def run_epoch(session, m, names, counts, epoch_size, eval_op, verbose=False):
"""Runs the model on the given data for one epoch
Args:
session: the tf session holding the model graph
m: an instance of the NamignizerModel
names: a set of lowercase names of 26 characters
counts: a list of the frequency of the above names
epoch_size: the number of batches to run
eval_op: whether to change the params or not, and how to do it
Kwargs:
verbose: whether to print out state of training during the epoch
Returns:
cost: the average cost during the last stage of the epoch
"""
start_time = time.time()
costs = 0.0
iters = 0
for step, (x, y) in enumerate(
data_utils.namignizer_iterator(names, counts, m.batch_size,
m.num_steps, epoch_size)):
cost, _ = session.run(
[m.cost, eval_op], {
m.input_data: x,
m.targets: y,
m.weights: np.ones(m.batch_size * m.num_steps)
})
costs += cost
iters += m.num_steps
if verbose and step % (epoch_size // 10) == 9:
print("%.3f perplexity: %.3f speed: %.0f lps" %
(step * 1.0 / epoch_size, np.exp(costs / iters),
iters * m.batch_size / (time.time() - start_time)))
if step >= epoch_size:
break
return np.exp(costs / iters)
def run_epoch(session, m, names, counts, epoch_size, eval_op, verbose=False):
"""Runs the model on the given data for one epoch
Args:
session: the tf session holding the model graph
m: an instance of the NamignizerModel
names: a set of lowercase names of 26 characters
counts: a list of the frequency of the above names
epoch_size: the number of batches to run
eval_op: whether to change the params or not, and how to do it
Kwargs:
verbose: whether to print out state of training during the epoch
Returns:
cost: the average cost during the last stage of the epoch
"""
start_time = time.time()
costs = 0.0
iters = 0
for step, (x, y) in enumerate(data_utils.namignizer_iterator(names, counts,
m.batch_size, m.num_steps, epoch_size)):
cost, _ = session.run([m.cost, eval_op],
{m.input_data: x,
m.targets: y,
m.initial_state: m.initial_state.eval(),
m.weights: np.ones(m.batch_size * m.num_steps)})
costs += cost
iters += m.num_steps
if verbose and step % (epoch_size // 10) == 9:
print("%.3f perplexity: %.3f speed: %.0f lps" %
(step * 1.0 / epoch_size, np.exp(costs / iters),
iters * m.batch_size / (time.time() - start_time)))
if step >= epoch_size:
break
return np.exp(costs / iters)