Python nets.mobilenet.mobilenet.training_scope() Examples

The following are 30 code examples of nets.mobilenet.mobilenet.training_scope(). You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may also want to check out all available functions/classes of the module nets.mobilenet.mobilenet , or try the search function .
Example #1
Source File: mobilenet_v2.py    From SENet-tensorflow-slim with MIT License 6 votes vote down vote up 
def training_scope(**kwargs):
  """Defines MobilenetV2 training scope.

  Usage:
     with tf.contrib.slim.arg_scope(mobilenet_v2.training_scope()):
       logits, endpoints = mobilenet_v2.mobilenet(input_tensor)

  with slim.

  Args:
    **kwargs: Passed to mobilenet.training_scope. The following parameters
    are supported:
      weight_decay- The weight decay to use for regularizing the model.
      stddev-  Standard deviation for initialization, if negative uses xavier.
      dropout_keep_prob- dropout keep probability
      bn_decay- decay for the batch norm moving averages.

  Returns:
    An `arg_scope` to use for the mobilenet v2 model.
  """
  return lib.training_scope(**kwargs)
Example #2
Source File: mobilenet_v2.py    From tf_ctpn with MIT License 6 votes vote down vote up 
def training_scope(**kwargs):
    """Defines MobilenetV2 training scope.
    Usage:
       with tf.contrib.slim.arg_scope(mobilenet_v2.training_scope()):
         logits, endpoints = mobilenet_v2.mobilenet(input_tensor)
    with slim.
    Args:
      **kwargs: Passed to mobilenet.training_scope. The following parameters
      are supported:
        weight_decay- The weight decay to use for regularizing the model.
        stddev-  Standard deviation for initialization, if negative uses xavier.
        dropout_keep_prob- dropout keep probability
        bn_decay- decay for the batch norm moving averages.
    Returns:
      An `arg_scope` to use for the mobilenet v2 model.
    """
    return lib.training_scope(**kwargs)
Example #3
Source File: mobilenet_v2.py    From CVTron with Apache License 2.0 6 votes vote down vote up 
def training_scope(**kwargs):
  """Defines MobilenetV2 training scope.

  Usage:
     with tf.contrib.slim.arg_scope(mobilenet_v2.training_scope()):
       logits, endpoints = mobilenet_v2.mobilenet(input_tensor)

  with slim.

  Args:
    **kwargs: Passed to mobilenet.training_scope. The following parameters
    are supported:
      weight_decay- The weight decay to use for regularizing the model.
      stddev-  Standard deviation for initialization, if negative uses xavier.
      dropout_keep_prob- dropout keep probability
      bn_decay- decay for the batch norm moving averages.

  Returns:
    An `arg_scope` to use for the mobilenet v2 model.
  """
  return lib.training_scope(**kwargs)
Example #4
Source File: mobilenet_v2.py    From multilabel-image-classification-tensorflow with MIT License 6 votes vote down vote up 
def training_scope(**kwargs):
  """Defines MobilenetV2 training scope.

  Usage:
     with tf.contrib.slim.arg_scope(mobilenet_v2.training_scope()):
       logits, endpoints = mobilenet_v2.mobilenet(input_tensor)

  with slim.

  Args:
    **kwargs: Passed to mobilenet.training_scope. The following parameters
    are supported:
      weight_decay- The weight decay to use for regularizing the model.
      stddev-  Standard deviation for initialization, if negative uses xavier.
      dropout_keep_prob- dropout keep probability
      bn_decay- decay for the batch norm moving averages.

  Returns:
    An `arg_scope` to use for the mobilenet v2 model.
  """
  return lib.training_scope(**kwargs)
Example #5
Source File: mobilenet_v2.py    From MAX-Object-Detector with Apache License 2.0 6 votes vote down vote up 
def training_scope(**kwargs):
  """Defines MobilenetV2 training scope.

  Usage:
     with tf.contrib.slim.arg_scope(mobilenet_v2.training_scope()):
       logits, endpoints = mobilenet_v2.mobilenet(input_tensor)

  with slim.

