Module: contrib.learn

Module: tf.contrib.learn

Module tf.contrib.learn

Defined in tensorflow/contrib/learn/__init__.py.

High level API for learning.

See the Learn (contrib) guide.

Modules

datasets module: Dataset utilities and synthetic/reference datasets.

graph_actions module: High level operations on graphs.

head module: Abstractions for the head(s) of a model.

io module: Tools to allow different io formats.

learn_runner module: Utilities to run and tune an Experiment.

models module: Various high level TF models.

monitors module: Monitors instrument the training process.

ops module: Various TensorFlow Ops.

preprocessing module: Preprocessing tools useful for building models.

utils module: TensorFlow Learn Utils.

Classes

class BaseEstimator: Abstract BaseEstimator class to train and evaluate TensorFlow models.

class DNNClassifier: A classifier for TensorFlow DNN models.

class DNNEstimator: A Estimator for TensorFlow DNN models with user specified _Head.

class DNNLinearCombinedClassifier: A classifier for TensorFlow Linear and DNN joined training models.

class DNNLinearCombinedEstimator: An estimator for TensorFlow Linear and DNN joined training models.

class DNNLinearCombinedRegressor: A regressor for TensorFlow Linear and DNN joined training models.

class DNNRegressor: A regressor for TensorFlow DNN models.

class DynamicRnnEstimator

class Estimator: Estimator class is the basic TensorFlow model trainer/evaluator.

class Evaluable: Interface for objects that are evaluatable by, e.g., Experiment.

class Experiment: Experiment is a class containing all information needed to train a model.

class ExportStrategy: A class representing a type of model export.

class Head: Interface for the head/top of a model.

class InputFnOps: A return type for an input_fn.

class KMeansClustering: An Estimator for K-Means clustering.

class LinearClassifier: Linear classifier model.

class LinearEstimator: Linear model with user specified head.

class LinearRegressor: Linear regressor model.

class MetricSpec: MetricSpec connects a model to metric functions.

class ModeKeys: Standard names for model modes.

class ModelFnOps: Ops returned from a model_fn.

class NanLossDuringTrainingError

class NotFittedError: Exception class to raise if estimator is used before fitting.

class PredictionKey

class ProblemType: Enum-like values for the type of problem that the model solves.

class RunConfig: This class specifies the configurations for an Estimator run.

class SKCompat: Scikit learn wrapper for TensorFlow Learn Estimator.

class SVM: Support Vector Machine (SVM) model for binary classification.

class TaskType

class Trainable: Interface for objects that are trainable by, e.g., Experiment.

Functions

LogisticRegressor(...): Builds a logistic regression Estimator for binary classification.

binary_svm_head(...): Creates a Head for binary classification with SVMs.

build_parsing_serving_input_fn(...): Build an input_fn appropriate for serving, expecting fed tf.Examples.

evaluate(...): Evaluate a model loaded from a checkpoint. (deprecated)

extract_dask_data(...): Extract data from dask.Series or dask.DataFrame for predictors.

extract_dask_labels(...): Extract data from dask.Series or dask.DataFrame for labels.

extract_pandas_data(...): Extract data from pandas.DataFrame for predictors.

extract_pandas_labels(...): Extract data from pandas.DataFrame for labels.

extract_pandas_matrix(...): Extracts numpy matrix from pandas DataFrame.

infer(...): Restore graph from restore_checkpoint_path and run output_dict tensors. (deprecated)

infer_real_valued_columns_from_input(...): Creates FeatureColumn objects for inputs defined by input x.

infer_real_valued_columns_from_input_fn(...): Creates FeatureColumn objects for inputs defined by input_fn.

make_export_strategy(...): Create an ExportStrategy for use with Experiment.

multi_class_head(...): Creates a Head for multi class single label classification.

multi_head(...): Creates a MultiHead stemming from same logits/hidden layer.

multi_label_head(...): Creates a Head for multi label classification.

no_op_train_fn(...)

poisson_regression_head(...): Creates a Head for poisson regression.

read_batch_examples(...): Adds operations to read, queue, batch Example protos.

read_batch_features(...): Adds operations to read, queue, batch and parse Example protos.

read_batch_record_features(...): Reads TFRecord, queues, batches and parses Example proto.

regression_head(...): Creates a Head for linear regression.

run_feeds(...): See run_feeds_iter(). Returns a list instead of an iterator. (deprecated)

run_n(...): Run output_dict tensors n times, with the same feed_dict each run. (deprecated)

train(...): Train a model. (deprecated)