estimators: Base Documentation for Canned Estimators
In tfestimators: Interface to 'TensorFlow' Estimators

Base Documentation for Canned Estimators

`object`	A TensorFlow estimator.
`feature_columns`	An R list containing all of the feature columns used by the model (typically, generated by `feature_columns()`).
`model_dir`	Directory to save the model parameters, graph, and so on. This can also be used to load checkpoints from the directory into a estimator to continue training a previously saved model.
`label_dimension`	Number of regression targets per example. This is the size of the last dimension of the labels and logits `Tensor` objects (typically, these have shape `[batch_size, label_dimension]`).
`label_vocabulary`	A list of strings represents possible label values. If given, labels must be string type and have any value in `label_vocabulary`. If it is not given, that means labels are already encoded as integer or float within `[0, 1]` for `n_classes == 2` and encoded as integer values in `{0, 1,..., n_classes -1}` for `n_classes > 2`. Also there will be errors if vocabulary is not provided and labels are string.
`weight_column`	A string, or a numeric column created by `column_numeric()` defining feature column representing weights. It is used to down weight or boost examples during training. It will be multiplied by the loss of the example. If it is a string, it is used as a key to fetch weight tensor from the `features` argument. If it is a numeric column, then the raw tensor is fetched by key `weight_column$key`, then `weight_column$normalizer_fn` is applied on it to get weight tensor.
`n_classes`	The number of label classes.
`config`	A run configuration created by `run_config()`, used to configure the runtime settings.
`input_layer_partitioner`	An optional partitioner for the input layer. Defaults to `min_max_variable_partitioner` with `min_slice_size` 64 << 20.
`partitioner`	An optional partitioner for the input layer.