  Args:
    **kwargs: Passed to mobilenet.training_scope. The following parameters
    are supported:
      weight_decay- The weight decay to use for regularizing the model.
      stddev-  Standard deviation for initialization, if negative uses xavier.
      dropout_keep_prob- dropout keep probability
      bn_decay- decay for the batch norm moving averages.

  Returns:
    An `arg_scope` to use for the mobilenet v2 model.
  """
  return lib.training_scope(**kwargs)
Example #6
Source File: mobilenet_v2.py    From edafa with MIT License 6 votes vote down vote up 
def training_scope(**kwargs):
  """Defines MobilenetV2 training scope.

  Usage:
     with tf.contrib.slim.arg_scope(mobilenet_v2.training_scope()):
       logits, endpoints = mobilenet_v2.mobilenet(input_tensor)

  with slim.

  Args:
    **kwargs: Passed to mobilenet.training_scope. The following parameters
    are supported:
      weight_decay- The weight decay to use for regularizing the model.
      stddev-  Standard deviation for initialization, if negative uses xavier.
      dropout_keep_prob- dropout keep probability
      bn_decay- decay for the batch norm moving averages.

  Returns:
    An `arg_scope` to use for the mobilenet v2 model.
  """
  return lib.training_scope(**kwargs)
Example #7
Source File: mobilenet_v2.py    From MAX-Image-Segmenter with Apache License 2.0 6 votes vote down vote up 
def training_scope(**kwargs):
  """Defines MobilenetV2 training scope.

  Usage:
     with tf.contrib.slim.arg_scope(mobilenet_v2.training_scope()):
       logits, endpoints = mobilenet_v2.mobilenet(input_tensor)

  with slim.

  Args:
    **kwargs: Passed to mobilenet.training_scope. The following parameters
    are supported:
      weight_decay- The weight decay to use for regularizing the model.
      stddev-  Standard deviation for initialization, if negative uses xavier.
      dropout_keep_prob- dropout keep probability
      bn_decay- decay for the batch norm moving averages.

  Returns:
    An `arg_scope` to use for the mobilenet v2 model.
  """
  return lib.training_scope(**kwargs)
Example #8
Source File: mobilenet_v2.py    From models with Apache License 2.0 6 votes vote down vote up 
def training_scope(**kwargs):
  """Defines MobilenetV2 training scope.

  Usage:
     with slim.arg_scope(mobilenet_v2.training_scope()):
       logits, endpoints = mobilenet_v2.mobilenet(input_tensor)

  Args:
    **kwargs: Passed to mobilenet.training_scope. The following parameters
    are supported:
      weight_decay- The weight decay to use for regularizing the model.
      stddev-  Standard deviation for initialization, if negative uses xavier.
      dropout_keep_prob- dropout keep probability
      bn_decay- decay for the batch norm moving averages.

  Returns:
    An `arg_scope` to use for the mobilenet v2 model.
  """
  return lib.training_scope(**kwargs)
Example #9
Source File: mobilenet_v2.py    From CBAM-tensorflow-slim with MIT License 6 votes vote down vote up 
def training_scope(**kwargs):
  """Defines MobilenetV2 training scope.

  Usage:
     with tf.contrib.slim.arg_scope(mobilenet_v2.training_scope()):
       logits, endpoints = mobilenet_v2.mobilenet(input_tensor)

  with slim.

  Args:
    **kwargs: Passed to mobilenet.training_scope. The following parameters
    are supported:
      weight_decay- The weight decay to use for regularizing the model.
      stddev-  Standard deviation for initialization, if negative uses xavier.
      dropout_keep_prob- dropout keep probability
      bn_decay- decay for the batch norm moving averages.

  Returns:
    An `arg_scope` to use for the mobilenet v2 model.
  """
  return lib.training_scope(**kwargs)
Example #10
Source File: mobilenet_v2.py    From DeepLab_v3 with MIT License 6 votes vote down vote up 
def training_scope(**kwargs):
  """Defines MobilenetV2 training scope.

  Usage:
     with tf.contrib.slim.arg_scope(mobilenet_v2.training_scope()):
       logits, endpoints = mobilenet_v2.mobilenet(input_tensor)

  with slim.

