def __init__()

in tcav/model.py [0:0]

• 11 lines of code
• 5 McCabe index (conditional complexity)

  def __init__(self, model_path=None, node_dict=None):
    """Initialize the wrapper.

    Optionally create a session, load
    the model from model_path to this session, and map the
    input/output and bottleneck tensors.

    Args:
      model_path: one of the following: 1) Directory path to checkpoint 2)
        Directory path to SavedModel 3) File path to frozen graph.pb 4) File
        path to frozen graph.pbtxt
      node_dict: mapping from a short name to full input/output and bottleneck
        tensor names. Users should pass 'input' and 'prediction'
        as keys and the corresponding input and prediction tensor
        names as values in node_dict. Users can additionally pass bottleneck
        tensor names for which gradient Ops will be added later.
    """
    # A dictionary of bottleneck tensors.
    self.bottlenecks_tensors = None
    # A dictionary of input, 'logit' and prediction tensors.
    self.ends = None
    # The model name string.
    self.model_name = None
    # a place holder for index of the neuron/class of interest.
    # usually defined under the graph. For example:
    # with g.as_default():
    #   self.tf.placeholder(tf.int64, shape=[None])
    self.y_input = None
    # The tensor representing the loss (used to calculate derivative).
    self.loss = None
    # If tensors in the loaded graph are prefixed with 'import/'
    self.import_prefix = False

    if model_path:
      self._try_loading_model(model_path)
    if node_dict:
      self._find_ends_and_bottleneck_tensors(node_dict)