TFTS: Switch to using core feature columns

This fixes some shape issues that came up when using the tf.contrib.layers
parsing functions.

Adds a string -> embedding column API example to the LSTM example.

PiperOrigin-RevId: 187076400

6 files changed, 177 insertions, 158 deletions

diff --git a/tensorflow/contrib/timeseries/examples/data/multivariate_periods.csv b/tensorflow/contrib/timeseries/examples/data/multivariate_periods.csv
index b49a0662c2..9b15b4f0b2 100644
--- a/tensorflow/contrib/timeseries/examples/data/multivariate_periods.csv
+++ b/tensorflow/contrib/timeseries/examples/data/multivariate_periods.csv
@@ -1,100 +1,100 @@
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+63,1.02554736569,2.25943737733,3.07031591528,2.78176218013,5.78206328989,1.,0.,strkey
+64,0.337814475969,2.07589147224,2.80356226089,2.55888206331,5.7094075496,1.,0.,strkey
+65,-1.12023369929,1.25333011618,2.56497288445,2.77361359194,5.50799418376,1.,0.,strkey
+66,-0.178980246554,1.11937139901,2.51598681313,2.91438309151,5.47469577206,1.,0.,strkey
+67,0.97550951531,0.60553823137,2.11657741073,2.88081098981,5.37034999502,1.,0.,strkey
+68,0.136653357206,0.365828836075,1.97386033165,3.13217903204,5.07254490219,1.,0.,strkey
+69,-1.05607596951,0.153152115069,1.52110743825,3.01308794192,5.08902539125,1.,0.,strkey
+70,-0.13095280331,0.337113974483,1.52703079853,3.16687131599,4.86649398514,1.,0.,strkey
+71,1.07081057754,0.714247566736,1.53761382634,3.45151989484,4.75892309166,1.,0.,strkey
+72,0.0153410376082,1.24631231847,1.61690939161,3.85481994498,4.35683752832,1.,0.,strkey
+73,-0.912801257303,1.60791309476,1.8729264524,4.03037260012,4.36072588913,1.,0.,strkey
+74,-0.0894895640338,2.02535207407,1.93484909619,4.09557485132,4.35327025188,1.,0.,strkey
+75,0.978646999652,2.20085086625,2.09003440427,4.27542353033,4.1805058388,1.,0.,strkey
+76,-0.113312642876,2.2444100761,2.50789248839,4.4151861502,4.03267168136,1.,0.,strkey
+77,-1.00215099149,1.84305628445,2.61691237246,4.45425147595,3.81203553766,1.,0.,strkey
+78,-0.0183234614205,1.49573923116,2.99308471214,4.71134960112,4.0273804959,1.,0.,strkey
+79,1.0823738177,1.12211589848,3.27079386925,4.94288270502,4.01851068083,1.,0.,strkey