  Args:
    **kwargs: Passed to mobilenet.training_scope. The following parameters
    are supported:
      weight_decay- The weight decay to use for regularizing the model.
      stddev-  Standard deviation for initialization, if negative uses xavier.
      dropout_keep_prob- dropout keep probability
      bn_decay- decay for the batch norm moving averages.

  Returns:
    An `arg_scope` to use for the mobilenet v2 model.
  """
  return lib.training_scope(**kwargs)
Example #11
Source File: mobilenet_v2.py    From Gun-Detector with Apache License 2.0 6 votes vote down vote up 
def training_scope(**kwargs):
  """Defines MobilenetV2 training scope.

  Usage:
     with tf.contrib.slim.arg_scope(mobilenet_v2.training_scope()):
       logits, endpoints = mobilenet_v2.mobilenet(input_tensor)

  with slim.

  Args:
    **kwargs: Passed to mobilenet.training_scope. The following parameters
    are supported:
      weight_decay- The weight decay to use for regularizing the model.
      stddev-  Standard deviation for initialization, if negative uses xavier.
      dropout_keep_prob- dropout keep probability
      bn_decay- decay for the batch norm moving averages.

  Returns:
    An `arg_scope` to use for the mobilenet v2 model.
  """
  return lib.training_scope(**kwargs)
Example #12
Source File: mobilenet_v2.py    From g-tensorflow-models with Apache License 2.0 6 votes vote down vote up 
def training_scope(**kwargs):
  """Defines MobilenetV2 training scope.

  Usage:
     with tf.contrib.slim.arg_scope(mobilenet_v2.training_scope()):
       logits, endpoints = mobilenet_v2.mobilenet(input_tensor)

  with slim.

  Args:
    **kwargs: Passed to mobilenet.training_scope. The following parameters
    are supported:
      weight_decay- The weight decay to use for regularizing the model.
      stddev-  Standard deviation for initialization, if negative uses xavier.
      dropout_keep_prob- dropout keep probability
      bn_decay- decay for the batch norm moving averages.

  Returns:
    An `arg_scope` to use for the mobilenet v2 model.
  """
  return lib.training_scope(**kwargs)
Example #13
Source File: mobilenet_v2.py    From BMW-TensorFlow-Training-GUI with Apache License 2.0 6 votes vote down vote up 
def training_scope(**kwargs):
  """Defines MobilenetV2 training scope.

  Usage:
     with tf.contrib.slim.arg_scope(mobilenet_v2.training_scope()):
       logits, endpoints = mobilenet_v2.mobilenet(input_tensor)

  with slim.

  Args:
    **kwargs: Passed to mobilenet.training_scope. The following parameters
    are supported:
      weight_decay- The weight decay to use for regularizing the model.
      stddev-  Standard deviation for initialization, if negative uses xavier.
      dropout_keep_prob- dropout keep probability
      bn_decay- decay for the batch norm moving averages.

