contrib.learn.DynamicRnnEstimator
tf.contrib.learn.DynamicRnnEstimator
class tf.contrib.learn.DynamicRnnEstimator
Defined in tensorflow/contrib/learn/python/learn/estimators/dynamic_rnn_estimator.py
.
Properties
config
model_dir
Methods
__init__
__init__( problem_type, prediction_type, sequence_feature_columns, context_feature_columns=None, num_classes=None, num_units=None, cell_type='basic_rnn', optimizer='SGD', learning_rate=0.1, predict_probabilities=False, momentum=None, gradient_clipping_norm=5.0, dropout_keep_probabilities=None, model_dir=None, feature_engineering_fn=None, config=None )
Initializes a DynamicRnnEstimator
.
The input function passed to this Estimator
optionally contains keys RNNKeys.SEQUENCE_LENGTH_KEY
. The value corresponding to RNNKeys.SEQUENCE_LENGTH_KEY
must be vector of size batch_size
where entry n
corresponds to the length of the n
th sequence in the batch. The sequence length feature is required for batches of varying sizes. It will be used to calculate loss and evaluation metrics. If RNNKeys.SEQUENCE_LENGTH_KEY
is not included, all sequences are assumed to have length equal to the size of dimension 1 of the input to the RNN.
In order to specify an initial state, the input function must include keys STATE_PREFIX_i
for all 0 <= i < n
where n
is the number of nested elements in cell.state_size
. The input function must contain values for all state components or none of them. If none are included, then the default (zero) state is used as an initial state. See the documentation for dict_to_state_tuple
and state_tuple_to_dict
for further details. The input function can call rnn_common.construct_rnn_cell() to obtain the same cell type that this class will select from arguments to init.
The predict()
method of the Estimator
returns a dictionary with keys STATE_PREFIX_i
for 0 <= i < n
where n
is the number of nested elements in cell.state_size
, along with PredictionKey.CLASSES
for problem type CLASSIFICATION
or PredictionKey.SCORES
for problem type LINEAR_REGRESSION
. The value keyed by PredictionKey.CLASSES
or PredictionKey.SCORES
has shape [batch_size, padded_length]
in the multi-value case and shape [batch_size]
in the single-value case. Here, padded_length
is the largest value in the RNNKeys.SEQUENCE_LENGTH
Tensor
passed as input. Entry [i, j]
is the prediction associated with sequence i
and time step j
. If the problem type is CLASSIFICATION
and predict_probabilities
is True
, it will also include keyPredictionKey.PROBABILITIES
.
Args:
-
problem_type
: whether theEstimator
is intended for a regression or classification problem. Value must be one ofProblemType.CLASSIFICATION
orProblemType.LINEAR_REGRESSION
. -
prediction_type
: whether theEstimator
should return a value for each step in the sequence, or just a single value for the final time step. Must be one ofPredictionType.SINGLE_VALUE
orPredictionType.MULTIPLE_VALUE
. -
sequence_feature_columns
: An iterable containing all the feature columns describing sequence features. All items in the iterable should be instances of classes derived fromFeatureColumn
. -
context_feature_columns
: An iterable containing all the feature columns describing context features, i.e., features that apply across all time steps. All items in the set should be instances of classes derived fromFeatureColumn
. -
num_classes
: the number of classes for a classification problem. Only used whenproblem_type=ProblemType.CLASSIFICATION
. -
num_units
: A list of integers indicating the number of units in theRNNCell
s in each layer. -
cell_type
: A subclass ofRNNCell
or one of 'basic_rnn,' 'lstm' or 'gru'. -
optimizer
: The type of optimizer to use. Either a subclass ofOptimizer
, an instance of anOptimizer
, a callback that returns an optimizer, or a string. Strings must be one of 'Adagrad', 'Adam', 'Ftrl', 'Momentum', 'RMSProp' or 'SGD. Seelayers.optimize_loss
for more details. -
learning_rate
: Learning rate. This argument has no effect ifoptimizer
is an instance of anOptimizer
. -
predict_probabilities
: A boolean indicating whether to predict probabilities for all classes. Used only ifproblem_type
isProblemType.CLASSIFICATION
-
momentum
: Momentum value. Only used ifoptimizer_type
is 'Momentum'. -
gradient_clipping_norm
: Parameter used for gradient clipping. IfNone
, then no clipping is performed. -
dropout_keep_probabilities
: a list of dropout probabilities orNone
. If a list is given, it must have lengthlen(num_units) + 1
. IfNone
, then no dropout is applied. -
model_dir
: The directory in which to save and restore the model graph, parameters, etc. -
feature_engineering_fn
: Takes features and labels which are the output ofinput_fn
and returns features and labels which will be fed intomodel_fn
. Please checkmodel_fn
for a definition of features and labels. -
config
: ARunConfig
instance.