+80,0.124370187893,0.616474412808,3.4284236674,4.76942168327,3.9749536483,1.,0.,strkey
+81,-0.929423379352,0.290977090976,3.34131726136,4.78590392707,4.10190661656,1.,0.,strkey
+82,0.23766302648,0.155302052254,3.49779513794,4.64605656795,4.15571321107,1.,0.,strkey
+83,1.03531486192,0.359702776204,3.4880725919,4.48167586667,4.21134561991,1.,0.,strkey
+84,-0.261234571382,0.713877760378,3.42756426614,4.426443869,4.25208300527,1.,0.,strkey
+85,-1.03572442277,1.25001113691,2.96908341113,4.25500915322,4.25723010649,1.,0.,strkey
+86,0.380034261243,1.70543355622,2.73605932518,4.16703432307,4.63700400788,1.,0.,strkey
+87,1.03734873488,1.97544410562,2.55586572141,3.84976673263,4.55282864289,1.,0.,strkey
+88,-0.177344253372,2.22614526325,2.09565864891,3.77378097953,4.82577400298,1.,0.,strkey
+89,-0.976821526892,2.18385079177,1.78522284118,3.67768223554,5.06302440873,1.,0.,strkey
+90,0.264820472091,1.86981946157,1.50048403865,3.43619796921,5.05651761669,1.,0.,strkey
+91,1.05642344868,1.47568646076,1.51347671977,3.20898518885,5.50149047462,1.,0.,strkey
+92,-0.311607433358,1.04226467636,1.52089650905,3.02291865417,5.4889046232,1.,0.,strkey
+93,-0.724285777937,0.553052311957,1.48573560173,2.7365973598,5.72549174225,1.,0.,strkey
+94,0.519859192905,0.226520626591,1.61543723167,2.84102086852,5.69330622288,1.,0.,strkey
+95,1.0323195039,0.260873217055,1.81913034804,2.83951143848,5.90325028086,1.,0.,strkey
+96,-0.53285682538,0.387695521405,1.70935609313,2.57977050631,5.79579213161,1.,0.,strkey
+97,-0.975127997215,0.920948771589,2.51292643636,2.71004616612,5.87016469227,1.,0.,strkey
+98,0.540246804099,1.36445470181,2.61949412896,2.98482553485,6.02447664937,1.,0.,strkey
+99,0.987764008058,1.85581989607,2.84685706149,2.94760204892,6.0212151724,1.,0.,strkey
diff --git a/tensorflow/contrib/timeseries/examples/known_anomaly.py b/tensorflow/contrib/timeseries/examples/known_anomaly.py
index 7659dd308a..c08c0b0acb 100644
--- a/tensorflow/contrib/timeseries/examples/known_anomaly.py
+++ b/tensorflow/contrib/timeseries/examples/known_anomaly.py
@@ -46,12 +46,12 @@ def train_and_evaluate_exogenous(csv_file_name=_DATA_FILE, train_steps=300):
 
