|---| | [ View source on GitHub](https://github.com/tensorflow/estimator/tree/master/tensorflow_estimator/python/estimator/canned/dnn_linear_combined.py#L392-L590) | An estimator for TensorFlow Linear and DNN joined classification models. (deprecated) > [!WARNING] > **Warning:** Estimators are not recommended for new code. Estimators run [`v1.Session`](https://www.tensorflow.org/api_docs/python/tf/compat/v1/Session)-style code which is more difficult to write correctly, and can behave unexpectedly, especially when combined with TF 2 code. Estimators do fall under our [compatibility guarantees](https://tensorflow.org/guide/versions), but will receive no fixes other than security vulnerabilities. See the [migration guide](https://tensorflow.org/guide/migrate) for details. Inherits From: [`Estimator`](https://www.tensorflow.org/versions/r2.15/api_docs/python/tf/estimator/Estimator), [`Estimator`](https://www.tensorflow.org/versions/r2.15/api_docs/python/tf/compat/v1/estimator/Estimator) tf.estimator.DNNLinearCombinedClassifier( model_dir=None, linear_feature_columns=None, linear_optimizer='Ftrl', dnn_feature_columns=None, dnn_optimizer='Adagrad', dnn_hidden_units=None, dnn_activation_fn=https://www.tensorflow.org/versions/r2.15/api_docs/python/tf/nn/relu, dnn_dropout=None, n_classes=2, weight_column=None, label_vocabulary=None, config=None, warm_start_from=None, loss_reduction=tf.losses.Reduction.SUM_OVER_BATCH_SIZE, batch_norm=False, linear_sparse_combiner='sum' ) > [!WARNING] > **Deprecated:** THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: Use tf.keras instead. > [!NOTE] > **Note:** This estimator is also known as wide-n-deep. #### 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_id_column=categorical_feature_b, ...) estimator = tf.estimator.DNNLinearCombinedClassifier( # 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 softmax cross entropy.


| ## 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`](https://www.tensorflow.org/versions/r2.15/api_docs/python/tf/nn/relu). | | `dnn_dropout` | When not None, the probability we will drop out a given coordinate. | | `n_classes` | Number of label classes. Defaults to 2, namely binary classification. Must be \> 1. | | `weight_column` | A string or a `_NumericColumn` created by [`tf.feature_column.numeric_column`](https://www.tensorflow.org/versions/r2.15/api_docs/python/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. | | `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. | | `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. |



| ## Attributes || |---|---| | `config` |

| | `export_savedmodel` |

| | `model_dir` |

| | `model_fn` | Returns the `model_fn` which is bound to `self.params`. | | `params` |

|
## Methods ### `eval_dir` [View source](https://github.com/tensorflow/estimator/tree/master/tensorflow_estimator/python/estimator/estimator.py#L389-L401) 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](https://github.com/tensorflow/estimator/tree/master/tensorflow_estimator/python/estimator/estimator.py#L403-L478) 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`](https://www.tensorflow.org/versions/r2.15/api_docs/python/tf/errors/OutOfRangeError) or `StopIteration`).


| Args || |---|---| | `input_fn` | A function that constructs the input data for evaluation. See [Premade Estimators](https://tensorflow.org/guide/premade_estimators#create_input_functions) for more information. The function should construct and return one of the following:
- A [`tf.data.Dataset`](https://www.tensorflow.org/versions/r2.15/api_docs/python/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`](https://www.tensorflow.org/versions/r2.15/api_docs/python/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 || |---|---| | 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](https://github.com/tensorflow/estimator/tree/master/tensorflow_estimator/python/estimator/estimator.py#L738-L810) 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 `Tensor`s. 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`](https://www.tensorflow.org/versions/r2.15/api_docs/python/tf/estimator/ModeKeys#TRAIN), [`tf.estimator.ModeKeys.EVAL`](https://www.tensorflow.org/versions/r2.15/api_docs/python/tf/estimator/ModeKeys#EVAL), then [`tf.estimator.ModeKeys.PREDICT`](https://www.tensorflow.org/versions/r2.15/api_docs/python/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.ExportOutput`s, 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`](https://www.tensorflow.org/versions/r2.15/api_docs/python/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](https://github.com/tensorflow/estimator/tree/master/tensorflow_estimator/python/estimator/estimator.py#L659-L736) 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](https://tensorflow.org/guide/saved_model#savedmodels_from_estimators). This method builds a new graph by first calling the `serving_input_receiver_fn` to obtain feature `Tensor`s, 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.ExportOutput`s, and the inputs are always the input receivers provided by the `serving_input_receiver_fn`. 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'}`. 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`](https://www.tensorflow.org/versions/r2.15/api_docs/python/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](https://github.com/tensorflow/estimator/tree/master/tensorflow_estimator/python/estimator/estimator.py#L259-L270) 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](https://github.com/tensorflow/estimator/tree/master/tensorflow_estimator/python/estimator/estimator.py#L243-L257) 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](https://github.com/tensorflow/estimator/tree/master/tensorflow_estimator/python/estimator/estimator.py#L272-L280) 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](https://github.com/tensorflow/estimator/tree/master/tensorflow_estimator/python/estimator/estimator.py#L555-L653) 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](https://github.com/tensorflow/tensorflow/issues/20506#issuecomment-422208517)


| Args || |---|---| | `input_fn` | A function that constructs the features. Prediction continues until `input_fn` raises an end-of-input exception ([`tf.errors.OutOfRangeError`](https://www.tensorflow.org/versions/r2.15/api_docs/python/tf/errors/OutOfRangeError) or `StopIteration`). See [Premade Estimators](https://tensorflow.org/guide/premade_estimators#create_input_functions) for more information. The function should construct and return one of the following:
- [`tf.data.Dataset`](https://www.tensorflow.org/versions/r2.15/api_docs/python/tf/data/Dataset) object -- Outputs of `Dataset` object must have same constraints as below. - features -- A [`tf.Tensor`](https://www.tensorflow.org/versions/r2.15/api_docs/python/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`](https://www.tensorflow.org/versions/r2.15/api_docs/python/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`](https://www.tensorflow.org/versions/r2.15/api_docs/python/tf/estimator/EstimatorSpec#predictions) is not a `dict`. |
### `train` [View source](https://github.com/tensorflow/estimator/tree/master/tensorflow_estimator/python/estimator/estimator.py#L282-L362) 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](https://tensorflow.org/guide/premade_estimators#create_input_functions) for more information. The function should construct and return one of the following:
- A [`tf.data.Dataset`](https://www.tensorflow.org/versions/r2.15/api_docs/python/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`](https://www.tensorflow.org/versions/r2.15/api_docs/python/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`](https://www.tensorflow.org/versions/r2.15/api_docs/python/tf/data) generally works in both graph and eager modes.