def compute_perplexity(model, sess, name):
"""Compute perplexity of the output of the model.
Args:
model: model for compute perplexity.
sess: tensorflow session to use.
name: name of the batch.
Returns:
The perplexity of the eval outputs.
"""
total_loss = 0
total_predict_count = 0
start_time = time.time()
while True:
try:
loss, predict_count, batch_size = model.eval(sess)
total_loss += loss * batch_size
total_predict_count += predict_count
except tf.errors.OutOfRangeError:
break
perplexity = utils.safe_exp(total_loss / total_predict_count)
utils.print_time(" eval %s: perplexity %.2f" % (name, perplexity),
start_time)
return perplexity
def process_stats(stats, info, global_step, steps_per_stats, log_f):
"""Update info and check for overflow."""
# Update info
info["avg_step_time"] = stats["step_time"] / steps_per_stats
info["avg_grad_norm"] = stats["grad_norm"] / steps_per_stats
info["train_ppl"] = utils.safe_exp(stats["loss"] / stats["predict_count"])
info["speed"] = stats["total_count"] / (1000 * stats["step_time"])
# Check for overflow
is_overflow = False
train_ppl = info["train_ppl"]
if math.isnan(train_ppl) or math.isinf(train_ppl) or train_ppl > 1e20:
utils.print_out(" step %d overflow, stop early" % global_step, log_f)
is_overflow = True
return is_overflow
def ppl(self):
return utils.safe_exp(self.loss / self.n_words)