tf.estimator.DNNLinearCombinedRegressor

An estimator for TensorFlow Linear and DNN joined models for regression.

Inherits From: Estimator

tf.estimator.DNNLinearCombinedRegressor(
    model_dir=None, linear_feature_columns=None, linear_optimizer='Ftrl',
    dnn_feature_columns=None, dnn_optimizer='Adagrad', dnn_hidden_units=None,
    dnn_activation_fn=tf.nn.relu, dnn_dropout=None, label_dimension=1,
    weight_column=None, config=None, warm_start_from=None,
    loss_reduction=losses_utils.ReductionV2.SUM_OVER_BATCH_SIZE, batch_norm=False,
    linear_sparse_combiner='sum'
)

Example:

numeric_feature = numeric_column(...)
categorical_column_a = categorical_column_with_hash_bucket(...)
categorical_column_b = categorical_column_with_hash_bucket(...)

categorical_feature_a_x_categorical_feature_b = crossed_column(...)
categorical_feature_a_emb = embedding_column(
    categorical_column=categorical_feature_a, ...)
categorical_feature_b_emb = embedding_column(
    categorical_column=categorical_feature_b, ...)

estimator = tf.estimator.DNNLinearCombinedRegressor(
    # wide settings
    linear_feature_columns=[categorical_feature_a_x_categorical_feature_b],
    linear_optimizer=tf.keras.optimizers.Ftrl(...),
    # deep settings
    dnn_feature_columns=[
        categorical_feature_a_emb, categorical_feature_b_emb,
        numeric_feature],
    dnn_hidden_units=[1000, 500, 100],
    dnn_optimizer=tf.keras.optimizers.Adagrad(...),
    # warm-start settings
    warm_start_from="/path/to/checkpoint/dir")

# To apply L1 and L2 regularization, you can set dnn_optimizer to:
tf.compat.v1.train.ProximalAdagradOptimizer(
    learning_rate=0.1,
    l1_regularization_strength=0.001,
    l2_regularization_strength=0.001)
# To apply learning rate decay, you can set dnn_optimizer to a callable:
lambda: tf.keras.optimizers.Adam(
    learning_rate=tf.compat.v1.train.exponential_decay(
        learning_rate=0.1,
        global_step=tf.compat.v1.train.get_global_step(),
        decay_steps=10000,
        decay_rate=0.96)
# It is the same for linear_optimizer.

# Input builders
def input_fn_train:
  # Returns tf.data.Dataset of (x, y) tuple where y represents label's class
  # index.
  pass
def input_fn_eval:
  # Returns tf.data.Dataset of (x, y) tuple where y represents label's class
  # index.
  pass
def input_fn_predict:
  # Returns tf.data.Dataset of (x, None) tuple.
  pass
estimator.train(input_fn=input_fn_train, steps=100)
metrics = estimator.evaluate(input_fn=input_fn_eval, steps=10)
predictions = estimator.predict(input_fn=input_fn_predict)

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

for each column in dnn_feature_columns + linear_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 mean squared error.

Args
`model_dir`	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.
`linear_feature_columns`	An iterable containing all the feature columns used by linear part of the model. All items in the set must be instances of classes derived from `FeatureColumn`.
`linear_optimizer`	An instance of `tf.keras.optimizers.*` used to apply gradients to the linear part of the model. Can also be a string (one of 'Adagrad', 'Adam', 'Ftrl', 'RMSProp', 'SGD'), or callable. Defaults to FTRL optimizer.
`dnn_feature_columns`	An iterable containing all the feature columns used by deep part of the model. All items in the set must be instances of classes derived from `FeatureColumn`.
`dnn_optimizer`	An instance of `tf.keras.optimizers.*` used to apply gradients to the deep part of the model. Can also be a string (one of 'Adagrad', 'Adam', 'Ftrl', 'RMSProp', 'SGD'), or callable. Defaults to Adagrad optimizer.
`dnn_hidden_units`	List of hidden units per layer. All layers are fully connected.
`dnn_activation_fn`	Activation function applied to each layer. If None, will use `tf.nn.relu`.
`dnn_dropout`	When not None, the probability we will drop out a given coordinate.
`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]`).
`weight_column`	A string or a `NumericColumn` created by `tf.feature_column.numeric_column` 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`. If it is a `_NumericColumn`, raw tensor is fetched by key `weight_column.key`, then weight_column.normalizer_fn is applied on it to get weight tensor.
`config`	RunConfig object to configure the runtime settings.
`warm_start_from`	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 are warm-started, and it is assumed that vocabularies and Tensor names are unchanged.
`loss_reduction`	One of `tf.losses.Reduction` except `NONE`. Describes how to reduce training loss over batch. Defaults to `SUM_OVER_BATCH_SIZE`.
`batch_norm`	Whether to use batch normalization after each hidden layer.
`linear_sparse_combiner`	A string specifying how to reduce the linear model 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 `tf.feature_column.linear_model`.

