tf.estimator.DNNLinearCombinedEstimator

An estimator for TensorFlow Linear and DNN joined models with custom head.

Inherits From: Estimator, Estimator

tf.estimator.DNNLinearCombinedEstimator(
    head, 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, config=None, 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.DNNLinearCombinedEstimator(
    head=tf.estimator.MultiLabelHead(n_classes=3),
    # 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(...))

# 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
`head`	A `Head` instance constructed with a method such as `tf.estimator.MultiLabelHead`.
`model_dir`	Directory to save model parameters, graph and etc. This can also be used to load checkpoints from the directory into an 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.
`config`	RunConfig object to configure the runtime settings.
`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.

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 for the mode in question.

Extra assets may be written into the SavedModel via the assets_extra argument. This should be a dict, where each key gives a destination path (including the filename) relative to the assets.extra directory. The corresponding value gives the full path of the source file to be copied. For example, the simple case of copying a single file without renaming it is specified as {'my_asset_file.txt': '/path/to/my_asset_file.txt'}.

Args
`export_dir_base`	A string containing a directory in which to create timestamped subdirectories containing exported `SavedModel`s.
`input_receiver_fn_map`	dict of `tf.estimator.ModeKeys` to `input_receiver_fn` mappings, where the `input_receiver_fn` is a function that takes no arguments and returns the appropriate subclass of `InputReceiver`.
`assets_extra`	A dict specifying how to populate the assets.extra directory within the exported `SavedModel`, or `None` if no extra assets are needed.
`as_text`	whether to write the `SavedModel` proto in text format.
`checkpoint_path`	The checkpoint path to export. If `None` (the default), the most recent checkpoint found within the model directory is chosen.

Returns
The path to the exported directory as a bytes object.

Raises
`ValueError`	if any `input_receiver_fn` is `None`, no `export_outputs` are provided, or no checkpoint can be found.

`export_saved_model`

View source

export_saved_model(
    export_dir_base, serving_input_receiver_fn, assets_extra=None, as_text=False,
    checkpoint_path=None, experimental_mode=ModeKeys.PREDICT
)

Exports inference graph as a SavedModel into the given dir.

For a detailed guide on SavedModel, see Using the SavedModel format.

This method builds a new graph by first calling the serving_input_receiver_fn to obtain feature Tensors, and then calling this Estimator's model_fn to generate the model graph based on those features. It restores the given checkpoint (or, lacking that, the most recent checkpoint) into this graph in a fresh session. Finally it creates a timestamped export directory below the given export_dir_base, and writes a SavedModel into it containing a single tf.MetaGraphDef saved from this session.

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.

The experimental_mode parameter can be used to export a single train/eval/predict graph as a SavedModel. See experimental_export_all_saved_models for full docs.

Args
`export_dir_base`	A string containing a directory in which to create timestamped subdirectories containing exported `SavedModel`s.
`serving_input_receiver_fn`	A function that takes no argument and returns a `tf.estimator.export.ServingInputReceiver` or `tf.estimator.export.TensorServingInputReceiver`.
`assets_extra`	A dict specifying how to populate the assets.extra directory within the exported `SavedModel`, or `None` if no extra assets are needed.
`as_text`	whether to write the `SavedModel` proto in text format.
`checkpoint_path`	The checkpoint path to export. If `None` (the default), the most recent checkpoint found within the model directory is chosen.
`experimental_mode`	`tf.estimator.ModeKeys` value indicating with mode will be exported. Note that this feature is experimental.

Returns
The path to the exported directory as a bytes object.

Raises
`ValueError`	if no `serving_input_receiver_fn` is provided, no `export_outputs` are provided, or no checkpoint can be found.

`get_variable_names`

View source

get_variable_names()

Returns list of all variable names in this model.

Returns
List of names.

Raises
`ValueError`	If the `Estimator` has not produced a checkpoint yet.

`get_variable_value`

View source

get_variable_value(
    name
)

Returns value of the variable given by name.

Args
`name`	string or a list of string, name of the tensor.

Returns
Numpy array - value of the tensor.

Raises
`ValueError`	If the `Estimator` has not produced a checkpoint yet.

