MXNet sequential classification model example

An example of a sequential network that does a binary classification on the credit-g dataset.

import mxnet
import mxnet.gluon

import openml
import openml.extensions.mxnet

import logging

Enable logging in order to observe the progress while running the example.

openml.config.logger.setLevel(logging.DEBUG)
openml.extensions.mxnet.config.logger.setLevel(logging.DEBUG)

Define a HybridSequential container

model = mxnet.gluon.nn.HybridSequential()

Add the layers to the HybridSequential container

with model.name_scope():
    model.add(mxnet.gluon.nn.BatchNorm())
    model.add(mxnet.gluon.nn.Dense(units=1024, activation="relu"))
    model.add(mxnet.gluon.nn.Dropout(rate=0.4))
    model.add(mxnet.gluon.nn.Dense(units=2))

Enable hybrid execution

model.hybridize()

Retrieve the credit_g classification task from OpenML

task = openml.tasks.get_task(31)

Run the model on the task (requires an API key).

run = openml.runs.run_model_on_task(model, task, avoid_duplicate_runs=False)
# Publish the Run on OpenML
run.publish()

print('URL for run: %s/run/%d' % (openml.config.server, run.run_id))