Raises:
-
ValueError
:problem_type
is not one ofProblemType.LINEAR_REGRESSION
orProblemType.CLASSIFICATION
. -
ValueError
:problem_type
isProblemType.CLASSIFICATION
butnum_classes
is not specifieProblemType -
ValueError
:prediction_type
is not one ofPredictionType.MULTIPLE_VALUE
orPredictionType.SINGLE_VALUE
.
evaluate
evaluate( x=None, y=None, input_fn=None, feed_fn=None, batch_size=None, steps=None, metrics=None, name=None, checkpoint_path=None, hooks=None, log_progress=True )
See Evaluable
. (deprecated arguments)
SOME ARGUMENTS ARE DEPRECATED. They will be removed after 2016-12-01. Instructions for updating: Estimator is decoupled from Scikit Learn interface by moving into separate class SKCompat. Arguments x, y and batch_size are only available in the SKCompat class, Estimator will only accept input_fn. Example conversion: est = Estimator(...) -> est = SKCompat(Estimator(...))
Raises:
-
ValueError
: If at least one ofx
ory
is provided, and at least one ofinput_fn
orfeed_fn
is provided. Or ifmetrics
is notNone
ordict
.
export
export( export_dir, input_fn=export._default_input_fn, input_feature_key=None, use_deprecated_input_fn=True, signature_fn=None, prediction_key=None, default_batch_size=1, exports_to_keep=None, checkpoint_path=None )
Exports inference graph into given dir. (deprecated)
THIS FUNCTION IS DEPRECATED. It will be removed after 2017-03-25. Instructions for updating: Please use Estimator.export_savedmodel() instead.
Args:
-
export_dir
: A string containing a directory to write the exported graph and checkpoints. -
input_fn
: Ifuse_deprecated_input_fn
is true, then a function that givenTensor
ofExample
strings, parses it into features that are then passed to the model. Otherwise, a function that takes no argument and returns a tuple of (features, labels), where features is a dict of string key toTensor
and labels is aTensor
that's currently not used (and so can beNone
). -
input_feature_key
: Only used ifuse_deprecated_input_fn
is false. String key into the features dict returned byinput_fn
that corresponds to a the rawExample
stringsTensor
that the exported model will take as input. Can only beNone
if you're using a customsignature_fn
that does not use the first arg (examples). -
use_deprecated_input_fn
: Determines the signature format ofinput_fn
. -
signature_fn
: Function that returns a default signature and a named signature map, givenTensor
ofExample
strings,dict
ofTensor
s for features andTensor
ordict
ofTensor
s for predictions. -
prediction_key
: The key for a tensor in thepredictions
dict (output from themodel_fn
) to use as thepredictions
input to thesignature_fn
. Optional. IfNone
, predictions will pass tosignature_fn
without filtering. -
default_batch_size
: Default batch size of theExample
placeholder. -
exports_to_keep
: Number of exports to keep. -
checkpoint_path
: the checkpoint path of the model to be exported. If it isNone
(which is default), will use the latest checkpoint in export_dir.
Returns:
The string path to the exported directory. NB: this functionality was added ca. 2016/09/25; clients that depend on the return value may need to handle the case where this function returns None because subclasses are not returning a value.
export_savedmodel
export_savedmodel( export_dir_base, serving_input_fn, default_output_alternative_key=None, assets_extra=None, as_text=False, checkpoint_path=None )
Exports inference graph as a SavedModel into given dir.
Args:
-
export_dir_base
: A string containing a directory to write the exported graph and checkpoints. -
serving_input_fn
: A function that takes no argument and returns anInputFnOps
. -
default_output_alternative_key
: the name of the head to serve when none is specified. Not needed for single-headed models. -
assets_extra
: A dict specifying how to populate the assets.extra directory within the exported SavedModel. Each key should give the 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'}
. -
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 an unrecognized export_type is requested.
fit
fit( x=None, y=None, input_fn=None, steps=None, batch_size=None, monitors=None, max_steps=None )
See Trainable
. (deprecated arguments)
SOME ARGUMENTS ARE DEPRECATED. They will be removed after 2016-12-01. Instructions for updating: Estimator is decoupled from Scikit Learn interface by moving into separate class SKCompat. Arguments x, y and batch_size are only available in the SKCompat class, Estimator will only accept input_fn. Example conversion: est = Estimator(...) -> est = SKCompat(Estimator(...))
