train-final/models.py
def build_model(images, model_name, training, override_params=None,
        parse_search_dir=None):
  """A helper functiion to creates a ConvNet model and returns predicted logits.

  Args:
    images: input images tensor.
    model_name: string, the model name of a pre-defined MnasNet.
    training: boolean, whether the model is constructed for training.
    override_params: A dictionary of params for overriding. Fields must exist in
      model_def.GlobalParams.

  Returns:
    logits: the logits tensor of classes.
    endpoints: the endpoints for each layer.
  Raises:
    When model_name specified an undefined model, raises NotImplementedError.
    When override_params has invalid fields, raises ValueError.
  """
  assert isinstance(images, tf.Tensor)
  if model_name == 'single-path':
    assert parse_search_dir is not None
    blocks_args, global_params = parse_netarch_model(parse_search_dir)
  else:
    raise NotImplementedError('model name is not pre-defined: %s' % model_name)

  if override_params:
    # ValueError will be raised here if override_params has fields not included
    # in global_params.
    global_params = global_params._replace(**override_params)

  with tf.variable_scope(model_name):
    model = model_def.MnasNetModel(blocks_args, global_params)
    logits = model(images, training=training)

  logits = tf.identity(logits, 'logits')
  return logits, model.endpoints
A helper functiion to creates a ConvNet model and returns predicted logits.

  Args:
    images: input images tensor.
    model_name: string, the model name of a pre-defined MnasNet.
    training: boolean, whether the model is constructed for training.
    override_params: A dictionary of params for overriding. Fields must exist in
      model_def.GlobalParams.

  Returns:
    logits: the logits tensor of classes.
    endpoints: the endpoints for each layer.
  Raises:
    When model_name specified an undefined model, raises NotImplementedError.
    When override_params has invalid fields, raises ValueError.
  
A helper functiion to creates a ConvNet model and returns predicted logits.
Args:
images: input images tensor.
model_name: string, the model name of a pre-defined MnasNet.
training: boolean, whether the model is constructed for training.
override_params: A dictionary of params for overriding.

the logits tensor of classes.
endpoints: the endpoints for each layer.
Raises:
When model_name specified an undefined model, raises NotImplementedError.
When override_params has invalid fields, raises ValueError.
A helper functiion to creates a ConvNet model and returns predicted logits . Args : images : input images tensor . model_name : string the model name of a pre - defined MnasNet . training : boolean whether the model is constructed for training . override_params : A dictionary of params for overriding . the logits tensor of classes . endpoints : the endpoints for each layer . Raises : When model_name specified an undefined model raises NotImplementedError . When override_params has invalid fields raises ValueError .
def build_model(images, model_name, training, override_params=None,
        parse_search_dir=None):
  assert isinstance(images, tf.Tensor)
  if model_name == 'single-path':
    assert parse_search_dir is not None
    blocks_args, global_params = parse_netarch_model(parse_search_dir)
  else:
    raise NotImplementedError('model name is not pre-defined: %s' % model_name)
  if override_params:
    global_params = global_params._replace(**override_params)
  with tf.variable_scope(model_name):
    model = model_def.MnasNetModel(blocks_args, global_params)
    logits = model(images, training=training)
  logits = tf.identity(logits, 'logits')
  return logits, model.endpoints
def build_model ( images , model_name , training , override_params = None , parse_search_dir = None ) : assert isinstance ( images , tf . Tensor ) if model_name == 'single-path' : assert parse_search_dir is not None blocks_args , global_params = parse_netarch_model ( parse_search_dir ) else : raise NotImplementedError ( 'model name is not pre-defined: %s' % model_name ) if override_params : global_params = global_params . _replace ( ** override_params ) with tf . variable_scope ( model_name ) : model = model_def . MnasNetModel ( blocks_args , global_params ) logits = model ( images , training = training ) logits = tf . identity ( logits , 'logits' ) return logits , model . endpoints
A helper functiion to creates a ConvNet model and returns predicted logits.
A helper functiion to creates a ConvNet model and returns predicted logits .
"""A helper functiion to creates a ConvNet model and returns predicted logits.

  Args:
    images: input images tensor.
    model_name: string, the model name of a pre-defined MnasNet.
    training: boolean, whether the model is constructed for training.
    override_params: A dictionary of params for overriding. Fields must exist in
      model_def.GlobalParams.

  Returns:
    logits: the logits tensor of classes.
    endpoints: the endpoints for each layer.
  Raises:
    When model_name specified an undefined model, raises NotImplementedError.
    When override_params has invalid fields, raises ValueError.
  """ # ValueError will be raised here if override_params has fields not included # in global_params.
images
model_name
training
override_params
parse_search_dir