def train_and_maybe_evaluate(hparams): """Run the training and evaluate using the high level API. Args: hparams: Holds hyperparameters used to train the model as name/value pairs. Returns: The estimator that was used for training (and maybe eval) """ schema = bookings.read_schema(hparams.schema_file) tf_transform_output = tft.TFTransformOutput(hparams.tf_transform_dir) train_input = lambda: model.input_fn( hparams.train_files, tf_transform_output, batch_size=TRAIN_BATCH_SIZE ) eval_input = lambda: model.input_fn( hparams.eval_files, tf_transform_output, batch_size=EVAL_BATCH_SIZE ) train_spec = tf.estimator.TrainSpec( train_input, max_steps=hparams.train_steps) serving_receiver_fn = lambda: model.example_serving_receiver_fn( tf_transform_output, schema) exporter = tf.estimator.FinalExporter('bookings', serving_receiver_fn) eval_spec = tf.estimator.EvalSpec( eval_input, steps=hparams.eval_steps, exporters=[exporter], name='bookings-eval') run_config = tf.estimator.RunConfig( save_checkpoints_steps=999, keep_checkpoint_max=1) serving_model_dir = os.path.join(hparams.output_dir, SERVING_MODEL_DIR) run_config = run_config.replace(model_dir=serving_model_dir) estimator = model.build_estimator( tf_transform_output, # Construct layers sizes with exponetial decay hidden_units=[ max(2, int(FIRST_DNN_LAYER_SIZE * DNN_DECAY_FACTOR**i)) for i in range(NUM_DNN_LAYERS) ], config=run_config) tf.estimator.train_and_evaluate(estimator, train_spec, eval_spec) return estimator
def train_and_maybe_evaluate(train_files, eval_files, hparams): """Run the training and evaluate using the high level API. Args: hparams: Holds hyperparameters used to train the model as name/value pairs. Returns: The estimator that was used for training (and maybe eval) """ schema = taxi.read_schema('schema.pbtxt') train_input = lambda: model.input_fn( train_files, hparams.tf_transform_dir, batch_size=TRAIN_BATCH_SIZE ) eval_input = lambda: model.input_fn( eval_files, hparams.tf_transform_dir, batch_size=EVAL_BATCH_SIZE ) train_spec = tf.estimator.TrainSpec( train_input, max_steps=hparams.train_steps) serving_receiver_fn = lambda: model.example_serving_receiver_fn( hparams.tf_transform_dir, schema) exporter = tf.estimator.FinalExporter('chicago-taxi', serving_receiver_fn) eval_spec = tf.estimator.EvalSpec( eval_input, steps=hparams.eval_steps, exporters=[exporter], name='chicago-taxi-eval') run_config = tf.estimator.RunConfig( save_checkpoints_steps=999, keep_checkpoint_max=1) serving_model_dir = os.path.join(hparams.output_dir, SERVING_MODEL_DIR) run_config = run_config.replace(model_dir=serving_model_dir) estimator = model.build_estimator( hparams.tf_transform_dir, # Construct layers sizes with exponetial decay hidden_units=[ max(2, int(FIRST_DNN_LAYER_SIZE * DNN_DECAY_FACTOR**i)) for i in range(NUM_DNN_LAYERS) ], config=run_config) tf.estimator.train_and_evaluate(estimator, train_spec, eval_spec) return estimator