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An Estimator for TensorFlow RNN models with user-specified head.
Inherits From: Estimator
tf.estimator.experimental.RNNEstimator(
head, sequence_feature_columns, context_feature_columns=None, units=None,
cell_type=USE_DEFAULT, rnn_cell_fn=None, return_sequences=False, model_dir=None,
optimizer='Adagrad', config=None
)
Example:
token_sequence = sequence_categorical_column_with_hash_bucket(...)
token_emb = embedding_column(categorical_column=token_sequence, ...)
estimator = RNNEstimator(
head=tf.estimator.RegressionHead(),
sequence_feature_columns=[token_emb],
units=[32, 16], cell_type='lstm')
# Or with custom RNN cell:
def rnn_cell_fn(_):
cells = [ tf.keras.layers.LSTMCell(size) for size in [32, 16] ]
return tf.keras.layers.StackedRNNCells(cells)
estimator = RNNEstimator(
head=tf.estimator.RegressionHead(),
sequence_feature_columns=[token_emb],
rnn_cell_fn=rnn_cell_fn)
# Input builders
def input_fn_train: # returns x, y
pass
estimator.train(input_fn=input_fn_train, steps=100)
def input_fn_eval: # returns x, y
pass
metrics = estimator.evaluate(input_fn=input_fn_eval, steps=10)
def input_fn_predict: # returns x, None
pass
predictions = estimator.predict(input_fn=input_fn_predict)
Input of train
and evaluate
should have following features,
otherwise there will be a KeyError
:
- if the head's
weight_column
is notNone
, a feature withkey=weight_column
whose value is aTensor
. - for each
column
insequence_feature_columns
:- a feature with
key=column.name
whosevalue
is aSparseTensor
.
- a feature with
- for each
column
incontext_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 and predicted output are determined by the specified head.
Args | |
---|---|
head
|
A Head instance. This specifies the model's output and loss
function to be optimized.
|
sequence_feature_columns
|
An iterable containing the FeatureColumn s that
represent sequential input. All items in the set should either be
sequence columns (e.g. sequence_numeric_column ) or constructed from
one (e.g. embedding_column with sequence_categorical_column_* as
input).
|
context_feature_columns
|
An iterable containing the FeatureColumn s for
contextual input. The data represented by these columns will be
replicated and given to the RNN at each timestep. These columns must be
instances of classes derived from DenseColumn such as
numeric_column , not the sequential variants.
|
units
|
Iterable of integer number of hidden units per RNN layer. If set,
cell_type must also be specified and rnn_cell_fn must be None .
|
cell_type
|
A class producing a RNN cell or a string specifying the cell
type. Supported strings are: 'simple_rnn' , 'lstm' , and 'gru' . If
set, units must also be specified and rnn_cell_fn must be None .
|
rnn_cell_fn
|
A function that returns a RNN cell instance that will be used
to construct the RNN. If set, units and cell_type cannot be set.
This is for advanced users who need additional customization beyond
units and cell_type . Note that tf.keras.layers.StackedRNNCells is
needed for stacked RNNs.
|
return_sequences
|
A boolean indicating whether to return the last output in the output sequence, or the full sequence. |
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. |
optimizer
|
An instance of tf.Optimizer or string specifying optimizer
type. Defaults to Adagrad optimizer.
|
config
|
RunConfig object to configure the runtime settings.
|
Raises | |
---|---|
ValueError
|
If units , cell_type , and rnn_cell_fn are not
compatible.
|
Eager Compatibility
Estimators are not compatible with eager execution.
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:
|
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 path to the exported directory as a bytes object. |