def train_model(self):
# train a model
model, optimizer, stats_dictionary = train(self.train_file,
self.test_file,
self.batch_size,
self.epochs,
self.gpu_mode,
self.num_workers,
self.retrain_model,
self.retrain_model_path,
self.gru_layers,
self.hidden_size,
self.learning_rate,
self.weight_decay,
self.model_dir,
self.stats_dir,
train_mode=True)
return model, optimizer, stats_dictionary
def try_params(self, n_iterations, model_params, model_path):
"""
Try a parameter space to train a model with n_iterations (epochs).
:param n_iterations: Number of epochs to train on
:param model_params: Parameter space
:return: trained model, optimizer and stats dictionary (loss and others)
"""
# Number of iterations or epoch for the model to train on
n_iterations = int(round(n_iterations))
params, retrain_model, retrain_model_path, prev_ite = model_params
sys.stderr.write(TextColor.BLUE + '\nEpochs: ' + str(n_iterations) + "\n" + TextColor.END)
sys.stderr.write(TextColor.BLUE + str(params) + "\n" + TextColor.END)
epoch_limit = int(n_iterations)
enc_lr = params['encoder_lr']
enc_l2 = params['encoder_l2']
dec_lr = params['decoder_lr']
dec_l2 = params['decoder_l2']
# train a model
enc_model, dec_model, enc_optimizer, dec_optimizer, stats_dictionary = train(self.train_file,
self.test_file,
self.batch_size,
epoch_limit,
self.gpu_mode,
self.num_workers,
retrain_model,
retrain_model_path,
self.gru_layers,
self.hidden_size,
enc_lr,
enc_l2,
dec_lr,
dec_l2,
model_dir=None,
stats_dir=None,
train_mode=False)
save_best_model(enc_model, dec_model, enc_optimizer, dec_optimizer,
self.hidden_size, self.gru_layers, epoch_limit, model_path)
return enc_model, dec_model, enc_optimizer, dec_optimizer, stats_dictionary
def train_model(self):
# train a model
enc_model, dec_model, enc_optimizer, dec_optimizer, stats_dictionary = train(
self.train_file,
self.test_file,
self.batch_size,
self.epochs,
self.gpu_mode,
self.num_workers,
self.retrain_model,
self.retrain_model_path,
self.gru_layers,
self.hidden_size,
self.encoder_lr,
self.encoder_l2,
self.decoder_lr,
self.decoder_l2,
self.model_dir,
self.stats_dir,
train_mode=True)
return enc_model, dec_model, enc_optimizer, dec_optimizer, stats_dictionary