tf.contrib.factorization.KMeansClustering

Class KMeansClustering

Inherits From: Estimator

Defined in tensorflow/contrib/factorization/python/ops/kmeans.py.

An Estimator for K-Means clustering.

Example:

import numpy as np
import tensorflow as tf

num_points = 100
dimensions = 2
points = np.random.uniform(0, 1000, [num_points, dimensions])

def input_fn():
  return tf.train.limit_epochs(
      tf.convert_to_tensor(points, dtype=tf.float32), num_epochs=1)

num_clusters = 5
kmeans = tf.contrib.factorization.KMeansClustering(
    num_clusters=num_clusters, use_mini_batch=False)

# train
num_iterations = 10
previous_centers = None
for _ in xrange(num_iterations):
  kmeans.train(input_fn)
  cluster_centers = kmeans.cluster_centers()
  if previous_centers is not None:
    print 'delta:', cluster_centers - previous_centers
  previous_centers = cluster_centers
  print 'score:', kmeans.score(input_fn)
print 'cluster centers:', cluster_centers

# map the input points to their clusters
cluster_indices = list(kmeans.predict_cluster_index(input_fn))
for i, point in enumerate(points):
  cluster_index = cluster_indices[i]
  center = cluster_centers[cluster_index]
  print 'point:', point, 'is in cluster', cluster_index, 'centered at', center

The SavedModel saved by the export_savedmodel method does not include the cluster centers. However, the cluster centers may be retrieved by the latest checkpoint saved during training. Specifically,

kmeans.cluster_centers()

is equivalent to

tf.train.load_variable(
    kmeans.model_dir, KMeansClustering.CLUSTER_CENTERS_VAR_NAME)

__init__

__init__(
    num_clusters,
    model_dir=None,
    initial_clusters=RANDOM_INIT,
    distance_metric=SQUARED_EUCLIDEAN_DISTANCE,
    random_seed=0,
    use_mini_batch=True,
    mini_batch_steps_per_iteration=1,
    kmeans_plus_plus_num_retries=2,
    relative_tolerance=None,
    config=None,
    feature_columns=None
)

Creates an Estimator for running KMeans training and inference.

This Estimator implements the following variants of the K-means algorithm:

If use_mini_batch is False, it runs standard full batch K-means. Each training step runs a single iteration of K-Means and must process the full input at once. To run in this mode, the input_fn passed to train must return the entire input dataset.

If use_mini_batch is True, it runs a generalization of the mini-batch K-means algorithm. It runs multiple iterations, where each iteration is composed of mini_batch_steps_per_iteration steps. Each training step accumulates the contribution from one mini-batch into temporary storage. Every mini_batch_steps_per_iteration steps, the cluster centers are updated and the temporary storage cleared for the next iteration. Note that: * If mini_batch_steps_per_iteration=1, the algorithm reduces to the standard K-means mini-batch algorithm. * If mini_batch_steps_per_iteration = num_inputs / batch_size, the algorithm becomes an asynchronous version of the full-batch algorithm. However, there is no guarantee by this implementation that each input is seen exactly once per iteration. Also, different updates are applied asynchronously without locking. So this asynchronous version may not behave exactly like a full-batch version.

Args:

