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tf.estimator.experimental.RNNClassifier

A classifier for TensorFlow RNN models.

Inherits From: RNNEstimator, Estimator

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

Example:

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

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

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

def input_fn_eval: # returns x, y
  pass
metrics = estimator.evaluate(input_fn=input_fn_eval, steps=10)
def input_fn_predict: # returns x, None
  pass
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 key=column.name whose value is a SparseTensor.
  • for each column in context_feature_columns:
    • if column is a CategoricalColumn, a feature with key=column.name 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 key=column.name 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 DenseColumn such as numeric_column, not the sequential variants.
units Iterable of integer number of hidden units per RNN layer. If set, cell_type must also be specified and rnn_cell_fn must be None.
cell_type A class producing a RNN cell or a string specifying the cell type. Supported strings are: 'simple_rnn', 'lstm', and 'gru'. If set, units must also be specified and rnn_cell_fn must be None.
rnn_cell_fn A function that returns a RNN cell instance that will be used to construct the RNN. If set, units and cell_type cannot be set. This is for advanced users who need additional customization beyond units and cell_type. Note that tf.keras.layers.StackedRNNCells is needed for stacked RNNs.
return_sequences A boolean indicating whether to return the last output in the output sequence, or the full sequence. Note that if True, weight_column must be None or a string.
model_dir Directory to save model parameters, graph and etc. This can also be used to load checkpoints from the