   # Indicate the format of our exogenous feature, in this case a string
   # representing a boolean value.
-  string_feature = tf.contrib.layers.sparse_column_with_keys(
-      column_name="is_changepoint", keys=["no", "yes"])
+  string_feature = tf.feature_column.categorical_column_with_vocabulary_list(
+      key="is_changepoint", vocabulary_list=["no", "yes"])
   # Specify the way this feature is presented to the model, here using a one-hot
   # encoding.
-  one_hot_feature = tf.contrib.layers.one_hot_column(
-      sparse_id_column=string_feature)
+  one_hot_feature = tf.feature_column.indicator_column(
+      categorical_column=string_feature)
 
   estimator = tf.contrib.timeseries.StructuralEnsembleRegressor(
       periodicities=12,
diff --git a/tensorflow/contrib/timeseries/examples/lstm.py b/tensorflow/contrib/timeseries/examples/lstm.py
index f37cafcc50..2eee878196 100644
--- a/tensorflow/contrib/timeseries/examples/lstm.py
+++ b/tensorflow/contrib/timeseries/examples/lstm.py
@@ -59,10 +59,10 @@ class _LSTMModel(ts_model.SequentialTimeSeriesModel):
       num_units: The number of units in the model's LSTMCell.
       num_features: The dimensionality of the time series (features per
         timestep).
-      exogenous_feature_columns: A list of tf.contrib.layers.FeatureColumn
-          objects representing features which are inputs to the model but are
-          not predicted by it. These must then be present for training,
-          evaluation, and prediction.
+      exogenous_feature_columns: A list of `tf.feature_column`s representing
+          features which are inputs to the model but are not predicted by
+          it. These must then be present for training, evaluation, and
+          prediction.
       dtype: The floating point data type to use.
     """
     super(_LSTMModel, self).__init__(
@@ -189,12 +189,16 @@ def train_and_predict(
     export_directory=None):
   """Train and predict using a custom time series model."""
   # Construct an Estimator from our LSTM model.
+  categorical_column = tf.feature_column.categorical_column_with_hash_bucket(
+      key="categorical_exogenous_feature", hash_bucket_size=16)
   exogenous_feature_columns = [
       # Exogenous features are not part of the loss, but can inform
       # predictions. In this example the features have no extra information, but
       # are included as an API example.
-      tf.contrib.layers.real_valued_column(
-          "2d_exogenous_feature", dimension=2)]
+      tf.feature_column.numeric_column(
+          "2d_exogenous_feature", shape=(2,)),
+      tf.feature_column.embedding_column(
+          categorical_column=categorical_column, dimension=10)]
   estimator = ts_estimators.TimeSeriesRegressor(
       model=_LSTMModel(num_features=5, num_units=128,
                        exogenous_feature_columns=exogenous_feature_columns),
@@ -205,7 +209,11 @@ def train_and_predict(
       csv_file_name,
       column_names=((tf.contrib.timeseries.TrainEvalFeatures.TIMES,)
                     + (tf.contrib.timeseries.TrainEvalFeatures.VALUES,) * 5
-                    + ("2d_exogenous_feature",) * 2))
+                    + ("2d_exogenous_feature",) * 2
+                    + ("categorical_exogenous_feature",)),
+      # Data types other than for `times` need to be specified if they aren't
+      # float32. In this case one of our exogenous features has string dtype.
+      column_dtypes=((tf.int64,) + (tf.float32,) * 7 + (tf.string,)))
   train_input_fn = tf.contrib.timeseries.RandomWindowInputFn(
       reader, batch_size=4, window_size=32)
   estimator.train(input_fn=train_input_fn, steps=training_steps)
@@ -215,7 +223,9 @@ def train_and_predict(
   predict_exogenous_features = {
       "2d_exogenous_feature": numpy.concatenate(
           [numpy.ones([1, 100, 1]), numpy.zeros([1, 100, 1])],
-          axis=-1)}
+          axis=-1),
+      "categorical_exogenous_feature": numpy.array(
+          ["strkey"] * 100)[None, :, None]}
   (predictions,) = tuple(estimator.predict(
       input_fn=tf.contrib.timeseries.predict_continuation_input_fn(
           evaluation, steps=100,
diff --git a/tensorflow/contrib/timeseries/python/timeseries/estimators.py b/tensorflow/contrib/timeseries/python/timeseries/estimators.py
index f8355f366f..8d13343e82 100644
--- a/tensorflow/contrib/timeseries/python/timeseries/estimators.py
+++ b/tensorflow/contrib/timeseries/python/timeseries/estimators.py
@@ -18,8 +18,6 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
-from tensorflow.contrib.layers.python.layers import feature_column
-
 from tensorflow.contrib.timeseries.python.timeseries import ar_model
 from tensorflow.contrib.timeseries.python.timeseries import feature_keys
 from tensorflow.contrib.timeseries.python.timeseries import head as ts_head_lib
@@ -31,10 +29,12 @@ from tensorflow.contrib.timeseries.python.timeseries.state_space_models.filterin
 
 from tensorflow.python.estimator import estimator_lib
 from tensorflow.python.estimator.export import export_lib
+from tensorflow.python.feature_column import feature_column
 from tensorflow.python.framework import dtypes
 from tensorflow.python.framework import ops
 from tensorflow.python.framework import tensor_shape
 from tensorflow.python.ops import array_ops
+from tensorflow.python.ops import parsing_ops
 from tensorflow.python.training import training as train
 
