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A regressor for TensorFlow DNN models.
Inherits From: Estimator
tf.compat.v1.estimator.DNNRegressor(
hidden_units, feature_columns, model_dir=None, label_dimension=1,
weight_column=None, optimizer='Adagrad', activation_fn=tf.nn.relu, dropout=None,
input_layer_partitioner=None, config=None, warm_start_from=None,
loss_reduction=losses.Reduction.SUM, batch_norm=False
)
Example:
categorical_feature_a = categorical_column_with_hash_bucket(...)
categorical_feature_b = categorical_column_with_hash_bucket(...)
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.DNNRegressor(
feature_columns=[categorical_feature_a_emb, categorical_feature_b_emb],
hidden_units=[1024, 512, 256])
# Or estimator using the ProximalAdagradOptimizer optimizer with
# regularization.
estimator = tf.estimator.DNNRegressor(
feature_columns=[categorical_feature_a_emb, categorical_feature_b_emb],
hidden_units=[1024, 512, 256],
optimizer=tf.compat.v1.train.ProximalAdagradOptimizer(
learning_rate=0.1,
l1_regularization_strength=0.001
))
# Or estimator using an optimizer with a learning rate decay.
estimator = tf.estimator.DNNRegressor(
feature_columns=[categorical_feature_a_emb, categorical_feature_b_emb],
hidden_units=[1024, 512, 256],
optimizer=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))
# Or estimator with warm-starting from a previous checkpoint.
estimator = tf.estimator.DNNRegressor(
feature_columns=[categorical_feature_a_emb, categorical_feature_b_emb],
hidden_units=[1024, 512, 256],
warm_start_from="/path/to/checkpoint/dir")
# 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)
metrics = estimator.evaluate(input_fn=input_fn_eval)
predictions = estimator.predict(input_fn=input_fn_predict)
Input of train
and evaluate
should have following features,
otherwise there will be a KeyError
:
- if
weight_column
is notNone
, a feature withkey=weight_column
whose value is aTensor
. - for each
column
infeature_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_fn
|
Model function. Follows the signature:
|
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. If PathLike object, the
path will be resolved. If None , the model_dir in config will be used
if set. If both are set, they must be same. If both are None , a
temporary directory will be used.
|
config
|
estimator.RunConfig configuration object.
|
params
|
dict of hyper parameters that will be passed into model_fn .
Keys are names of parameters, values are basic python types.
|
warm_start_from
|
Optional string filepath to a checkpoint or SavedModel to
warm-start from, or a tf.estimator.WarmStartSettings
object to fully configure warm-starting.
If None, only TRAINABLE variables are warm-started. If the string filepath is provided instead of a
|
Raises | |
---|---|
ValueError
|
parameters of model_fn don't match params .
|
ValueError
|
if this is called via a subclass and if that class overrides
a member of Estimator .
|
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
|
|
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: * 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 .
|
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 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 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 string path to the exported directory. |
Raises | |
---|---|
ValueError
|
if any input_receiver_fn is None , no export_outputs
are provided, or no checkpoint can be found.
|
export_saved_model
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, see Using SavedModel with 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 value indicating with mode
will be exported. Note that this feature is experimental.
|
Returns | |
---|---|
The string path to the exported directory. |
Raises | |
---|---|
ValueError
|
if no serving_input_receiver_fn is provided, no
export_outputs are provided, or no checkpoint can be found.
|
export_savedmodel
export_savedmodel(
export_dir_base, serving_input_receiver_fn, assets_extra=None, as_text=False,
checkpoint_path=None, strip_default_attrs=False
)
Exports inference graph as a SavedModel
into the given dir. (deprecated)
For a detailed guide, see Using SavedModel with 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'}
.
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.
|
strip_default_attrs
|
Boolean. If True , default-valued attributes will be
removed from the NodeDef s. For a detailed guide, see Stripping
Default-Valued Attributes.
|
Returns | |
---|---|
The string path to the exported directory. |
Raises | |
---|---|
ValueError
|
if no serving_input_receiver_fn is provided, no
export_outputs are provided, or no checkpoint can be found.
|
get_variable_names
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
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
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
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:
|
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
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:
|
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 .
|