  Returns:
    An `arg_scope` to use for the mobilenet v2 model.
  """
  return lib.training_scope(**kwargs)
Example #14
Source File: mobilenet_v2_test.py    From g-tensorflow-models with Apache License 2.0 5 votes vote down vote up 
def testBatchNormScopeDoesHasIsTrainingWhenItsNotNone(self):
    sc = mobilenet.training_scope(is_training=False)
    self.assertIn('is_training', sc[slim.arg_scope_func_key(slim.batch_norm)])
    sc = mobilenet.training_scope(is_training=True)
    self.assertIn('is_training', sc[slim.arg_scope_func_key(slim.batch_norm)])
    sc = mobilenet.training_scope()
    self.assertIn('is_training', sc[slim.arg_scope_func_key(slim.batch_norm)])
Example #15
Source File: mobilenet_v2_test.py    From SENet-tensorflow-slim with MIT License 5 votes vote down vote up 
def testBatchNormScopeDoesHasIsTrainingWhenItsNotNone(self):
    sc = mobilenet.training_scope(is_training=False)
    self.assertIn('is_training', sc[slim.arg_scope_func_key(slim.batch_norm)])
    sc = mobilenet.training_scope(is_training=True)
    self.assertIn('is_training', sc[slim.arg_scope_func_key(slim.batch_norm)])
    sc = mobilenet.training_scope()
    self.assertIn('is_training', sc[slim.arg_scope_func_key(slim.batch_norm)])
Example #16
Source File: mobilenet_v2_test.py    From MAX-Object-Detector with Apache License 2.0 5 votes vote down vote up 
def testBatchNormScopeDoesNotHaveIsTrainingWhenItsSetToNone(self):
    sc = mobilenet.training_scope(is_training=None)
    self.assertNotIn('is_training', sc[slim.arg_scope_func_key(
        slim.batch_norm)])
Example #17
Source File: mobilenet_v2_test.py    From MAX-Object-Detector with Apache License 2.0 5 votes vote down vote up 
def testBatchNormScopeDoesHasIsTrainingWhenItsNotNone(self):
    sc = mobilenet.training_scope(is_training=False)
    self.assertIn('is_training', sc[slim.arg_scope_func_key(slim.batch_norm)])
    sc = mobilenet.training_scope(is_training=True)
    self.assertIn('is_training', sc[slim.arg_scope_func_key(slim.batch_norm)])
    sc = mobilenet.training_scope()
    self.assertIn('is_training', sc[slim.arg_scope_func_key(slim.batch_norm)])
Example #18
Source File: mobilenet_v2_test.py    From g-tensorflow-models with Apache License 2.0 5 votes vote down vote up 
def testBatchNormScopeDoesNotHaveIsTrainingWhenItsSetToNone(self):
    sc = mobilenet.training_scope(is_training=None)
    self.assertNotIn('is_training', sc[slim.arg_scope_func_key(
        slim.batch_norm)])
Example #19
Source File: mobilenet_v2_test.py    From BMW-TensorFlow-Training-GUI with Apache License 2.0 5 votes vote down vote up 
def testBatchNormScopeDoesHasIsTrainingWhenItsNotNone(self):
    sc = mobilenet.training_scope(is_training=False)
    self.assertIn('is_training', sc[slim.arg_scope_func_key(slim.batch_norm)])
    sc = mobilenet.training_scope(is_training=True)
    self.assertIn('is_training', sc[slim.arg_scope_func_key(slim.batch_norm)])
    sc = mobilenet.training_scope()
    self.assertIn('is_training', sc[slim.arg_scope_func_key(slim.batch_norm)])
Example #20
Source File: mobilenet_v2_test.py    From models with Apache License 2.0 5 votes vote down vote up 
def testBatchNormScopeDoesNotHaveIsTrainingWhenItsSetToNone(self):
    sc = mobilenet.training_scope(is_training=None)
    self.assertNotIn('is_training', sc[slim.arg_scope_func_key(
        slim.batch_norm)])
Example #21
Source File: mobilenet_v2_test.py    From models with Apache License 2.0 5 votes vote down vote up 
def testBatchNormScopeDoesHasIsTrainingWhenItsNotNone(self):
    sc = mobilenet.training_scope(is_training=False)
    self.assertIn('is_training', sc[slim.arg_scope_func_key(slim.batch_norm)])
    sc = mobilenet.training_scope(is_training=True)
    self.assertIn('is_training', sc[slim.arg_scope_func_key(slim.batch_norm)])
    sc = mobilenet.training_scope()
    self.assertIn('is_training', sc[slim.arg_scope_func_key(slim.batch_norm)])
Example #22
