exp_mnist/train_sl.py [125:152]:
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del train_params['embed_scope']
if 'module_scope' in train_params:
  del train_params['module_scope']
#-------------------------------------------------------------------------

print('Running training iteration..')
num_iter_per_epoch = int(train_loader.batch_loader.num_inst/args['batch_size'])
print('Number of iterations per epoch: %d' % num_iter_per_epoch)

# exponential smoothing for loss
smoother = metrics.ExponentialSmoothing()

for n_iter, batch in enumerate(train_loader.batches()):
  # add epoch and iteration
  epoch = float(n_iter) / num_iter_per_epoch
  batch['epoch'] = epoch
  batch['n_iter'] = n_iter

  if n_iter >= args['num_epochs'] * num_iter_per_epoch:
    break

  # perform training iteration
  losses, _ = model.run_train_iteration(batch, sess)
  losses = smoother.report(losses)

  # printing log
  if n_iter % 10 == 0:
    cur_time = time.strftime('%a %d%b%y %X', time.gmtime())
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exp_vd/train_sl.py [135:162]:
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del train_params['embed_scope']
if 'module_scope' in train_params:
  del train_params['module_scope']
#-------------------------------------------------------------------------

print('Running training iteration..')
num_iter_per_epoch = int(train_loader.batch_loader.num_inst/args['batch_size'])
print('Number of iterations per epoch: %d' % num_iter_per_epoch)

# exponential smoothing for loss
smoother = metrics.ExponentialSmoothing()

for n_iter, batch in enumerate(train_loader.batches()):
  # add epoch and iteration
  epoch = float(n_iter) / num_iter_per_epoch
  batch['epoch'] = epoch
  batch['n_iter'] = n_iter

  if n_iter >= args['num_epochs'] * num_iter_per_epoch:
    break

  # perform training iteration
  losses, _ = model.run_train_iteration(batch, sess)
  losses = smoother.report(losses)

  # printing log
  if n_iter % 10 == 0:
    cur_time = time.strftime('%a %d%b%y %X', time.gmtime())
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