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An Estimator for K-Means clustering.
Inherits From: Estimator
tf.compat.v1.estimator.experimental.KMeans(
num_clusters, model_dir=None, initial_clusters=RANDOM_INIT,
distance_metric=SQUARED_EUCLIDEAN_DISTANCE, seed=None, use_mini_batch=True,
mini_batch_steps_per_iteration=1, kmeans_plus_plus_num_retries=2,
relative_tolerance=None, config=None, feature_columns=None
)
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.compat.v1.train.limit_epochs(
tf.convert_to_tensor(points, dtype=tf.float32), num_epochs=1)
num_clusters = 5
kmeans = tf.compat.v1.estimator.experimental.KMeans(
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_saved_model
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)
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.
|
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 \(1 - (u . v) / (||u||_2 ||v||_2)\).
|
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 .
|
Attributes | |
---|---|
config
|
|
model_dir
|
|
model_fn
|
Returns the model_fn which is bound to self.params .
|
params
|
Methods
cluster_centers
cluster_centers()
Returns the cluster centers.
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. |
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, orinput_fn
raises an end-of-input exception (tf.errors.OutOfRangeError
orStopIteration
).
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 .
|
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 Tensor
s. 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.ExportOutput
s, 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 SavedModel s.
|
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 path to the exported directory as a bytes object. |
Raises | |
---|---|
ValueError
|
if any input_receiver_fn is None , no export_outputs
are provided, or no checkpoint can be found.
|
export_saved_model
export_saved_model(
export_dir_base, serving_input_receiver_fn, assets_extra=None, as_text=False,
checkpoint_path=None, experimental_mode=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 Tensor
s, 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