Source File: mobilenet_v2_test.py    From multilabel-image-classification-tensorflow with MIT License 5 votes vote down vote up 
def testBatchNormScopeDoesNotHaveIsTrainingWhenItsSetToNone(self):
    sc = mobilenet.training_scope(is_training=None)
    self.assertNotIn('is_training', sc[slim.arg_scope_func_key(
        slim.batch_norm)])
Example #23
Source File: mobilenet_v2_test.py    From multilabel-image-classification-tensorflow with MIT License 5 votes vote down vote up 
def testBatchNormScopeDoesHasIsTrainingWhenItsNotNone(self):
    sc = mobilenet.training_scope(is_training=False)
    self.assertIn('is_training', sc[slim.arg_scope_func_key(slim.batch_norm)])
    sc = mobilenet.training_scope(is_training=True)
    self.assertIn('is_training', sc[slim.arg_scope_func_key(slim.batch_norm)])
    sc = mobilenet.training_scope()
    self.assertIn('is_training', sc[slim.arg_scope_func_key(slim.batch_norm)])
Example #24
Source File: mobilenet_v2_test.py    From SENet-tensorflow-slim with MIT License 5 votes vote down vote up 
def testBatchNormScopeDoesNotHaveIsTrainingWhenItsSetToNone(self):
    sc = mobilenet.training_scope(is_training=None)
    self.assertNotIn('is_training', sc[slim.arg_scope_func_key(
        slim.batch_norm)])
Example #25
Source File: mobilenet_v2_test.py    From MAX-Image-Segmenter with Apache License 2.0 5 votes vote down vote up 
def testBatchNormScopeDoesHasIsTrainingWhenItsNotNone(self):
    sc = mobilenet.training_scope(is_training=False)
    self.assertIn('is_training', sc[slim.arg_scope_func_key(slim.batch_norm)])
    sc = mobilenet.training_scope(is_training=True)
    self.assertIn('is_training', sc[slim.arg_scope_func_key(slim.batch_norm)])
    sc = mobilenet.training_scope()
    self.assertIn('is_training', sc[slim.arg_scope_func_key(slim.batch_norm)])
Example #26
Source File: mobilenet_v2_test.py    From MAX-Image-Segmenter with Apache License 2.0 5 votes vote down vote up 
def testBatchNormScopeDoesNotHaveIsTrainingWhenItsSetToNone(self):
    sc = mobilenet.training_scope(is_training=None)
    self.assertNotIn('is_training', sc[slim.arg_scope_func_key(
        slim.batch_norm)])
Example #27
Source File: mobilenet_v2_test.py    From BMW-TensorFlow-Training-GUI with Apache License 2.0 5 votes vote down vote up 
def testBatchNormScopeDoesNotHaveIsTrainingWhenItsSetToNone(self):
    sc = mobilenet.training_scope(is_training=None)
    self.assertNotIn('is_training', sc[slim.arg_scope_func_key(
        slim.batch_norm)])
Example #28
Source File: mobilenet_v2_test.py    From Gun-Detector with Apache License 2.0 5 votes vote down vote up 
def testBatchNormScopeDoesHasIsTrainingWhenItsNotNone(self):
    sc = mobilenet.training_scope(is_training=False)
    self.assertIn('is_training', sc[slim.arg_scope_func_key(slim.batch_norm)])
    sc = mobilenet.training_scope(is_training=True)
    self.assertIn('is_training', sc[slim.arg_scope_func_key(slim.batch_norm)])
    sc = mobilenet.training_scope()
    self.assertIn('is_training', sc[slim.arg_scope_func_key(slim.batch_norm)])
Example #29
Source File: mobilenet_v2_test.py    From Gun-Detector with Apache License 2.0 5 votes vote down vote up 
def testBatchNormScopeDoesNotHaveIsTrainingWhenItsSetToNone(self):
    sc = mobilenet.training_scope(is_training=None)
    self.assertNotIn('is_training', sc[slim.arg_scope_func_key(
        slim.batch_norm)])
Example #30
Source File: mobilenet_v2_test.py    From CBAM-tensorflow-slim with MIT License 5 votes vote down vote up 
def testBatchNormScopeDoesHasIsTrainingWhenItsNotNone(self):
    sc = mobilenet.training_scope(is_training=False)
    self.assertIn('is_training', sc[slim.arg_scope_func_key(slim.batch_norm)])
    sc = mobilenet.training_scope(is_training=True)
    self.assertIn('is_training', sc[slim.arg_scope_func_key(slim.batch_norm)])
    sc = mobilenet.training_scope()
    self.assertIn('is_training', sc[slim.arg_scope_func_key(slim.batch_norm)])