  • num_clusters: An integer tensor specifying the number of clusters. This argument is ignored if initial_clusters is a tensor or numpy array.
  • model_dir: The directory to save the model results and log files.
  • initial_clusters: Specifies how the initial cluster centers are chosen. One of the following:
    • a tensor or numpy array with the initial cluster centers.
    • a callable f(inputs, k) that selects and returns up to k centers from an input batch. f is free to return any number of centers from 0 to k. It will be invoked on successive input batches as necessary until all num_clusters centers are chosen.
    • KMeansClustering.RANDOM_INIT: Choose centers randomly from an input batch. If the batch size is less than num_clusters then the entire batch is chosen to be initial cluster centers and the remaining centers are chosen from successive input batches.
    • KMeansClustering.KMEANS_PLUS_PLUS_INIT: Use kmeans++ to choose centers from the first input batch. If the batch size is less than num_clusters, a TensorFlow runtime error occurs.
  • distance_metric: The distance metric used for clustering. One of:
    • KMeansClustering.SQUARED_EUCLIDEAN_DISTANCE: Euclidean distance between vectors u and v is defined as (||u - v||_2) which is the square root of the sum of the absolute squares of the elements' difference.
    • KMeansClustering.COSINE_DISTANCE: Cosine distance between vectors u and v is defined as (1 - (u . v) / (||u||_2 ||v||_2)).
  • random_seed: Python integer. Seed for PRNG used to initialize centers.
  • use_mini_batch: A boolean specifying whether to use the mini-batch k-means algorithm. See explanation above.
  • mini_batch_steps_per_iteration: The number of steps after which the updated cluster centers are synced back to a master copy. Used only if use_mini_batch=True. See explanation above.
  • kmeans_plus_plus_num_retries: For each point that is sampled during kmeans++ initialization, this parameter specifies the number of additional points to draw from the current distribution before selecting the best. If a negative value is specified, a heuristic is used to sample O(log(num_to_sample)) additional points. Used only if initial_clusters=KMeansClustering.KMEANS_PLUS_PLUS_INIT.
  • relative_tolerance: A relative tolerance of change in the loss between iterations. Stops learning if the loss changes less than this amount. This may not work correctly if use_mini_batch=True.
  • config: See tf.estimator.Estimator.
  • feature_columns: An optionable iterable containing all the feature columns used by the model. All items in the set should be feature column instances that can be passed to tf.feature_column.input_layer. If this is None, all features will be used.

Raises:

  • ValueError: An invalid argument was passed to initial_clusters or distance_metric.

Properties

config

model_dir

model_fn

Returns the model_fn which is bound to self.params.

Returns:

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

params

Methods

tf.contrib.factorization.KMeansClustering.cluster_centers

cluster_centers()

Returns the cluster centers.

tf.contrib.factorization.KMeansClustering.eval_dir

eval_dir(name=None)

Shows the directory name where evaluation metrics are dumped.

Args:

  • name: Name of the evaluation if user needs to run multiple evaluations on different data sets, such as on training data vs test data. Metrics for different evaluations are saved in separate folders, and appear separately in tensorboard.

Returns:

A string which is the path of directory contains evaluation metrics.

tf.contrib.factorization.KMeansClustering.evaluate

evaluate(
    input_fn,
    steps=None,
    hooks=None,
    checkpoint_path=None,
    name=None
)

Evaluates the model given evaluation data input_fn.

For each step, calls input_fn, which returns one batch of data. Evaluates until: - steps batches are processed, or - input_fn raises an end-of-input exception (tf.errors.OutOfRangeError or StopIteration).

Args:

  • input_fn: A function that constructs the input data for evaluation. See Premade Estimators for more information. The function should construct and return one of the following: * A tf.data.Dataset object: Outputs of Dataset object must be a tuple (features, labels) with same constraints as below. * A tuple (features, labels): Where features is a tf.Tensor or a dictionary of string feature name to Tensor and labels is a Tensor or a dictionary of string label name to Tensor. Both features and labels are consumed by model_fn. They should satisfy the expectation of model_fn from inputs.
  • steps: Number of steps for which to evaluate model. If None, evaluates until input_fn raises an end-of-input exception.
  • hooks: List of tf.train.SessionRunHook subclass instances. Used for callbacks inside the evaluation call.
  • checkpoint_path: Path of a specific checkpoint to evaluate. If None, the latest checkpoint in model_dir is used. If there are no checkpoints in model_dir, evaluation is run with newly initialized Variables instead of ones restored from checkpoint.
  • name: Name of the evaluation if user needs to run multiple evaluations on different data sets, such as on training data vs test data. Metrics for different evaluations are saved in separate folders, and appear separately in tensorboard.

Returns:

A dict containing the evaluation metrics specified in model_fn keyed by name, as well as an entry global_step which contains the value of the global step for which this evaluation was performed. For canned estimators, the dict contains the loss (mean loss per mini-batch) and the average_loss (mean loss per sample). Canned classifiers also return the accuracy. Canned regressors also return the label/mean and the prediction/mean.

