lstm.py 文件源码

def run_epoch(session, m, data, eval_op, ITERS, verbose=False):
  """Runs the model on the given data."""
  epoch_size = ((len(data) // m.batch_size) - 1) // m.num_steps
  start_time = time.time()
  costs = 0.0
  iters = 0
  #state = m.initial_state.eval()
  state = session.run(m.initial_state) #.eval()
  step = 0
  for step, (x, y) in enumerate(reader.ptb_iterator(data, m.batch_size,
                                                    m.num_steps)):
    cost, state, _ = session.run([m.cost, m.final_state, eval_op],
                                 {m.input_data: x,
                                  m.targets: y,
                                  m.initial_state: state})
    costs += cost
    iters += m.num_steps

    if verbose and step % (epoch_size // 10) == 10:
      print("%.3f perplexity: %.3f speed: %.0f wps" %
            (step * 1.0 / epoch_size, np.exp(costs / iters),
             iters * m.batch_size / (time.time() - start_time)))

    # few iters for profiling, remove if complete training is needed
    if step > ITERS - 1:
      break

  print("Time for %d iterations %.4f seconds" %
            (ITERS, time.time() - start_time))

  return np.exp(costs / iters)