Raises
`ValueError`	If both linear_feature_columns and dnn_features_columns are empty at the same time.

Eager Compatibility

Estimators can be used while eager execution is enabled. Note that input_fn and all hooks are executed inside a graph context, so they have to be written to be compatible with graph mode. Note that input_fn code using tf.data generally works in both graph and eager modes.

Attributes
`config`
`export_savedmodel`
`model_dir`
`model_fn`	Returns the `model_fn` which is bound to `self.params`.
`params`

Methods

`eval_dir`

View source

eval_dir(
    name=None
)

Shows the directory name where evaluation metrics are dumped.

Args
`name`	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.

Returns
A string which is the path of directory contains evaluation metrics.

`evaluate`

View source

evaluate(
    input_fn, steps=None, hooks=None, checkpoint_path=None, name=None
)

Evaluates the model given evaluation data input_fn.

For each step, calls input_fn, which returns one batch of data. Evaluates until:

steps batches are processed, or
input_fn raises an end-of-input exception (tf.errors.OutOfRangeError or StopIteration).

Args
`input_fn`	A function that constructs the input data for evaluation. See Premade Estimators for more information. The function should construct and return one of the following: A `tf.data.Dataset` object: Outputs of `Dataset` object must be a tuple `(features, labels)` with same constraints as below. A tuple `(features, labels)`: Where `features` is a `tf.Tensor` or a dictionary of string feature name to `Tensor` and `labels` is a `Tensor` or a dictionary of string label name to `Tensor`. Both `features` and `labels` are consumed by `model_fn`. They should satisfy the expectation of `model_fn` from inputs.
`steps`	Number of steps for which to evaluate model. If `None`, evaluates until `input_fn` raises an end-of-input exception.
`hooks`	List of `tf.train.SessionRunHook` subclass instances. Used for callbacks inside the evaluation call.
`checkpoint_path`	Path of a specific checkpoint to evaluate. If `None`, the latest checkpoint in `model_dir` is used. If there are no checkpoints in `model_dir`, evaluation is run with newly initialized `Variables` instead of ones restored from checkpoint.
`name`	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.

Returns

Returns
A dict containing the evaluation metrics specified in `model_fn` keyed by name, as well as an entry `global_step` which contains the value of the global step for which this evaluation was performed. For canned estimators, the dict contains the `loss` (mean loss per mini-batch) and the `average_loss` (mean loss per sample). Canned classifiers also return the `accuracy`. Canned regressors also return the `label/mean` and the `prediction/mean`.

A dict containing the evaluation metrics specified in model_fn keyed by name, as well as an entry global_step which contains the value of the global step for which this evaluation was performed. For canned estimators, the dict contains the loss (mean loss per mini-batch) and the average_loss (mean loss per sample). Canned classifiers also return the accuracy. Canned regressors also return the label/mean and the prediction/mean.

Raises
`ValueError`	If `steps <= 0`.

`experimental_export_all_saved_models`

View source

experimental_export_all_saved_models(
    export_dir_base, input_receiver_fn_map, assets_extra=None, as_text=False,
    checkpoint_path=None
)

Exports a SavedModel with tf.MetaGraphDefs for each requested mode.

For each mode passed in via the input_receiver_fn_map, this method builds a new graph by calling the input_receiver_fn to obtain feature and label Tensors. Next, this method calls the Estimator's model_fn in the passed mode to generate the model graph based on those features and labels, and restores the given checkpoint (or, lacking that, the most recent checkpoint) into the graph. Only one of the modes is used for saving variables to the SavedModel (order of preference: tf.estimator.ModeKeys.TRAIN, tf.estimator.ModeKeys.EVAL, then tf.estimator.ModeKeys.PREDICT), such that up to three tf.MetaGraphDefs are saved with a single set of variables in a single SavedModel directory.

For the variables and tf.MetaGraphDefs, a timestamped export directory below export_dir_base, and writes a SavedModel into it containing the tf.MetaGraphDef for the given mode and its associated signatures.

For prediction, the exported MetaGraphDef will provide one SignatureDef for each element of the export_outputs dict returned from the model_fn, named using the same keys. One of these keys is always tf.saved_model.signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY, indicating which signature will be served when a serving request does not specify one. For each signature, the outputs are provided by the corresponding tf.estimator.export.ExportOutputs, and the inputs are always the input receivers provided by the serving_input_receiver_fn.

For training and evaluation, the train_op is stored in an extra collection, and loss, metrics, and predictions are included in a SignatureDef

tf.estimator.DNNLinearCombinedRegressor

Example:

Args

Raises

Eager Compatibility

Attributes

Methods

eval_dir

evaluate

experimental_export_all_saved_models

`eval_dir`

`evaluate`

`experimental_export_all_saved_models`