message: Update docstring for BoostedTrees n_batches_per_layer.
diff: @@ -1000,8 +1000,11 @@ class BoostedTreesClassifier(estimator.Estimator):
  bucketized_feature_2 = bucketized_column(
  numeric_column('feature_2'), BUCKET_BOUNDARIES_2)
  + # Need to see a large portion of the data before we can build a layer, for
  + # example half of data n_batches_per_layer = 0.5 * NUM_EXAMPLES / BATCH_SIZE
  classifier = estimator.BoostedTreesClassifier(
  feature_columns=[bucketized_feature_1, bucketized_feature_2],
  + n_batches_per_layer=n_batches_per_layer,
  n_trees=100,
  ... <some other params>
  )
  @@ -1024,7 +1027,8 @@ class BoostedTreesClassifier(estimator.Estimator):
  the model. All items in the set should be instances of classes derived
  from `FeatureColumn`.
  n_batches_per_layer: the number of batches to collect statistics per
  - layer.
  + layer. The total number of batches is total number of data divided by
  + batch size.
  model_dir: Directory to save model parameters, graph and etc. This can
  also be used to load checkpoints from the directory into a estimator
  to continue training a previously saved model.
  @@ -1138,8 +1142,11 @@ class BoostedTreesRegressor(estimator.Estimator):
  bucketized_feature_2 = bucketized_column(
  numeric_column('feature_2'), BUCKET_BOUNDARIES_2)
  + # Need to see a large portion of the data before we can build a layer, for
  + # example half of data n_batches_per_layer = 0.5 * NUM_EXAMPLES / BATCH_SIZE
  regressor = estimator.BoostedTreesRegressor(
  feature_columns=[bucketized_feature_1, bucketized_feature_2],
  + n_batches_per_layer=n_batches_per_layer,
  n_trees=100,
  ... <some other params>
  )
  @@ -1162,7 +1169,8 @@ class BoostedTreesRegressor(estimator.Estimator):
  the model. All items in the set should be instances of classes derived
  from `FeatureColumn`.
  n_batches_per_layer: the number of batches to collect statistics per
  - layer.
  + layer. The total number of batches is total number of data divided by
  + batch size.
  model_dir: Directory to save model parameters, graph and etc. This can
  also be used to load checkpoints from the directory into a estimator
  to continue training a previously saved model.
  