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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
indnn_feature_columns
+linear_feature_columns
:- if
column
is aCategoricalColumn
, a feature withkey=column.name
whosevalue
is aSparseTensor
. - if
column
is aWeightedCategoricalColumn
, two features: the first withkey
the id column name, the second withkey
the weight column name. Both features'value
must be aSparseTensor
. - if
column
is aDenseColumn
, a feature withkey=column.name
whosevalue
is aTensor
.
- if
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
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
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, orinput_fn
raises an end-of-input exception (tf.errors.OutOfRangeError
orStopIteration
).
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:
|
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
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
,
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