Python cifar10.train方法代码示例

# 需要导入模块: import cifar10 [as 别名]
# 或者: from cifar10 import train [as 别名]
def main(argv=None):  # pylint: disable=unused-argument
  cifar10.maybe_download_and_extract()
  if tf.gfile.Exists(FLAGS.train_dir):
    tf.gfile.DeleteRecursively(FLAGS.train_dir)
  tf.gfile.MakeDirs(FLAGS.train_dir)
  train() 

# 需要导入模块: import cifar10 [as 别名]
# 或者: from cifar10 import train [as 别名]
def main(argv=None):  # pylint: disable=unused-argument
  if tf.gfile.Exists(FLAGS.train_dir):
    tf.gfile.DeleteRecursively(FLAGS.train_dir)
  tf.gfile.MakeDirs(FLAGS.train_dir)
  train() 

# 需要导入模块: import cifar10 [as 别名]
# 或者: from cifar10 import train [as 别名]
def main(argv=None):  # pylint: disable=unused-argument
  # cifar10.maybe_download_and_extract()
  if tf.gfile.Exists(FLAGS.train_dir):
    tf.gfile.DeleteRecursively(FLAGS.train_dir)
  tf.gfile.MakeDirs(FLAGS.train_dir)
  _, _ = yarntf.createClusterServer()
  train() 

# 需要导入模块: import cifar10 [as 别名]
# 或者: from cifar10 import train [as 别名]
def main(argv=None):  # pylint: disable=unused-argument
  cifar10.maybe_download_and_extract()
  if FLAGS.vanilla:
    train_dir = FLAGS.train_dir + '/vanilla' 
  else:
    train_dir = FLAGS.train_dir + '/vardrop'
  if tf.gfile.Exists(train_dir) and FLAGS.clean:
    tf.gfile.DeleteRecursively(train_dir)
  if not tf.gfile.Exists(train_dir):
    tf.gfile.MakeDirs(train_dir)
  train(train_dir) 

# 需要导入模块: import cifar10 [as 别名]
# 或者: from cifar10 import train [as 别名]
def main(argv=None):  # pylint: disable=unused-argument
  cifar10.maybe_download_and_extract()
  train() 

# 需要导入模块: import cifar10 [as 别名]
# 或者: from cifar10 import train [as 别名]
def train():
  """Train CIFAR-10 for a number of steps."""
  with tf.Graph().as_default():
    global_step = tf.contrib.framework.get_or_create_global_step()

    # Get images and labels for CIFAR-10.
    # Force input pipeline to CPU:0 to avoid operations sometimes ending up on
    # GPU and resulting in a slow down.
    with tf.device('/cpu:0'):
      images, labels = cifar10.distorted_inputs()

    # Build a Graph that computes the logits predictions from the
    # inference model.
    logits = cifar10.inference(images)

    # Calculate loss.
    loss = cifar10.loss(logits, labels)

    # Build a Graph that trains the model with one batch of examples and
    # updates the model parameters.
    train_op = cifar10.train(loss, global_step)

    class _LoggerHook(tf.train.SessionRunHook):
      """Logs loss and runtime."""

      def begin(self):
        self._step = -1
        self._start_time = time.time()

      def before_run(self, run_context):
        self._step += 1
        return tf.train.SessionRunArgs(loss)  # Asks for loss value.

      def after_run(self, run_context, run_values):
        if self._step % FLAGS.log_frequency == 0:
          current_time = time.time()
          duration = current_time - self._start_time
          self._start_time = current_time

          loss_value = run_values.results
          examples_per_sec = FLAGS.log_frequency * FLAGS.batch_size / duration
          sec_per_batch = float(duration / FLAGS.log_frequency)

          format_str = ('%s: step %d, loss = %.2f (%.1f examples/sec; %.3f '
                        'sec/batch)')
          print (format_str % (datetime.now(), self._step, loss_value,
                               examples_per_sec, sec_per_batch))

    with tf.train.MonitoredTrainingSession(
        checkpoint_dir=FLAGS.train_dir,
        hooks=[tf.train.StopAtStepHook(last_step=FLAGS.max_steps),
               tf.train.NanTensorHook(loss),
               _LoggerHook()],
        config=tf.ConfigProto(
            log_device_placement=FLAGS.log_device_placement)) as mon_sess:
      while not mon_sess.should_stop():
        mon_sess.run(train_op) 