 
@@ -117,22 +117,29 @@ class TimeSeriesRegressor(estimator_lib.Estimator):
                   dtype=self._model.dtype),
               shape=(default_batch_size, default_series_length,
                      self._model.num_features)))
-      with ops.Graph().as_default():
-        # Default placeholders have only an unknown batch dimension. Make them
-        # in a separate graph, then splice in the series length to the shapes
-        # and re-create them in the outer graph.
-        exogenous_feature_shapes = {
-            key: (value.get_shape(), value.dtype) for key, value
-            in feature_column.make_place_holder_tensors_for_base_features(
-                self._model.exogenous_feature_columns).items()}
-      for feature_key, (batch_only_feature_shape, value_dtype) in (
-          exogenous_feature_shapes.items()):
-        batch_only_feature_shape = batch_only_feature_shape.with_rank_at_least(
-            1).as_list()
-        feature_shape = ([default_batch_size, default_series_length]
-                         + batch_only_feature_shape[1:])
-        placeholders[feature_key] = array_ops.placeholder(
-            dtype=value_dtype, name=feature_key, shape=feature_shape)
+      if self._model.exogenous_feature_columns:
+        with ops.Graph().as_default():
+          # Default placeholders have only an unknown batch dimension. Make them
+          # in a separate graph, then splice in the series length to the shapes
+          # and re-create them in the outer graph.
+          parsed_features = (
+              feature_column.make_parse_example_spec(
+                  self._model.exogenous_feature_columns))
+          placeholder_features = parsing_ops.parse_example(
+              serialized=array_ops.placeholder(
+                  shape=[None], dtype=dtypes.string),
+              features=parsed_features)
+          exogenous_feature_shapes = {
+              key: (value.get_shape(), value.dtype) for key, value
+              in placeholder_features.items()}
+        for feature_key, (batch_only_feature_shape, value_dtype) in (
+            exogenous_feature_shapes.items()):
+          batch_only_feature_shape = (
+              batch_only_feature_shape.with_rank_at_least(1).as_list())
+          feature_shape = ([default_batch_size, default_series_length]
+                           + batch_only_feature_shape[1:])
+          placeholders[feature_key] = array_ops.placeholder(
+              dtype=value_dtype, name=feature_key, shape=feature_shape)
       # Models may not know the shape of their state without creating some
       # variables/ops. Avoid polluting the default graph by making a new one. We
       # use only static metadata from the returned Tensors.
@@ -333,11 +340,11 @@ class StructuralEnsembleRegressor(StateSpaceRegressor):
           determine the model size. Learning autoregressive coefficients
           typically requires more steps and a smaller step size than other
           components.
-      exogenous_feature_columns: A list of tf.contrib.layers.FeatureColumn
-          objects (for example tf.contrib.layers.embedding_column) corresponding
-          to exogenous features which provide extra information to the model but
-          are not part of the series to be predicted. Passed to
-          tf.contrib.layers.input_from_feature_columns.
+      exogenous_feature_columns: A list of `tf.feature_column`s (for example
+          `tf.feature_column.embedding_column`) corresponding to exogenous
+          features which provide extra information to the model but are not part
+          of the series to be predicted. Passed to
+          `tf.feature_column.input_layer`.
       exogenous_update_condition: A function taking two Tensor arguments,
           `times` (shape [batch size]) and `features` (a dictionary mapping
           exogenous feature keys to Tensors with shapes [batch size, ...]), and
diff --git a/tensorflow/contrib/timeseries/python/timeseries/model.py b/tensorflow/contrib/timeseries/python/timeseries/model.py
index bac7d1ebf5..7644764a74 100644
--- a/tensorflow/contrib/timeseries/python/timeseries/model.py
+++ b/tensorflow/contrib/timeseries/python/timeseries/model.py
@@ -21,18 +21,17 @@ from __future__ import print_function
 import abc
 import collections
 
-from tensorflow.contrib import layers
-from tensorflow.contrib.layers import feature_column
-
 from tensorflow.contrib.timeseries.python.timeseries import math_utils
 from tensorflow.contrib.timeseries.python.timeseries.feature_keys import PredictionFeatures
 from tensorflow.contrib.timeseries.python.timeseries.feature_keys import TrainEvalFeatures
 
+from tensorflow.python.feature_column import feature_column
 from tensorflow.python.framework import dtypes
 from tensorflow.python.framework import ops
 from tensorflow.python.ops import array_ops
 from tensorflow.python.ops import control_flow_ops
 from tensorflow.python.ops import math_ops
+from tensorflow.python.ops import parsing_ops
 from tensorflow.python.ops import tensor_array_ops
 from tensorflow.python.ops import variable_scope
 
@@ -66,11 +65,11 @@ class TimeSeriesModel(object):
 
     Args:
       num_features: Number of features for the time series
-      exogenous_feature_columns: A list of tf.contrib.layers.FeatureColumn
-          objects (for example tf.contrib.layers.embedding_column) corresponding
-          to exogenous features which provide extra information to the model but
-          are not part of the series to be predicted. Passed to
-          tf.contrib.layers.input_from_feature_columns.
+      exogenous_feature_columns: A list of `tf.feature_column`s (for example
+           `tf.feature_column.embedding_column`) corresponding to exogenous
+           features which provide extra information to the model but are not
+           part of the series to be predicted. Passed to
+           `tf.feature_column.input_layer`.
       dtype: The floating point datatype to use.
     """
     if exogenous_feature_columns:
@@ -86,7 +85,7 @@ class TimeSeriesModel(object):
 
