tf.contrib.learn.DynamicRnnEstimator

Class 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.

__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 nth 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 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 RNNCells 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.

Raises:

  • 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.

Properties

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.

Returns:

The model_fn with the following signature: def model_fn(features, labels, mode, metrics)

Methods

tf.contrib.learn.DynamicRnnEstimator.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)

Raises:

  • 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.

tf.contrib.learn.DynamicRnnEstimator.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)

Args:

  • 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 Tensors for features and Tensor or dict of Tensors 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.

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.

tf.contrib.learn.DynamicRnnEstimator.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.

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 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.

Returns:

The string path to the exported directory.

Raises:

  • ValueError: if an unrecognized export_type is requested.

tf.contrib.learn.DynamicRnnEstimator.fit

fit(
    x=None,
    y=None,
    input_fn=None,
    steps=None,
    batch_size=None,
    monitors=None,
    max_steps=None
)

See Trainable. (deprecated arguments)

Raises:

  • ValueError: If x or y are not None while input_fn is not None.
  • ValueError: If both steps and max_steps are not None.

tf.contrib.learn.DynamicRnnEstimator.get_params

get_params(deep=True)

Get parameters for this estimator.

Args:

  • deep: boolean, optional

    If 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.

tf.contrib.learn.DynamicRnnEstimator.get_variable_names

get_variable_names()

Returns list of all variable names in this model.

Returns:

List of names.

tf.contrib.learn.DynamicRnnEstimator.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.

tf.contrib.learn.DynamicRnnEstimator.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)

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 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.

Returns:

self, for chaining.

Raises:

  • ValueError: If at least one of x and y is provided, and input_fn is provided.

tf.contrib.learn.DynamicRnnEstimator.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)

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 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.

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 both None.

tf.contrib.learn.DynamicRnnEstimator.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.