`latest_checkpoint`

View source

latest_checkpoint()

Finds the filename of the latest saved checkpoint file in model_dir.

Returns
The full path to the latest checkpoint or `None` if no checkpoint was found.

`predict`

View source

predict(
    input_fn, predict_keys=None, hooks=None, checkpoint_path=None,
    yield_single_examples=True
)

Yields predictions for given features.

Please note that interleaving two predict outputs does not work. See: issue/20506

Args
`input_fn`	A function that constructs the features. Prediction continues until `input_fn` raises an end-of-input exception (`tf.errors.OutOfRangeError` or `StopIteration`). See Premade Estimators for more information. The function should construct and return one of the following: `tf.data.Dataset` object -- Outputs of `Dataset` object must have same constraints as below. features -- A `tf.Tensor` or a dictionary of string feature name to `Tensor`. features are consumed by `model_fn`. They should satisfy the expectation of `model_fn` from inputs. A tuple, in which case the first item is extracted as features.
`predict_keys`	list of `str`, name of the keys to predict. It is used if the `tf.estimator.EstimatorSpec.predictions` is a `dict`. If `predict_keys` is used then rest of the predictions will be filtered from the dictionary. If `None`, returns all.
`hooks`	List of `tf.train.SessionRunHook` subclass instances. Used for callbacks inside the prediction call.
`checkpoint_path`	Path of a specific checkpoint to predict. If `None`, the latest checkpoint in `model_dir` is used. If there are no checkpoints in `model_dir`, prediction is run with newly initialized `Variables` instead of ones restored from checkpoint.
`yield_single_examples`	If `False`, yields the whole batch as returned by the `model_fn` instead of decomposing the batch into individual elements. This is useful if `model_fn` returns some tensors whose first dimension is not equal to the batch size.

Yields
Evaluated values of `predictions` tensors.

Raises
`ValueError`	If batch length of predictions is not the same and `yield_single_examples` is `True`.
`ValueError`	If there is a conflict between `predict_keys` and `predictions`. For example if `predict_keys` is not `None` but `tf.estimator.EstimatorSpec.predictions` is not a `dict`.

`train`

View source

train(
    input_fn, hooks=None, steps=None, max_steps=None, saving_listeners=None
)

Trains a model given training data input_fn.

Args
`input_fn`	A function that provides input data for training as minibatches. 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.
`hooks`	List of `tf.train.SessionRunHook` subclass instances. Used for callbacks inside the training loop.
`steps`	Number of steps for which to train the model. If `None`, train forever or train until `input_fn` generates the `tf.errors.OutOfRange` error or `StopIteration` exception. `steps` works incrementally. If you call two times `train(steps=10)` then training occurs in total 20 steps. If `OutOfRange` or `StopIteration` occurs in the middle, training stops before 20 steps. If you don't want to have incremental behavior please set `max_steps` instead. If set, `max_steps` must be `None`.
`max_steps`	Number of total steps for which to train model. If `None`, train forever or train until `input_fn` generates the `tf.errors.OutOfRange` error or `StopIteration` exception. If set, `steps` must be `None`. If `OutOfRange` or `StopIteration` occurs in the middle, training stops before `max_steps` steps. Two calls to `train(steps=100)` means 200 training iterations. On the other hand, two calls to `train(max_steps=100)` means that the second call will not do any iteration since first call did all 100 steps.
`saving_listeners`	list of `CheckpointSaverListener` objects. Used for callbacks that run immediately before or after checkpoint savings.

Returns
`self`, for chaining.

Raises
`ValueError`	If both `steps` and `max_steps` are not `None`.
`ValueError`	If either `steps` or `max_steps <= 0`.

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.

tf.estimator.DNNLinearCombinedEstimator

Example:

Args

Raises

Attributes

Methods

eval_dir

evaluate

experimental_export_all_saved_models

export_saved_model

get_variable_names

get_variable_value

latest_checkpoint

predict

train

eager compatibility

`eval_dir`

`evaluate`

`experimental_export_all_saved_models`

`export_saved_model`

`get_variable_names`

`get_variable_value`

`latest_checkpoint`

`predict`

`train`