train-final/models.py
def parse_netarch_model(parse_lambda_dir, depth_multiplier=None):
  """Creates the RNAS found model. No need to hard-code
  model, it parses the output of previous search

  Args:
    depth_multiplier: multiplier to number of filters per layer.

  Returns:
    blocks_args: a list of BlocksArgs for internal MnasNet blocks.
    global_params: GlobalParams, global parameters for the model.
  """
  # Loading too much data is slow...
  tf_size_guidance = {
        'compressedHistograms': 10,
        'images': 0,
        'scalars': 100,
        'histograms': 1
  }
  indicator_values = parse_netarch.parse_indicators_single_path_nas(parse_lambda_dir, tf_size_guidance)
  network = parse_netarch.encode_single_path_nas_arch(indicator_values)
  parse_netarch.print_net(network)
  blocks_args = parse_netarch.convnet_encoder(network)
  parse_netarch.print_encoded_net(blocks_args)

  decoder = MnasNetDecoder()
  global_params = model_def.GlobalParams(
      batch_norm_momentum=0.99,
      batch_norm_epsilon=1e-3,
      dropout_rate=0.2,
      data_format='channels_last',
      num_classes=1000,
      depth_multiplier=depth_multiplier,
      depth_divisor=8,
      min_depth=None)
  return decoder.decode(blocks_args), global_params
Creates the RNAS found model. No need to hard-code
  model, it parses the output of previous search

  Args:
    depth_multiplier: multiplier to number of filters per layer.

  Returns:
    blocks_args: a list of BlocksArgs for internal MnasNet blocks.
    global_params: GlobalParams, global parameters for the model.
  
Creates the RNAS found model. No need to hard-code
model, it parses the output of previous search
Creates the RNAS found model . No need to hard - code model it parses the output of previous search
def parse_netarch_model(parse_lambda_dir, depth_multiplier=None):
  tf_size_guidance = {
        'compressedHistograms': 10,
        'images': 0,
        'scalars': 100,
        'histograms': 1
  }
  indicator_values = parse_netarch.parse_indicators_single_path_nas(parse_lambda_dir, tf_size_guidance)
  network = parse_netarch.encode_single_path_nas_arch(indicator_values)
  parse_netarch.print_net(network)
  blocks_args = parse_netarch.convnet_encoder(network)
  parse_netarch.print_encoded_net(blocks_args)
  decoder = MnasNetDecoder()
  global_params = model_def.GlobalParams(
      batch_norm_momentum=0.99,
      batch_norm_epsilon=1e-3,
      dropout_rate=0.2,
      data_format='channels_last',
      num_classes=1000,
      depth_multiplier=depth_multiplier,
      depth_divisor=8,
      min_depth=None)
  return decoder.decode(blocks_args), global_params
def parse_netarch_model ( parse_lambda_dir , depth_multiplier = None ) : tf_size_guidance = { 'compressedHistograms' : 10 , 'images' : 0 , 'scalars' : 100 , 'histograms' : 1 } indicator_values = parse_netarch . parse_indicators_single_path_nas ( parse_lambda_dir , tf_size_guidance ) network = parse_netarch . encode_single_path_nas_arch ( indicator_values ) parse_netarch . print_net ( network ) blocks_args = parse_netarch . convnet_encoder ( network ) parse_netarch . print_encoded_net ( blocks_args ) decoder = MnasNetDecoder ( ) global_params = model_def . GlobalParams ( batch_norm_momentum = 0.99 , batch_norm_epsilon = 1e-3 , dropout_rate = 0.2 , data_format = 'channels_last' , num_classes = 1000 , depth_multiplier = depth_multiplier , depth_divisor = 8 , min_depth = None ) return decoder . decode ( blocks_args ) , global_params
Creates the RNAS found model.
Creates the RNAS found model .
"""Creates the RNAS found model. No need to hard-code
  model, it parses the output of previous search

  Args:
    depth_multiplier: multiplier to number of filters per layer.

  Returns:
    blocks_args: a list of BlocksArgs for internal MnasNet blocks.
    global_params: GlobalParams, global parameters for the model.
  """ # Loading too much data is slow...
a list of BlocksArgs for internal MnasNet blocks.
global_params: GlobalParams, global parameters for the model.
a list of BlocksArgs for internal MnasNet blocks . global_params : GlobalParams global parameters for the model .
blocks_args
parse_lambda_dir
depth_multiplier
multiplier to number of filters per layer.
multiplier to number of filters per layer .