Raises:

  • ValueError: If steps <= 0.
  • ValueError: If no model has been trained, namely model_dir, or the given checkpoint_path is empty.

tf.contrib.factorization.KMeansClustering.experimental_export_all_saved_models

experimental_export_all_saved_models(
    export_dir_base,
    input_receiver_fn_map,
    assets_extra=None,
    as_text=False,
    checkpoint_path=None
)

Exports a SavedModel with tf.MetaGraphDefs for each requested mode.

For each mode passed in via the input_receiver_fn_map, this method builds a new graph by calling the input_receiver_fn to obtain feature and label Tensors. Next, this method calls the Estimator's model_fn in the passed mode to generate the model graph based on those features and labels, and restores the given checkpoint (or, lacking that, the most recent checkpoint) into the graph. Only one of the modes is used for saving variables to the SavedModel (order of preference: tf.estimator.ModeKeys.TRAIN, tf.estimator.ModeKeys.EVAL, then tf.estimator.ModeKeys.PREDICT), such that up to three tf.MetaGraphDefs are saved with a single set of variables in a single SavedModel directory.

For the variables and tf.MetaGraphDefs, a timestamped export directory below export_dir_base, and writes a SavedModel into it containing the tf.MetaGraphDef for the given mode and its associated signatures.

For prediction, the exported MetaGraphDef will provide one SignatureDef for each element of the export_outputs dict returned from the model_fn, named using the same keys. One of these keys is always tf.saved_model.signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY, indicating which signature will be served when a serving request does not specify one. For each signature, the outputs are provided by the corresponding tf.estimator.export.ExportOutputs, and the inputs are always the input receivers provided by the serving_input_receiver_fn.

For training and evaluation, the train_op is stored in an extra collection, and loss, metrics, and predictions are included in a SignatureDef for the mode in question.

Extra assets may be written into the SavedModel via the assets_extra argument. This should be a dict, where each key gives a 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'}.

Args:

  • export_dir_base: A string containing a directory in which to create timestamped subdirectories containing exported SavedModels.
  • input_receiver_fn_map: dict of tf.estimator.ModeKeys to input_receiver_fn mappings, where the input_receiver_fn is a function that takes no arguments and returns the appropriate subclass of InputReceiver.
  • assets_extra: A dict specifying how to populate the assets.extra directory within the exported SavedModel, or None if no extra assets are needed.
  • 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 any input_receiver_fn is None, no export_outputs are provided, or no checkpoint can be found.

tf.contrib.factorization.KMeansClustering.export_saved_model

export_saved_model(
    export_dir_base,
    serving_input_receiver_fn,
    assets_extra=None,
    as_text=False,
    checkpoint_path=None,
    experimental_mode=model_fn_lib.ModeKeys.PREDICT
)

Exports inference graph as a SavedModel into the given dir.

For a detailed guide, see Using SavedModel with Estimators.

This method builds a new graph by first calling the serving_input_receiver_fn to obtain feature Tensors, and then calling this Estimator's model_fn to generate the model graph based on those features. It restores the given checkpoint (or, lacking that, the most recent checkpoint) into this graph in a fresh session. Finally it creates a timestamped export directory below the given export_dir_base, and writes a SavedModel into it containing a single tf.MetaGraphDef saved from this session.

The exported MetaGraphDef will provide one SignatureDef for each element of the export_outputs dict returned from the model_fn, named using the same keys. One of these keys is always tf.saved_model.signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY, indicating which signature will be served when a serving request does not specify one. For each signature, the outputs are provided by the corresponding tf.estimator.export.ExportOutputs, and the inputs are always the input receivers provided by the serving_input_receiver_fn.

Extra assets may be written into the SavedModel via the assets_extra argument. This should be a dict, where each key gives a 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'}.

The experimental_mode parameter can be used to export a single train/eval/predict graph as a SavedModel. See experimental_export_all_saved_models for full docs.