# 需要导入模块: import cifar10 [as 别名]
# 或者: from cifar10 import train [as 别名]
def train():
  """Train CIFAR-10 for a number of steps."""
  with tf.Graph().as_default():
    global_step = tf.train.get_or_create_global_step()

    # Get images and labels for CIFAR-10.
    # Force input pipeline to CPU:0 to avoid operations sometimes ending up on
    # GPU and resulting in a slow down.
    with tf.device('/cpu:0'):
      images, labels = cifar10.distorted_inputs()

    # Build a Graph that computes the logits predictions from the
    # inference model.
    logits = cifar10.inference(images)

    # Calculate loss.
    loss = cifar10.loss(logits, labels)

    # Build a Graph that trains the model with one batch of examples and
    # updates the model parameters.
    train_op = cifar10.train(loss, global_step)

    class _LoggerHook(tf.train.SessionRunHook):
      """Logs loss and runtime."""

      def begin(self):
        self._step = -1
        self._start_time = time.time()

      def before_run(self, run_context):
        self._step += 1
        return tf.train.SessionRunArgs(loss)  # Asks for loss value.

      def after_run(self, run_context, run_values):
        if self._step % FLAGS.log_frequency == 0:
          current_time = time.time()
          duration = current_time - self._start_time
          self._start_time = current_time

          loss_value = run_values.results
          examples_per_sec = FLAGS.log_frequency * FLAGS.batch_size / duration
          sec_per_batch = float(duration / FLAGS.log_frequency)

          format_str = ('%s: step %d, loss = %.2f (%.1f examples/sec; %.3f '
                        'sec/batch)')
          print (format_str % (datetime.now(), self._step, loss_value,
                               examples_per_sec, sec_per_batch))

    with tf.train.MonitoredTrainingSession(
        checkpoint_dir=FLAGS.train_dir,
        hooks=[tf.train.StopAtStepHook(last_step=FLAGS.max_steps),
               tf.train.NanTensorHook(loss),
               _LoggerHook()],
        config=tf.ConfigProto(
            log_device_placement=FLAGS.log_device_placement)) as mon_sess:
      while not mon_sess.should_stop():
        mon_sess.run(train_op) 

# 需要导入模块: import cifar10 [as 别名]
# 或者: from cifar10 import train [as 别名]
def train():
  """Train CIFAR-10 for a number of steps."""
  with tf.Graph().as_default():
    global_step = tf.contrib.framework.get_or_create_global_step()

    # Get images and labels for CIFAR-10.
    images, labels = cifar10.distorted_inputs()

    # Build a Graph that computes the logits predictions from the
    # inference model.
    logits = cifar10.inference(images)

    # Calculate loss.
    loss = cifar10.loss(logits, labels)

    # Build a Graph that trains the model with one batch of examples and
    # updates the model parameters.
    train_op = cifar10.train(loss, global_step)

    class _LoggerHook(tf.train.SessionRunHook):
      """Logs loss and runtime."""

      def begin(self):
        self._step = -1
        self._start_time = time.time()

      def before_run(self, run_context):
        self._step += 1
        return tf.train.SessionRunArgs(loss)  # Asks for loss value.

      def after_run(self, run_context, run_values):
        if self._step % FLAGS.log_frequency == 0:
          current_time = time.time()
          duration = current_time - self._start_time
          self._start_time = current_time

          loss_value = run_values.results
          examples_per_sec = FLAGS.log_frequency * FLAGS.batch_size / duration
          sec_per_batch = float(duration / FLAGS.log_frequency)

          format_str = ('%s: step %d, loss = %.2f (%.1f examples/sec; %.3f '
                        'sec/batch)')
          print (format_str % (datetime.now(), self._step, loss_value,
                               examples_per_sec, sec_per_batch))

    with tf.train.MonitoredTrainingSession(
        checkpoint_dir=FLAGS.train_dir,
        hooks=[tf.train.StopAtStepHook(last_step=FLAGS.max_steps),
               tf.train.NanTensorHook(loss),
               _LoggerHook()],
        config=tf.ConfigProto(
            log_device_placement=FLAGS.log_device_placement)) as mon_sess:
      while not mon_sess.should_stop():
        mon_sess.run(train_op)