Raises:
-
ValueError
: Ifx
ory
are notNone
whileinput_fn
is notNone
. -
ValueError
: If bothsteps
andmax_steps
are notNone
.
get_params
get_params(deep=True)
Get parameters for this estimator.
Args:
-
deep
: boolean, optionalIf
True
, will return the parameters for this estimator and contained subobjects that are estimators.
Returns:
params : mapping of string to any Parameter names mapped to their values.
get_variable_names
get_variable_names()
Returns list of all variable names in this model.
Returns:
List of names.
get_variable_value
get_variable_value(name)
Returns value of the variable given by name.
Args:
-
name
: string, name of the tensor.
Returns:
Numpy array - value of the tensor.
partial_fit
partial_fit( x=None, y=None, input_fn=None, steps=1, batch_size=None, monitors=None )
Incremental fit on a batch of samples. (deprecated arguments)
SOME ARGUMENTS ARE DEPRECATED. They will be removed after 2016-12-01. Instructions for updating: Estimator is decoupled from Scikit Learn interface by moving into separate class SKCompat. Arguments x, y and batch_size are only available in the SKCompat class, Estimator will only accept input_fn. Example conversion: est = Estimator(...) -> est = SKCompat(Estimator(...))
This method is expected to be called several times consecutively on different or the same chunks of the dataset. This either can implement iterative training or out-of-core/online training.
This is especially useful when the whole dataset is too big to fit in memory at the same time. Or when model is taking long time to converge, and you want to split up training into subparts.
Args:
-
x
: Matrix of shape [n_samples, n_features...]. Can be iterator that returns arrays of features. The training input samples for fitting the model. If set,input_fn
must beNone
. -
y
: Vector or matrix [n_samples] or [n_samples, n_outputs]. Can be iterator that returns array of labels. The training label values (class labels in classification, real numbers in regression). If set,input_fn
must beNone
. -
input_fn
: Input function. If set,x
,y
, andbatch_size
must beNone
. -
steps
: Number of steps for which to train model. IfNone
, train forever. -
batch_size
: minibatch size to use on the input, defaults to first dimension ofx
. Must beNone
ifinput_fn
is provided. -
monitors
: List ofBaseMonitor
subclass instances. Used for callbacks inside the training loop.
Returns:
self
, for chaining.
Raises:
-
ValueError
: If at least one ofx
andy
is provided, andinput_fn
is provided.
predict
predict( x=None, input_fn=None, batch_size=None, outputs=None, as_iterable=True )
Returns predictions for given features. (deprecated arguments)
SOME ARGUMENTS ARE DEPRECATED. They will be removed after 2016-12-01. Instructions for updating: Estimator is decoupled from Scikit Learn interface by moving into separate class SKCompat. Arguments x, y and batch_size are only available in the SKCompat class, Estimator will only accept input_fn. Example conversion: est = Estimator(...) -> est = SKCompat(Estimator(...))
Args:
-
x
: Matrix of shape [n_samples, n_features...]. Can be iterator that returns arrays of features. The training input samples for fitting the model. If set,input_fn
must beNone
. -
input_fn
: Input function. If set,x
and 'batch_size' must beNone
. -
batch_size
: Override default batch size. If set, 'input_fn' must be 'None'. -
outputs
: list ofstr
, name of the output to predict. IfNone
, returns all. -
as_iterable
: If True, return an iterable which keeps yielding predictions for each example until inputs are exhausted. Note: The inputs must terminate if you want the iterable to terminate (e.g. be sure to pass num_epochs=1 if you are using something like read_batch_features).
Returns:
A numpy array of predicted classes or regression values if the constructor's model_fn
returns a Tensor
for predictions
or a dict
of numpy arrays if model_fn
returns a dict
. Returns an iterable of predictions if as_iterable is True.
Raises:
-
ValueError
: If x and input_fn are both provided or bothNone
.
set_params
set_params(**params)
Set the parameters of this estimator.
The method works on simple estimators as well as on nested objects (such as pipelines). The former have parameters of the form <component>__<parameter>
so that it's possible to update each component of a nested object.
Args:
**params: Parameters.
Returns:
self
Raises:
-
ValueError
: If params contain invalid names.
© 2017 The TensorFlow Authors. All rights reserved.
Licensed under the Creative Commons Attribution License 3.0.
Code samples licensed under the Apache 2.0 License.
https://www.tensorflow.org/api_docs/python/tf/contrib/learn/DynamicRnnEstimator