Args:

  • export_dir_base: A string containing a directory in which to create timestamped subdirectories containing exported SavedModels.
  • serving_input_receiver_fn: A function that takes no argument and returns a tf.estimator.export.ServingInputReceiver or tf.estimator.export.TensorServingInputReceiver.
  • assets_extra: A dict specifying how to populate the assets.extra directory within the exported SavedModel, or None if no extra assets are needed.
  • 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.
  • experimental_mode: tf.estimator.ModeKeys value indicating with mode will be exported. Note that this feature is experimental.

Returns:

The string path to the exported directory.

Raises:

  • ValueError: if no serving_input_receiver_fn is provided, no export_outputs are provided, or no checkpoint can be found.

tf.contrib.factorization.KMeansClustering.export_savedmodel

export_savedmodel(
    export_dir_base,
    serving_input_receiver_fn,
    assets_extra=None,
    as_text=False,
    checkpoint_path=None,
    strip_default_attrs=False
)

Exports inference graph as a SavedModel into the given dir.

For a detailed guide, see Using SavedModel with Estimators.

This method builds a new graph by first calling the serving_input_receiver_fn to obtain feature Tensors, and then calling this Estimator's model_fn to generate the model graph based on those features. It restores the given checkpoint (or, lacking that, the most recent checkpoint) into this graph in a fresh session. Finally it creates a timestamped export directory below the given export_dir_base, and writes a SavedModel into it containing a single tf.MetaGraphDef saved from this session.

The exported MetaGraphDef will provide one SignatureDef for each element of the export_outputs dict returned from the model_fn, named using the same keys. One of these keys is always tf.saved_model.signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY, indicating which signature will be served when a serving request does not specify one. For each signature, the outputs are provided by the corresponding tf.estimator.export.ExportOutputs, and the inputs are always the input receivers provided by the serving_input_receiver_fn.

Extra assets may be written into the SavedModel via the assets_extra argument. This should be a dict, where each key gives a 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'}.

Args:

  • export_dir_base: A string containing a directory in which to create timestamped subdirectories containing exported SavedModels.
  • serving_input_receiver_fn: A function that takes no argument and returns a tf.estimator.export.ServingInputReceiver or tf.estimator.export.TensorServingInputReceiver.
  • assets_extra: A dict specifying how to populate the assets.extra directory within the exported SavedModel, or None if no extra assets are needed.
  • 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.
  • 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 no serving_input_receiver_fn is provided, no export_outputs are provided, or no checkpoint can be found.

tf.contrib.factorization.KMeansClustering.get_variable_names

get_variable_names()

Returns list of all variable names in this model.

Returns:

List of names.

Raises:

  • ValueError: If the Estimator has not produced a checkpoint yet.

tf.contrib.factorization.KMeansClustering.get_variable_value

get_variable_value(name)

Returns value of the variable given by name.

Args:

  • name: string or a list of string, name of the tensor.

Returns:

Numpy array - value of the tensor.

Raises:

  • ValueError: If the Estimator has not produced a checkpoint yet.

tf.contrib.factorization.KMeansClustering.latest_checkpoint

latest_checkpoint()

Finds the filename of the latest saved checkpoint file in model_dir.

Returns:

The full path to the latest checkpoint or None if no checkpoint was found.

tf.contrib.factorization.KMeansClustering.predict

predict(
    input_fn,
    predict_keys=None,
    hooks=None,
    checkpoint_path=None,
    yield_single_examples=True
)

Yields predictions for given features.

Please note that interleaving two predict outputs does not work. See: issue/20506

Args:

  • input_fn: A function that constructs the features. Prediction continues until input_fn raises an end-of-input exception (tf.errors.OutOfRangeError or StopIteration). See Premade Estimators for more information. The function should construct and return one of the following:

    • A tf.data.Dataset object: Outputs of Dataset object must have same constraints as below.
    • features: A tf.Tensor or a dictionary of string feature name to Tensor. features are consumed by model_fn. They should satisfy the expectation of model_fn from inputs.
    • A tuple, in which case the first item is extracted as features.

