Metadata-Version: 1.0
Name: bigml-sensenet
Version: 0.1.4
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
        and an optional map of settings and return a `tf.keras.Model` based on
        these arguments.
        
        ### Deepnets
        
        The class `sensenet.models.deepnet.DeepnetWrapper` exposes this
        functionality for BigML deepnet models.  To instantiate one of these,
        pass the model specification and the map of additional settings:
        
        ```
        model = sensenet.models.deepnet.DeepnetWrapper(model_dict, settings)
        ```
        
        Again, `model_dict` is typically the relevant section from the
        downloaded BigML model, and `settings` is a map of optional settings
        which may contain:
        
        - `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`) or a string
          containing the raw image bytes (`bytes`)
        
        Once instantiated, you can use the model to make predictions:
        
        ```
        model.predict([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.
        
        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.
        
Platform: UNKNOWN
