def evaluate(hps):
"""Eval loop."""
images, labels = cifar_input.build_input(FLAGS.eval_data_path,
hps.batch_size, FLAGS.mode)
model = Net(hps, images, labels, FLAGS.mode)
model.build_graph()
saver = tf.train.Saver()
summary_writer = tf.summary.FileWriter(FLAGS.eval_dir)
sess = tf.Session(config=tf.ConfigProto(allow_soft_placement=True))
tf.train.start_queue_runners(sess)
best_precision = 0.0
while True:
try:
ckpt_state = tf.train.get_checkpoint_state(FLAGS.log_root)
except tf.errors.OutOfRangeError as e:
tf.logging.error("Cannot restore checkpoint: %s", e)
continue
if not (ckpt_state and ckpt_state.model_checkpoint_path):
tf.logging.info("No model to eval yet at %s", FLAGS.log_root)
continue
tf.logging.info("Loading checkpoint %s",
ckpt_state.model_checkpoint_path)
saver.restore(sess, ckpt_state.model_checkpoint_path)
total_prediction, correct_prediction = 0, 0
for _ in six.moves.range(FLAGS.eval_batch_count):
(summaries, loss, predictions, truth, train_step, wmc) = sess.run([
model.summaries, model.cost, model.predictions, model.labels,
model.global_step, model.wmc
])
truth = np.argmax(truth, axis=1)
predictions = np.argmax(predictions, axis=1)
correct_prediction += np.sum(truth == predictions)
total_prediction += predictions.shape[0]
precision = 1.0 * correct_prediction / total_prediction
best_precision = max(precision, best_precision)
precision_summ = tf.Summary()
precision_summ.value.add(tag="Precision", simple_value=precision)
summary_writer.add_summary(precision_summ, train_step)
best_precision_summ = tf.Summary()
best_precision_summ.value.add(tag="Best Precision",
simple_value=best_precision)
summary_writer.add_summary(best_precision_summ, train_step)
summary_writer.add_summary(summaries, train_step)
tf.logging.info(
"loss: %.5f, precision: %.5f, wmc: %.5f, best precision: %.5f" %
(loss, precision, wmc, best_precision))
summary_writer.flush()
if FLAGS.eval_once:
break
time.sleep(60)
def train(hps):
"""Training loop."""
images, labels = cifar_input.build_input(FLAGS.train_data_path,
hps.batch_size, FLAGS.mode)
model = Net(hps, images, labels, FLAGS.mode)
model.build_graph()
param_stats = tf.contrib.tfprof.model_analyzer.print_model_analysis(
tf.get_default_graph(),
tfprof_options=tf.contrib.tfprof.model_analyzer.
TRAINABLE_VARS_PARAMS_STAT_OPTIONS)
sys.stdout.write("total_params: %d\n" % param_stats.total_parameters)
tf.contrib.tfprof.model_analyzer.print_model_analysis(
tf.get_default_graph(),
tfprof_options=tf.contrib.tfprof.model_analyzer.FLOAT_OPS_OPTIONS)
truth = tf.argmax(model.labels, axis=1)
predictions = tf.argmax(model.predictions, axis=1)
labeled_examples = tf.greater(tf.reduce_max(model.labels, axis=1),
tf.zeros([hps.batch_size, 1]))
labeled_examples = tf.cast(labeled_examples, tf.float32)
correct_predictions = tf.cast(tf.equal(predictions, truth), tf.float32)
correct_predictions = tf.multiply(correct_predictions, labeled_examples)
precision = tf.reduce_sum(correct_predictions) / tf.reduce_sum(
labeled_examples)
summary_hook = tf.train.SummarySaverHook(
save_steps=100,
output_dir=FLAGS.train_dir,
summary_op=tf.summary.merge(
[model.summaries,
tf.summary.scalar("Precision", precision)]),
)
logging_hook = tf.train.LoggingTensorHook(
tensors={
"step": model.global_step,
"loss": model.cost,
"wmc": model.wmc,
"cross_entropy": model.cross_entropy,
"precision": precision,
},
every_n_iter=100,
)
class _LearningRateSetterHook(tf.train.SessionRunHook):
"""Sets learning_rate based on global step."""
def begin(self):
self._lrn_rate = 0.1
def before_run(self, run_context):
return tf.train.SessionRunArgs(
model.global_step, feed_dict={model.lrn_rate: self._lrn_rate})
def after_run(self, run_context, run_values):
train_step = run_values.results
if train_step < 10000:
self._lrn_rate = 0.1
elif train_step < 20000:
self._lrn_rate = 0.05
elif train_step < 35000:
self._lrn_rate = 0.01
else:
self._lrn_rate = 0.001
with tf.train.MonitoredTrainingSession(
checkpoint_dir=FLAGS.log_root,
hooks=[logging_hook, _LearningRateSetterHook()],
chief_only_hooks=[summary_hook],
save_summaries_steps=0,
config=tf.ConfigProto(allow_soft_placement=True),
) as mon_sess:
while not mon_sess.should_stop():
mon_sess.run(model.train_op)