   @property
   def exogenous_feature_columns(self):
-    """`FeatureColumn` objects for features which are not predicted."""
+    """`tf.feature_colum`s for features which are not predicted."""
     return self._exogenous_feature_columns
 
   # TODO(allenl): Move more of the generic machinery for generating and
@@ -265,11 +264,14 @@ class TimeSeriesModel(object):
     if not self._exogenous_feature_columns:
       return (0,)
     with ops.Graph().as_default():
-      placeholder_features = (
-          feature_column.make_place_holder_tensors_for_base_features(
+      parsed_features = (
+          feature_column.make_parse_example_spec(
               self._exogenous_feature_columns))
-      embedded = layers.input_from_feature_columns(
-          columns_to_tensors=placeholder_features,
+      placeholder_features = parsing_ops.parse_example(
+          serialized=array_ops.placeholder(shape=[None], dtype=dtypes.string),
+          features=parsed_features)
+      embedded = feature_column.input_layer(
+          features=placeholder_features,
           feature_columns=self._exogenous_feature_columns)
       return embedded.get_shape().as_list()[1:]
 
@@ -308,13 +310,13 @@ class TimeSeriesModel(object):
         # Avoid shape warnings when embedding "scalar" exogenous features (those
         # with only batch and window dimensions); input_from_feature_columns
         # expects input ranks to match the embedded rank.
-        if tensor.get_shape().ndims == 1:
+        if tensor.get_shape().ndims == 1 and tensor.dtype != dtypes.string:
           exogenous_features_single_batch_dimension[name] = tensor[:, None]
         else:
           exogenous_features_single_batch_dimension[name] = tensor
       embedded_exogenous_features_single_batch_dimension = (
-          layers.input_from_feature_columns(
-              columns_to_tensors=exogenous_features_single_batch_dimension,
+          feature_column.input_layer(
+              features=exogenous_features_single_batch_dimension,
               feature_columns=self._exogenous_feature_columns,
               trainable=True))
       exogenous_regressors = array_ops.reshape(
@@ -381,8 +383,8 @@ class SequentialTimeSeriesModel(TimeSeriesModel):
           may use _scale_back_data or _scale_back_variance to return predictions
           to the input scale.
       dtype: The floating point datatype to use.
-      exogenous_feature_columns: A list of tf.contrib.layers.FeatureColumn
-          objects. See `TimeSeriesModel`.
+      exogenous_feature_columns: A list of `tf.feature_column`s objects. See
+          `TimeSeriesModel`.
       exogenous_update_condition: A function taking two Tensor arguments `times`
           (shape [batch size]) and `features` (a dictionary mapping exogenous
           feature keys to Tensors with shapes [batch size, ...]) and returning a
diff --git a/tensorflow/contrib/timeseries/python/timeseries/state_space_models/state_space_model.py b/tensorflow/contrib/timeseries/python/timeseries/state_space_models/state_space_model.py
index 6257002647..951c6546d5 100644
--- a/tensorflow/contrib/timeseries/python/timeseries/state_space_models/state_space_model.py
+++ b/tensorflow/contrib/timeseries/python/timeseries/state_space_models/state_space_model.py
@@ -112,11 +112,11 @@ class StateSpaceModelConfiguration(
       exogenous_noise_decreases: If True, exogenous regressors can "set" model
           state, decreasing uncertainty. If both this parameter and
           exogenous_noise_increases are False, exogenous regressors are ignored.
-      exogenous_feature_columns: A list of tf.contrib.layers.FeatureColumn
-          objects (for example tf.contrib.layers.embedding_column) corresponding
-          to exogenous features which provide extra information to the model but
-          are not part of the series to be predicted. Passed to
-          tf.contrib.layers.input_from_feature_columns.
+      exogenous_feature_columns: A list of `tf.feature_column`s (for example
+          `tf.feature_column.embedding_column`) corresponding to exogenous
+          features which provide extra information to the model but are not part
+          of the series to be predicted. Passed to
+          `tf.feature_column.input_layer`.
       exogenous_update_condition: A function taking two Tensor arguments `times`
           (shape [batch size]) and `features` (a dictionary mapping exogenous
           feature keys to Tensors with shapes [batch size, ...]) and returning a