def main(argv):
tf.logging.set_verbosity(tf.logging.INFO)
trainer_lib.set_random_seed(FLAGS.random_seed)
usr_dir.import_usr_dir(FLAGS.t2t_usr_dir)
t2t_trainer.maybe_log_registry_and_exit()
if FLAGS.generate_data:
t2t_trainer.generate_data()
if argv:
t2t_trainer.set_hparams_from_args(argv[1:])
hparams = t2t_trainer.create_hparams()
trainer_lib.add_problem_hparams(hparams, FLAGS.problem)
pruning_params = create_pruning_params()
pruning_strategy = create_pruning_strategy(pruning_params.strategy)
config = t2t_trainer.create_run_config(hparams)
params = {"batch_size": hparams.batch_size}
# add "_rev" as a hack to avoid image standardization
problem = registry.problem(FLAGS.problem)
input_fn = problem.make_estimator_input_fn(tf.estimator.ModeKeys.EVAL,
hparams)
dataset = input_fn(params, config).repeat()
features, labels = dataset.make_one_shot_iterator().get_next()
sess = tf.Session()
model_fn = t2t_model.T2TModel.make_estimator_model_fn(
FLAGS.model, hparams, use_tpu=FLAGS.use_tpu)
spec = model_fn(
features,
labels,
tf.estimator.ModeKeys.EVAL,
params=hparams,
config=config)
# Restore weights
saver = tf.train.Saver()
checkpoint_path = os.path.expanduser(FLAGS.output_dir or
FLAGS.checkpoint_path)
saver.restore(sess, tf.train.latest_checkpoint(checkpoint_path))
def eval_model():
preds = spec.predictions["predictions"]
preds = tf.argmax(preds, -1, output_type=labels.dtype)
_, acc_update_op = tf.metrics.accuracy(labels=labels, predictions=preds)
sess.run(tf.initialize_local_variables())
for _ in range(FLAGS.eval_steps):
acc = sess.run(acc_update_op)
return acc
pruning_utils.sparsify(sess, eval_model, pruning_strategy, pruning_params)
def main(argv):
tf.logging.set_verbosity(tf.logging.INFO)
trainer_lib.set_random_seed(FLAGS.random_seed)
usr_dir.import_usr_dir(FLAGS.t2t_usr_dir)
t2t_trainer.maybe_log_registry_and_exit()
if FLAGS.generate_data:
t2t_trainer.generate_data()
if argv:
t2t_trainer.set_hparams_from_args(argv[1:])
hparams = t2t_trainer.create_hparams()
trainer_lib.add_problem_hparams(hparams, FLAGS.problem)
pruning_params = create_pruning_params()
pruning_strategy = create_pruning_strategy(pruning_params.strategy)
config = t2t_trainer.create_run_config(hparams)
params = {"batch_size": hparams.batch_size}
# add "_rev" as a hack to avoid image standardization
problem = registry.problem(FLAGS.problem)
input_fn = problem.make_estimator_input_fn(tf.estimator.ModeKeys.EVAL,
hparams)
dataset = input_fn(params, config).repeat()
features, labels = dataset.make_one_shot_iterator().get_next()
sess = tf.Session()
model_fn = t2t_model.T2TModel.make_estimator_model_fn(
FLAGS.model, hparams, use_tpu=FLAGS.use_tpu)
spec = model_fn(
features,
labels,
tf.estimator.ModeKeys.EVAL,
params=hparams,
config=config)
# Restore weights
saver = tf.train.Saver()
checkpoint_path = os.path.expanduser(FLAGS.output_dir or
FLAGS.checkpoint_path)
saver.restore(sess, tf.train.latest_checkpoint(checkpoint_path))
def eval_model():
preds = spec.predictions["predictions"]
preds = tf.argmax(preds, -1, output_type=labels.dtype)
_, acc_update_op = tf.metrics.accuracy(labels=labels, predictions=preds)
sess.run(tf.initialize_local_variables())
for _ in range(FLAGS.eval_steps):
acc = sess.run(acc_update_op)
return acc
pruning_utils.sparsify(sess, eval_model, pruning_strategy, pruning_params)
