The Census Income Data Set contains over 48,000 samples with attributes including age, occupation, education, and income (a binary label, either >50K or <=50K). The dataset is split into roughly 32,000 training and 16,000 testing samples.
Here, we use the wide and deep model to predict the income labels. The wide model is able to memorize interactions with data with a large number of features but not able to generalize these learned interactions on new data. The deep model generalizes well but is unable to learn exceptions within the data. The wide and deep model combines the two models and is able to generalize while learning exceptions.
For the purposes of this example code, the Census Income Data Set was chosen to allow the model to train in a reasonable amount of time. You'll notice that the deep model performs almost as well as the wide and deep model on this dataset. The wide and deep model truly shines on larger data sets with high-cardinality features, where each feature has millions/billions of unique possible values (which is the specialty of the wide model).
The code sample in this directory uses the high level tf.estimator.Estimator API. This API is great for fast iteration and quickly adapting models to your own datasets without major code overhauls. It allows you to move from single-worker training to distributed training, and it makes it easy to export model binaries for prediction.
The input function for the Estimator uses tf.contrib.data.TextLineDataset, which creates a Dataset object. The Dataset API makes it easy to apply transformations (map, batch, shuffle, etc.) to the data. Read more here.
The Estimator and Dataset APIs are both highly encouraged for fast development and efficient training.
The Census Income Data Set that this sample uses for training is hosted by the UC Irvine Machine Learning Repository. We have provided a script that downloads and cleans the necessary files.
python data_download.py
This will download the files to /tmp/census_data. To change the directory, set the --data_dir flag.
You can run the code locally as follows:
python wide_deep.py
The model is saved to /tmp/census_model by default, which can be changed using the --model_dir flag.
To run the wide or deep-only models, set the --model_type flag to wide or deep. Other flags are configurable as well; see wide_deep.py for details.
The final accuracy should be over 83% with any of the three model types.
Run TensorBoard to inspect the details about the graph and training progression.
tensorboard --logdir=/tmp/census_model
If you are interested in distributed training, take a look at Distributed TensorFlow.
You can also run this model on Cloud ML Engine, which provides hyperparameter tuning to maximize your model's results and enables deploying your model for prediction.