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 settingslinear_feature_columns=[categorical_feature_a_x_categorical_feature_b],linear_optimizer=tf.keras.optimizers.Ftrl(...),# deep settingsdnn_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 settingswarm_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 buildersdefinput_fn_train:# Returns tf.data.Dataset of (x, y) tuple where y represents label's class# index.passdefinput_fn_eval:# Returns tf.data.Dataset of (x, y) tuple where y represents label's class# index.passdefinput_fn_predict:# Returns tf.data.Dataset of (x, None) tuple.passestimator.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.
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.
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
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.
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 SavedModels.
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.
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.
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 SavedModels.
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.
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.
TensorFlow 1 version
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