bigml-sensenet 0.1.4

Network builder for bigml deepnet topologies

# 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.