本文整理汇总了Python中cifar_input.build_input方法的典型用法代码示例。如果您正苦于以下问题：Python cifar_input.build_input方法的具体用法？Python cifar_input.build_input怎么用？Python cifar_input.build_input使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类cifar_input的用法示例。
在下文中一共展示了cifar_input.build_input方法的4个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: get_model
# 需要导入模块: import cifar_input [as 别名]
# 或者: from cifar_input import build_input [as 别名]
def get_model(hps, dataset, train_data_path, mode='train'):
images, labels = cifar_input.build_input(
dataset, train_data_path, hps.batch_size, mode)
model = resnet_model.ResNet(hps, images, labels, mode)
model.build_graph()
return model 示例2: evaluate
# 需要导入模块: import cifar_input [as 别名]
# 或者: from cifar_input import build_input [as 别名]
def evaluate(hps):
"""Eval loop."""
images, labels = cifar_input.build_input(
FLAGS.dataset, FLAGS.eval_data_path, hps.batch_size, FLAGS.mode)
model = resnet_model.ResNet(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) = sess.run(
[model.summaries, model.cost, model.predictions,
model.labels, model.global_step])
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: %.3f, precision: %.3f, best precision: %.3f' %
(loss, precision, best_precision))
summary_writer.flush()
if FLAGS.eval_once:
break
time.sleep(60) 示例3: evaluate
# 需要导入模块: import cifar_input [as 别名]
# 或者: from cifar_input import build_input [as 别名]
def evaluate(hps):
"""Eval loop."""
images, labels = cifar_input.build_input(
FLAGS.dataset, FLAGS.eval_data_path, hps.batch_size, FLAGS.mode)
model = resnet_model.ResNet(hps, images, labels, FLAGS.mode)
model.build_graph()
saver = tf.train.Saver()
summary_writer = tf.train.SummaryWriter(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:
time.sleep(60)
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 xrange(FLAGS.eval_batch_count):
(summaries, loss, predictions, truth, train_step) = sess.run(
[model.summaries, model.cost, model.predictions,
model.labels, model.global_step])
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: %.3f, precision: %.3f, best precision: %.3f\n' %
(loss, precision, best_precision))
summary_writer.flush()
if FLAGS.eval_once:
break 示例4: train
# 需要导入模块: import cifar_input [as 别名]
# 或者: from cifar_input import build_input [as 别名]
def train(hps):
"""Training loop."""
images, labels = cifar_input.build_input(
FLAGS.dataset, FLAGS.train_data_path, hps.batch_size, FLAGS.mode)
model = resnet_model.ResNet(hps, images, labels, FLAGS.mode)
model.build_graph()
summary_writer = tf.train.SummaryWriter(FLAGS.train_dir)
sv = tf.train.Supervisor(logdir=FLAGS.log_root,
is_chief=True,
summary_op=None,
save_summaries_secs=60,
save_model_secs=300,
global_step=model.global_step)
sess = sv.prepare_or_wait_for_session(
config=tf.ConfigProto(allow_soft_placement=True))
step = 0
lrn_rate = 0.1
while not sv.should_stop():
(_, summaries, loss, predictions, truth, train_step) = sess.run(
[model.train_op, model.summaries, model.cost, model.predictions,
model.labels, model.global_step],
feed_dict={model.lrn_rate: lrn_rate})
if train_step < 40000:
lrn_rate = 0.1
elif train_step < 60000:
lrn_rate = 0.01
elif train_step < 80000:
lrn_rate = 0.001
else:
lrn_rate = 0.0001
truth = np.argmax(truth, axis=1)
predictions = np.argmax(predictions, axis=1)
precision = np.mean(truth == predictions)
step += 1
if step % 100 == 0:
precision_summ = tf.Summary()
precision_summ.value.add(
tag='Precision', simple_value=precision)
summary_writer.add_summary(precision_summ, train_step)
summary_writer.add_summary(summaries, train_step)
tf.logging.info('loss: %.3f, precision: %.3f\n' % (loss, precision))
summary_writer.flush()
sv.Stop()