  • predict_keys: list of str, name of the keys to predict. It is used if the tf.estimator.EstimatorSpec.predictions is a dict. If predict_keys is used then rest of the predictions will be filtered from the dictionary. If None, returns all.

  • hooks: List of tf.train.SessionRunHook subclass instances. Used for callbacks inside the prediction call.

  • checkpoint_path: Path of a specific checkpoint to predict. If None, the latest checkpoint in model_dir is used. If there are no checkpoints in model_dir, prediction is run with newly initialized Variables instead of ones restored from checkpoint.

  • yield_single_examples: If False, yields the whole batch as returned by the model_fn instead of decomposing the batch into individual elements. This is useful if model_fn returns some tensors whose first dimension is not equal to the batch size.

Yields:

Evaluated values of predictions tensors.

Raises:

  • ValueError: Could not find a trained model in model_dir.
  • ValueError: If batch length of predictions is not the same and yield_single_examples is True.
  • ValueError: If there is a conflict between predict_keys and predictions. For example if predict_keys is not None but tf.estimator.EstimatorSpec.predictions is not a dict.

tf.contrib.factorization.KMeansClustering.predict_cluster_index

predict_cluster_index(input_fn)

Finds the index of the closest cluster center to each input point.

Args:

Yields:

The index of the closest cluster center for each input point.

tf.contrib.factorization.KMeansClustering.score

score(input_fn)

Returns the sum of squared distances to nearest clusters.

Note that this function is different from the corresponding one in sklearn which returns the negative sum.

Args:

Returns:

The sum of the squared distance from each point in the first batch of inputs to its nearest cluster center.

tf.contrib.factorization.KMeansClustering.train

train(
    input_fn,
    hooks=None,
    steps=None,
    max_steps=None,
    saving_listeners=None
)

Trains a model given training data input_fn.

Args:

  • input_fn: A function that provides input data for training as minibatches. See Premade Estimators for more information. The function should construct and return one of the following: * A tf.data.Dataset object: Outputs of Dataset object must be a tuple (features, labels) with same constraints as below. * A tuple (features, labels): Where features is a tf.Tensor or a dictionary of string feature name to Tensor and labels is a Tensor or a dictionary of string label name to Tensor. Both features and labels are consumed by model_fn. They should satisfy the expectation of model_fn from inputs.
  • hooks: List of tf.train.SessionRunHook subclass instances. Used for callbacks inside the training loop.
  • steps: Number of steps for which to train the model. If None, train forever or train until input_fn generates the tf.errors.OutOfRange error or StopIteration exception. steps works incrementally. If you call two times train(steps=10) then training occurs in total 20 steps. If OutOfRange or StopIteration occurs in the middle, training stops before 20 steps. If you don't want to have incremental behavior please set max_steps instead. If set, max_steps must be None.
  • max_steps: Number of total steps for which to train model. If None, train forever or train until input_fn generates the tf.errors.OutOfRange error or StopIteration exception. If set, steps must be None. If OutOfRange or StopIteration occurs in the middle, training stops before max_steps steps. Two calls to train(steps=100) means 200 training iterations. On the other hand, two calls to train(max_steps=100) means that the second call will not do any iteration since first call did all 100 steps.
  • saving_listeners: list of CheckpointSaverListener objects. Used for callbacks that run immediately before or after checkpoint savings.

Returns:

self, for chaining.

Raises:

  • ValueError: If both steps and max_steps are not None.
  • ValueError: If either steps or max_steps <= 0.

tf.contrib.factorization.KMeansClustering.transform

transform(input_fn)

Transforms each input point to its distances to all cluster centers.

Note that if distance_metric=KMeansClustering.SQUARED_EUCLIDEAN_DISTANCE, this function returns the squared Euclidean distance while the corresponding sklearn function returns the Euclidean distance.

Args:

Yields:

The distances from each input point to each cluster center.

Class Members

ALL_DISTANCES

CLUSTER_CENTERS_VAR_NAME

CLUSTER_INDEX

COSINE_DISTANCE

KMEANS_PLUS_PLUS_INIT

RANDOM_INIT

SCORE

SQUARED_EUCLIDEAN_DISTANCE