Fixed Issues in README#605

src/python/twitter/deepbird/projects/timelines/scripts/models/earlybird/train.py

@@ -36,7 +36,6 @@ def get_feature_values(features_values, params):
     return features_values
 
 def build_graph(features, label, mode, params, config=None):
-  # Function to build the Earlybird model graph
   weights = None
   if "weights" in features:
     weights = make_weights_tensor(features["weights"], label, params)
@@ -101,7 +100,6 @@ def build_graph(features, label, mode, params, config=None):
   return {"output": output, "loss": loss, "weights": weights}
 
 def print_data_example(logits, lolly_activations, features):
-  # Function to print data example
   return tf.Print(
     logits,
     [logits, lolly_activations, tf.reshape(features['keys'], (1, -1)), tf.reshape(tf.multiply(features['values'], -1.0), (1, -1))],
@@ -110,7 +108,6 @@ def print_data_example(logits, lolly_activations, features):
   )
 
 def earlybird_output_fn(graph_output):
-  # Function to process the Earlybird model output
   export_outputs = {
     tf.saved_model.signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY:
       tf.estimator.export.PredictOutput(
@@ -120,7 +117,6 @@ def earlybird_output_fn(graph_output):
   return export_outputs
 
 if __name__ == "__main__":
-  # Set up argument parser
   parser = DataRecordTrainer.add_parser_arguments()
 
   parser = twml.contrib.calibrators.add_discretizer_arguments(parser)
@@ -141,10 +137,8 @@ if __name__ == "__main__":
                       help="Prints 'DATA EXAMPLE = [[tf logit]][[logged lolly logit]][[feature ids][feature values]]'")
   add_weight_arguments(parser)
 
-  # Parse arguments
   opt = parser.parse_args()
 
-  # Set up feature configuration
   feature_config_module = all_configs.select_feature_config(opt.feature_config)
 
   feature_config = feature_config_module.get_feature_config(data_spec_path=opt.data_spec, label=opt.label)
@@ -153,7 +147,6 @@ if __name__ == "__main__":
     feature_config,
     keep_fields=("ids", "keys", "values", "batch_size", "total_size", "codes"))
 
-  # Discretizer calibration (if necessary)
   if not opt.lolly_model_tsv:
     if opt.model_use_existing_discretizer:
       logging.info("Skipping discretizer calibration [model.use_existing_discretizer=True]")
@@ -170,7 +163,6 @@ if __name__ == "__main__":
                                        build_graph_fn=build_percentile_discretizer_graph,
                                        feature_config=feature_config)
 
-  # Initialize trainer
   trainer = DataRecordTrainer(
     name="earlybird",
     params=opt,
@@ -184,7 +176,6 @@ if __name__ == "__main__":
     warm_start_from=None
   )
 
-  # Train and evaluate model
   train_input_fn = trainer.get_train_input_fn(parse_fn=parse_fn)
   eval_input_fn = trainer.get_eval_input_fn(parse_fn=parse_fn)
 
@@ -194,7 +185,6 @@ if __name__ == "__main__":
   trainingEndTime = datetime.now()
   logging.info("Training and Evaluation time: " + str(trainingEndTime - trainingStartTime))
 
-  # Export model (if current node is chief)
   if trainer._estimator.config.is_chief:
     serving_input_in_earlybird = {
       "input_sparse_tensor_indices": array_ops.placeholder(