add logging operation

train.py

@@ -101,6 +101,20 @@ def eval_epoch(model, validation_data, crit):
 def train(model, training_data, validation_data, crit, optimizer, opt):
  ''' Start training '''
 
+ log_train_file = None
+ log_valid_file = None
+
+ if opt.log:
+ log_train_file = opt.log + '.train.log'
+ log_valid_file = opt.log + '.valid.log'
+
+ print('Training performance will be written to file: {} and {}'.format(
+ log_train_file, log_valid_file))
+
+ with open(log_train_file, 'w') as log_tf, open(log_valid_file, 'w') as log_vf:
+ log_tf.write('epoch, loss, accuracy\n')
+ log_vf.write('epoch, loss, accuracy\n')
+
  valid_accus = []
  for epoch_i in range(opt.epoch):
  print('[ Epoch', epoch_i, ']')
@@ -131,6 +145,13 @@ def train(model, training_data, validation_data, crit, optimizer, opt):
  torch.save(checkpoint, model_name)
  print(' - [Info] The checkpoint file has been updated.')
 
+ if log_train_file and log_valid_file:
+ with open(log_train_file, 'w') as log_tf, open(log_valid_file, 'w') as log_vf:
+ log_tf.write('{epoch}, {loss: 8.5f}, {accu:3.3}\n'.format(
+ epoch=epoch_i, loss=train_loss, accu=100*train_accu))
+ log_vf.write('{epoch}, {loss: 8.5f}, {accu:3.3}\n'.format(
+ epoch=epoch_i, loss=valid_loss, accu=100*valid_accu))
+
 def main():
  ''' Main function '''
  parser = argparse.ArgumentParser()
@@ -212,10 +233,10 @@ def main():
  #print(transformer)
 
  optimizer = ScheduledOptim(
-  optim.Adam(
-  transformer.get_trainable_parameters(),
-  betas=(0.9, 0.98), eps=1e-09),
-  opt.d_model, opt.n_warmup_steps)
+ optim.Adam(
+ transformer.get_trainable_parameters(),
+ betas=(0.9, 0.98), eps=1e-09),
+ opt.d_model, opt.n_warmup_steps)
 
 
  def get_criterion(vocab_size):