DynamicRnnEstimator
Inherits From: Estimator
Defined in tensorflow/contrib/learn/python/learn/estimators/dynamic_rnn_estimator.py
.
Dynamically unrolled RNN (deprecated).
THIS CLASS IS DEPRECATED. See contrib/learn/README.md for general migration instructions.
config
model_dir
Returns a path in which the eval process will look for checkpoints.
model_fn
Returns the model_fn which is bound to self.params.
The model_fn with the following signature: def model_fn(features, labels, mode, metrics)
__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
.
problem_type
: whether the Estimator
is intended for a regression or classification problem. Value must be one of ProblemType.CLASSIFICATION
or ProblemType.LINEAR_REGRESSION
.prediction_type
: whether the Estimator
should return a value for each step in the sequence, or just a single value for the final time step. Must be one of PredictionType.SINGLE_VALUE
or PredictionType.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 from FeatureColumn
.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 from FeatureColumn
.num_classes
: the number of classes for a classification problem. Only used when problem_type=ProblemType.CLASSIFICATION
.num_units
: A list of integers indicating the number of units in the RNNCell
s in each layer.cell_type
: A subclass of RNNCell
or one of 'basic_rnn,' 'lstm' or 'gru'.optimizer
: The type of optimizer to use. Either a subclass of Optimizer
, an instance of an Optimizer
, a callback that returns an optimizer, or a string. Strings must be one of 'Adagrad', 'Adam', 'Ftrl', 'Momentum', 'RMSProp' or 'SGD. See layers.optimize_loss
for more details.learning_rate
: Learning rate. This argument has no effect if optimizer
is an instance of an Optimizer
.predict_probabilities
: A boolean indicating whether to predict probabilities for all classes. Used only if problem_type
is ProblemType.CLASSIFICATION
momentum
: Momentum value. Only used if optimizer_type
is 'Momentum'.gradient_clipping_norm
: Parameter used for gradient clipping. If None
, then no clipping is performed.dropout_keep_probabilities
: a list of dropout probabilities or None
. If a list is given, it must have length len(num_units) + 1
. If None
, 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 of input_fn
and returns features and labels which will be fed into model_fn
. Please check model_fn
for a definition of features and labels.config
: A RunConfig
instance.ValueError
: problem_type
is not one of ProblemType.LINEAR_REGRESSION
or ProblemType.CLASSIFICATION
.ValueError
: problem_type
is ProblemType.CLASSIFICATION
but num_classes
is not specified.ValueError
: prediction_type
is not one of PredictionType.MULTIPLE_VALUE
or PredictionType.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(...))
ValueError
: If at least one of x
or y
is provided, and at least one of input_fn
or feed_fn
is provided. Or if metrics
is not None
or dict
.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.
export_dir
: A string containing a directory to write the exported graph and checkpoints.input_fn
: If use_deprecated_input_fn
is true, then a function that given Tensor
of Example
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 to Tensor
and labels is a Tensor
that's currently not used (and so can be None
).input_feature_key
: Only used if use_deprecated_input_fn
is false. String key into the features dict returned by input_fn
that corresponds to a the raw Example
strings Tensor
that the exported model will take as input. Can only be None
if you're using a custom signature_fn
that does not use the first arg (examples).use_deprecated_input_fn
: Determines the signature format of input_fn
.signature_fn
: Function that returns a default signature and a named signature map, given Tensor
of Example
strings, dict
of Tensor
s for features and Tensor
or dict
of Tensor
s for predictions.prediction_key
: The key for a tensor in the predictions
dict (output from the model_fn
) to use as the predictions
input to the signature_fn
. Optional. If None
, predictions will pass to signature_fn
without filtering.default_batch_size
: Default batch size of the Example
placeholder.exports_to_keep
: Number of exports to keep.checkpoint_path
: the checkpoint path of the model to be exported. If it is None
(which is default), will use the latest checkpoint in export_dir.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, graph_rewrite_specs=(GraphRewriteSpec((tag_constants.SERVING,), ()),), strip_default_attrs=False )
Exports inference graph as a SavedModel into given dir.
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 an InputFnOps
.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.graph_rewrite_specs
: an iterable of GraphRewriteSpec
. Each element will produce a separate MetaGraphDef within the exported SavedModel, tagged and rewritten as specified. Defaults to a single entry using the default serving tag ("serve") and no rewriting.strip_default_attrs
: Boolean. If True
, default-valued attributes will be removed from the NodeDefs. For a detailed guide, see Stripping Default-Valued Attributes.The string path to the exported directory.
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(...))
ValueError
: If x
or y
are not None
while input_fn
is not None
.ValueError
: If both steps
and max_steps
are not None
.get_params
get_params(deep=True)
Get parameters for this estimator.
deep
: boolean, optional
If True
, will return the parameters for this estimator and contained subobjects that are estimators.
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.
List of names.
get_variable_value
get_variable_value(name)
Returns value of the variable given by name.
name
: string, name of the tensor.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.
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 be None
.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 be None
.input_fn
: Input function. If set, x
, y
, and batch_size
must be None
.steps
: Number of steps for which to train model. If None
, train forever.batch_size
: minibatch size to use on the input, defaults to first dimension of x
. Must be None
if input_fn
is provided.monitors
: List of BaseMonitor
subclass instances. Used for callbacks inside the training loop.self
, for chaining.
ValueError
: If at least one of x
and y
is provided, and input_fn
is provided.predict
predict( x=None, input_fn=None, batch_size=None, outputs=None, as_iterable=True, iterate_batches=False )
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(...))
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 be None
.input_fn
: Input function. If set, x
and 'batch_size' must be None
.batch_size
: Override default batch size. If set, 'input_fn' must be 'None'.outputs
: list of str
, name of the output to predict. If None
, 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).iterate_batches
: If True, yield the whole batch at once instead of decomposing the batch into individual samples. Only relevant when as_iterable is True.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.
ValueError
: If x and input_fn are both provided or both None
.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.
**params
: Parameters.self
ValueError
: If params contain invalid names.
© 2018 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