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//The framework for obtaining user arguments has been inspired by Sittichai Jiampojamarn's Many-to-Many alignment model (m2m-aligner). https://code.google.com/p/m2m-aligner/
#pragma once
#include <string>
#if NPLM_DOUBLE_PRECISION == 1
typedef double user_data_t;
#else
typedef float user_data_t;
#endif
namespace nplm
{
struct param
{
std::string train_file;
std::string validation_file;
std::string test_file;
std::string model_file;
std::string unigram_probs_file;
std::string words_file;
std::string input_words_file;
std::string output_words_file;
std::string model_prefix;
int ngram_size;
int vocab_size;
int input_vocab_size;
int output_vocab_size;
int num_hidden;
int embedding_dimension;
int input_embedding_dimension;
int output_embedding_dimension;
std::string activation_function;
std::string loss_function;
std::string parameter_update;
int minibatch_size;
int validation_minibatch_size;
int num_epochs;
user_data_t learning_rate;
user_data_t conditioning_constant;
user_data_t decay;
user_data_t adagrad_epsilon;
bool init_normal;
user_data_t init_range;
int num_noise_samples;
bool use_momentum;
user_data_t initial_momentum;
user_data_t final_momentum;
user_data_t L2_reg;
double input_dropout;
int null_index;
bool save_best;
bool normalization;
user_data_t normalization_init;
int num_threads;
int debug;
bool premultiply;
bool share_embeddings;
};
} // namespace nplm
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