A classifier for TensorFlow RNN models.

Inherits From: RNNEstimator

Trains a recurrent neural network model to classify instances into one of multiple classes.


token_sequence = sequence_categorical_column_with_hash_bucket(...)
token_emb = embedding_column(categorical_column=token_sequence, ...)

estimator = RNNClassifier(
    units=[32, 16], cell_type='lstm')

# Input builders
def input_fn_train: # returns x, y
estimator.train(input_fn=input_fn_train, steps=100)

def input_fn_eval: # returns x, y
metrics = estimator.evaluate(input_fn=input_fn_eval, steps=10)
def input_fn_predict: # returns x, None
predictions = estimator.predict(input_fn=input_fn_predict)

Input of train and evaluate should have following features, otherwise there will be a KeyError:

  • if weight_column is not None, a feature with key=weight_column whose value is a Tensor.
  • for each column in sequence_feature_columns:
    • a feature with whose value is a SparseTensor.
  • for each column in context_feature_columns:
    • if column is a CategoricalColumn, a feature with whose value is a SparseTensor.
    • if column is a WeightedCategoricalColumn, two features: the first with key the id column name, the second with key the weight column name. Both features' value must be a SparseTensor.
    • if column is a DenseColumn, a feature with whose value is a Tensor.

Loss is calculated by using softmax cross entropy.

sequence_feature_columns An iterable containing the FeatureColumns that represent sequential input. All items in the set should either be sequence columns (e.g. sequence_numeric_column) or constructed from one (e.g. embedding_column with sequence_categorical_column_* as input).
context_feature_columns An iterable containing the FeatureColumns for contextual input. The data represented by these columns will be replicated and given to the RNN at each timestep. These columns must be instances of classes derived from <