LostTech.TensorFlow : API Documentation

Type RNNClassifier

Namespace tensorflow_estimator.contrib.estimator

Parent Estimator

Interfaces IRNNClassifier

Methods

Properties

Public instance methods

object eval_dir(string name)

object eval_dir_dyn(object name)

IDictionary<object, object> evaluate(PythonFunctionContainer input_fn, int steps, IEnumerable<object> hooks, object checkpoint_path, string name)

IDictionary<object, object> evaluate(PythonFunctionContainer input_fn, string steps, IEnumerable<object> hooks, object checkpoint_path, string name)

object evaluate_dyn(object input_fn, object steps, object hooks, object checkpoint_path, object name)

object export_savedmodel(object export_dir_base, Evaluable serving_input_receiver_fn, IDictionary<string, string> assets_extra, bool as_text, object checkpoint_path, bool strip_default_attrs)

object export_savedmodel(object export_dir_base, Estimator serving_input_receiver_fn, IDictionary<string, string> assets_extra, bool as_text, object checkpoint_path, bool strip_default_attrs)

object export_savedmodel(object export_dir_base, PythonFunctionContainer serving_input_receiver_fn, IDictionary<string, string> assets_extra, bool as_text, object checkpoint_path, bool strip_default_attrs)

object export_savedmodel_dyn(object export_dir_base, object serving_input_receiver_fn, object assets_extra, ImplicitContainer<T> as_text, object checkpoint_path, ImplicitContainer<T> strip_default_attrs)

IList<object> get_variable_names()

Returns list of all variable names in this model.
Returns
IList<object>
List of names.

object get_variable_names_dyn()

Returns list of all variable names in this model.
Returns
object
List of names.

object get_variable_value(string name)

Returns value of the variable given by name.
Parameters
string name
string, name of the tensor.
Returns
object
Numpy array - value of the tensor.

object get_variable_value_dyn(object name)

Returns value of the variable given by name.
Parameters
object name
string, name of the tensor.
Returns
object
Numpy array - value of the tensor.

object latest_checkpoint()

object latest_checkpoint_dyn()

IEnumerator<object> predict(ValueTuple<object, object, object> input_fn, IEnumerable<string> predict_keys, IEnumerable<object> hooks, object checkpoint_path, bool yield_single_examples)

IEnumerator<object> predict(PythonFunctionContainer input_fn, IEnumerable<string> predict_keys, IEnumerable<object> hooks, object checkpoint_path, bool yield_single_examples)

object predict_dyn(object input_fn, object predict_keys, object hooks, object checkpoint_path, ImplicitContainer<T> yield_single_examples)

Returns predictions for given features. (deprecated argument values) (deprecated argument values)

Warning: SOME ARGUMENT VALUES ARE DEPRECATED: `(as_iterable=False)`. They will be removed after 2016-09-15. Instructions for updating: The default behavior of predict() is changing. The default value for as_iterable will change to True, and then the flag will be removed altogether. The behavior of this flag is described below.

Warning: SOME ARGUMENT VALUES ARE DEPRECATED: `(outputs=None)`. They will be removed after 2017-03-01. Instructions for updating: Please switch to predict_classes, or set `outputs` argument.

By default, returns predicted classes. But this default will be dropped soon. Users should either pass `outputs`, or call `predict_classes` method.
Parameters
object input_fn
Input function. If set, x must be None.
object predict_keys
object hooks
object checkpoint_path
ImplicitContainer<T> yield_single_examples
Returns
object
Numpy array of predicted classes with shape [batch_size] (or an iterable of predicted classes if as_iterable is True). Each predicted class is represented by its class index (i.e. integer from 0 to n_classes-1). If `outputs` is set, returns a dict of predictions.

object train(string input_fn, object hooks, int steps, string max_steps, string saving_listeners)

object train(string input_fn, int hooks, int steps, int max_steps, string saving_listeners)

object train(PythonFunctionContainer input_fn, int hooks, int steps, string max_steps, string saving_listeners)

object train(string input_fn, IEnumerable<object> hooks, string steps, string max_steps, string saving_listeners)

object train(PythonFunctionContainer input_fn, IEnumerable<object> hooks, int steps, int max_steps, string saving_listeners)

object train(PythonFunctionContainer input_fn, IEnumerable<object> hooks, int steps, string max_steps, string saving_listeners)

object train(string input_fn, IEnumerable<object> hooks, string steps, int max_steps, string saving_listeners)

object train(PythonFunctionContainer input_fn, int hooks, string steps, int max_steps, string saving_listeners)

object train(PythonFunctionContainer input_fn, IEnumerable<object> hooks, string steps, int max_steps, string saving_listeners)

object train(PythonFunctionContainer input_fn, int hooks, string steps, string max_steps, string saving_listeners)

object train(PythonFunctionContainer input_fn, IEnumerable<object> hooks, string steps, string max_steps, string saving_listeners)

object train(PythonFunctionContainer input_fn, object hooks, int steps, int max_steps, string saving_listeners)

object train(string input_fn, IEnumerable<object> hooks, int steps, string max_steps, string saving_listeners)

object train(string input_fn, object hooks, string steps, string max_steps, string saving_listeners)

object train(PythonFunctionContainer input_fn, object hooks, int steps, string max_steps, string saving_listeners)

object train(PythonFunctionContainer input_fn, int hooks, int steps, int max_steps, string saving_listeners)

object train(string input_fn, IEnumerable<object> hooks, int steps, int max_steps, string saving_listeners)

object train(PythonFunctionContainer input_fn, object hooks, string steps, int max_steps, string saving_listeners)

object train(string input_fn, int hooks, int steps, string max_steps, string saving_listeners)

object train(PythonFunctionContainer input_fn, object hooks, string steps, string max_steps, string saving_listeners)

object train(string input_fn, object hooks, string steps, int max_steps, string saving_listeners)

object train_dyn(object input_fn, object hooks, object steps, object max_steps, object saving_listeners)

Public static methods

RNNClassifier NewDyn(object sequence_feature_columns, object context_feature_columns, object num_units, ImplicitContainer<T> cell_type, object rnn_cell_fn, object model_dir, ImplicitContainer<T> n_classes, object weight_column, object label_vocabulary, ImplicitContainer<T> optimizer, ImplicitContainer<T> loss_reduction, object input_layer_partitioner, object config)

Public properties

object config get;

object config_dyn get;

object model_dir get;

object model_dir_dyn get;

object model_fn get;

object model_fn_dyn get;

object params get;

object params_dyn get;

object PythonObject get;