An iterable containing all the feature columns used by
the model. All items in the set should be instances of classes derived
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.
An instance of tf.keras.optimizers.* used to train the model.
Can also be a string (one of 'Adagrad', 'Adam', 'Ftrl', 'RMSProp',
'SGD'), or callable. Defaults to FTRL optimizer.
RunConfig object to configure the runtime settings.
A string specifying how to reduce if a categorical column
is multivalent. One of "mean", "sqrtn", and "sum" -- these are
effectively different ways to do example-level normalization, which can
be useful for bag-of-words features. for more details, see
A string filepath to a checkpoint to warm-start from, or
a WarmStartSettings object to fully configure warm-starting. If the
string filepath is provided instead of a WarmStartSettings, then all
weights and biases are warm-started, and it is assumed that vocabularies
and Tensor names are unchanged.
Returns the model_fn which is bound to self.params.
Shows the directory name where evaluation metrics are dumped.
Name of the evaluation if user needs to run multiple evaluations on
different data sets, such as on training data vs test data. Metrics for
different evaluations are saved in separate folders, and appear
separately in tensorboard.
A string which is the path of directory contains evaluation metrics.