Here, we use the wide and deep model to predict the click 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.
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.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 code is based on the TensorFlow wide and deep tutorial.
- provide flexible feature configuration.
- scalable to arbitrarily train data size in production environment.
- support multi value feature input (multihot).
- support train in distribution
- provide some script to load data from hdfs.
cd conf
vim feature.conf
...
You can run the code locally as follows:
python train.py
The model is saved to /model by default, which can be changed using the --model_dir flag.
Run TensorBoard to inspect the details about the graph and training progression.
tensorboard --logdir=/model/wide_deep
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.