BigML Bindings: 101 - Images ClassificationΒΆ

Following the schema described in the prediction workflow, document, this is the code snippet that shows the minimal workflow to create a deepnet from an images dataset and produce a single prediction.

from bigml.api import BigML
# step 0: creating a connection to the service (default credentials)
api = BigML()
# step 1: creating a source from the data in your local
# "data/images/" file. The file contains two folders, each
# of which contains a collection of images. The folder name will be used
# as label for each image it contains.
# The source is created disabling image analysis, as we want the deepnet
# model to take care of extracting the features. If not said otherwise,
# the analysis would be enabled and features like the histogram of
# gradients would be extracted to become part of the resulting dataset.
source = api.create_source("data/images/",
    args={"image_analysis": {"enabled": False}})
# waiting for the source to be finished. Results will be stored in `source`
# and the new ``image_id`` and ``label`` fields will be generated in the
# source
# step 3: creating a dataset from the previously created `source`
dataset = api.create_dataset(source)
# waiting for the dataset to be finished
# step 5: creating a deepnet
deepnet = api.create_deepnet(dataset)
# waiting for the deepnet to be finished
# the new input data to predict for should contain the path to the
# new image to be used for testing
input_data = {"image_id": "data/images/f2/fruits2.png"}
# creating a single prediction: The image file is uploaded to BigML,
# a new source is created for it and its ID is used as value
# for the ``image_id`` field in the input data to generate the prediction
prediction = api.create_prediction(deepnet, input_data)

In the previous code, the api.ok method is used to wait for the resource to be finished before calling the next create method or accessing the resource properties. In the first case, we could skip that api.ok`call because the next `create method would internally do the waiting when needed.

You can also predict locally using the Deepnet class in the deepnet module. A simple example of that is:

from bigml.deepnet import Deepnet
local_deepnet = Deepnet("deepnet/5968ec46983efc21b000001c")
# predicting for some input data
input_data = {"image_id": "data/images/f2/fruits2.png"}