Metadata-Version: 2.1
Name: bigml-sensenet
Version: 0.2.7
Summary: Network builder for bigml deepnet topologies
Home-page: http://bigml.com/
Author: BigML Team
Author-email: team@bigml.com
License: UNKNOWN
Description: # BigML Sense/Net
        
        Sense/Net is a BigML interface to Tensorflow, which takes a network
        specification as a dictionary (read from BigML's JSON model format)
        and instantiates a TensorFlow compute graph based on that
        specification.
        
        ## Entry Points
        
        The library is meant, in general, to take a BigML model specification
        as a JSON document, and an optional map of settings and return a
        lightweight wrapper around a `tf.keras.Model` based on these
        arguments.  The wrapper creation function can be found in
        `sensenet.models.wrappers.create_model`
        
        ## Model Instantiation
        
        To instantiate a model, pass the model specification and the dict
        of additional, optional settings to `create_model`.  For example:
        
        ```
        model = create_model(a_dict, settings={'image_path_prefix': 'images/path/'})
        ```
        
        Again, `a_dict` is typically a downloaded BigML model, read into a
        python dictionary via `json.load` or similar.
        
        For image models, `settings` is a dict of optional settings which may
        contain:
        
        - `bounding_box_threshold`: For object detection models only, the
          minimal score that an object can have and still be surfaced to the
          user as part of the output.  The default is 0.5, and lower the score
          will have the effect of more (possibly spurious) boxes identified in
          each input image.
        
        - `image_path_prefix`: A string directory indicating the path where
          images are to be found for image predictions.  When an image path is
          passed at prediction time, this string is prepended to the given
          path.
        
        - `input_image_format`: The format of input images for the network.
          This can be either an image file on disk (`'file'`, the default), a
          string containing the raw, undecoded, image file bytes (`'bytes'`)
          or the decompressed image data represented as a nested python list,
          numpy array, or TensorFlow tensor of pixel values
          (`'pixel_values'`).
        
        - `iou_threshold`: A threshold indicating the amount of overlap boxes
          predicting the same class should have before they are considered to
          be bounding the same object.  The default is 0.5, and lower values
          have the effect of eliminating boxes which would otherwise have been
          surfaced to the user.
        
        - `max_objects`: The maximum number of bounding boxes to return for
          each image.  The default is 32.
        
        
        ## Usage
        
        Once instantiated, you can use the model to make predictions by using
        the returned model as a function, like so:
        
        ```
        prediction = model([1.0, 2.0, 3.0])
        ```
        
        The input point or points must be a list (or nested list) containing
        the input data for each point, in the order implied by
        `model._preprocessors`.  Categorical and image variables should be
        passed as strings, where the image is either a path to the image on
        disk, or the raw compressed image bytes.
        
        For classification or regression models, the function returns a numpy
        array where each row is the model's prediction for each input point.
        For classification models, there will be a probability for each class
        in each row.  For regression models, each row will contain only a
        single entry.
        
        For object detection models, the input should always be a single image
        (again, either as a file path, compressed byte string, or an array of
        pixel values, depending on the settings map, and the result will be
        list of detected boxes, each one represented as a dictionary.  For
        example:
        
        ```
        In [5]: model('pizza_people.jpg')
        Out[5]:
        [{'box': [16, 317, 283, 414], 'label': 'pizza', 'score': 0.9726969599723816},
         {'box': [323, 274, 414, 332], 'label': 'pizza', 'score': 0.7364346981048584},
         {'box': [158, 29, 400, 327], 'label': 'person', 'score': 0.6204285025596619},
         {'box': [15, 34, 283, 336], 'label': 'person', 'score': 0.5346986055374146},
         {'box': [311, 23, 416, 255], 'label': 'person', 'score': 0.41961848735809326}]
        ```
        
        The `box` array contains the coordinates of the detected box, as `x1,
        y1, x2, y2`, where those coordinates represent the upper-left and
        lower-right corners of each bounding box, in a coordinate system with
        (0, 0) at the upper-left of the input image.  The `score` is the rough
        probability that the object has been correctly identified, and the
        `label` is the detected class of the object.
        
Platform: UNKNOWN
Description-Content-Type: text/markdown
