std::cout << "=> (" << std::setprecision( 5 ) << beam.score << ") ";
std::cout << utils::join(word_vocab.decode(&priming_no_start), " ") << " ";
std::cout << utils::bold;
std::cout << utils::join(word_vocab.decode(&beam.solution, true), " ") << std::endl;
std::cout << utils::reset_color << std::endl;
}
if (visualizer != nullptr) {
vector<vector<string>> sentences;
vector<REAL_t> probs;
for (auto& beam : beams) {
sentences.emplace_back(word_vocab.decode(&beam.solution, true));
probs.emplace_back(beam.score);
}
auto input_sentence = make_shared<Sentence<REAL_t>>(
word_vocab.decode(&priming_no_start));
auto sentences_viz = make_shared<Sentences<REAL_t>>(sentences);
sentences_viz->set_weights(probs);
auto input_output_pair = GridLayout();
input_output_pair.add_in_column(0, input_sentence);
input_output_pair.add_in_column(1, sentences_viz);
visualizer->feed(input_output_pair.to_json());
}
journalist.resume();
});
}
});
}
pool->wait_until_idle();
journalist.done();
new_cost = average_error(model, validation);
if (new_cost >= cost) {
patience += 1;
} else {
patience = 0;
}
cost = new_cost;
std::cout << "epoch (" << epoch << ") KL error = "
<< std::setprecision(3) << std::fixed
<< std::setw(5) << std::setfill(' ') << new_cost
<< " patience = " << patience << std::endl;
maybe_save_model(&model);
Timer::report();
epoch++;
}
return 0;
}
