contrib.learn.SVM
tf.contrib.learn.SVM
class tf.contrib.learn.SVM
Defined in tensorflow/contrib/learn/python/learn/estimators/svm.py
.
Support Vector Machine (SVM) model for binary classification.
Currently, only linear SVMs are supported. For the underlying optimization problem, the SDCAOptimizer
is used. For performance and convergence tuning, the num_loss_partitions parameter passed to SDCAOptimizer
(see __init__()
method), should be set to (#concurrent train ops per worker) x (#workers). If num_loss_partitions is larger or equal to this value, convergence is guaranteed but becomes slower as num_loss_partitions increases. If it is set to a smaller value, the optimizer is more aggressive in reducing the global loss but convergence is not guaranteed. The recommended value in tf.learn (where there is one process per worker) is the number of workers running the train steps. It defaults to 1 (single machine).
Example:
real_feature_column = real_valued_column(...) sparse_feature_column = sparse_column_with_hash_bucket(...) estimator = SVM( example_id_column='example_id', feature_columns=[real_feature_column, sparse_feature_column], l2_regularization=10.0) # Input builders def input_fn_train: # returns x, y ... def input_fn_eval: # returns x, y ... estimator.fit(input_fn=input_fn_train) estimator.evaluate(input_fn=input_fn_eval) estimator.predict(x=x)
Input of fit
and evaluate
should have following features, otherwise there will be a KeyError
: a feature with key=example_id_column
whose value is a Tensor
of dtype string. if weight_column_name
is not None
, a feature with key=weight_column_name
whose value is a Tensor
. for each column
in feature_columns
: - if column
is a SparseColumn
, a feature with key=column.name
whose value
is a SparseTensor
. - if column
is a RealValuedColumn, a feature with
key=column.namewhose
valueis a
Tensor`.
Properties
config
model_dir
Methods
__init__
__init__( example_id_column, feature_columns, weight_column_name=None, model_dir=None, l1_regularization=0.0, l2_regularization=0.0, num_loss_partitions=1, kernels=None, config=None, feature_engineering_fn=None )
Constructs an SVM
estimator object.
Args:
-
example_id_column
: A string defining the feature column name representing example ids. Used to initialize the underlying optimizer. -
feature_columns
: An iterable containing all the feature columns used by the model. All items in the set should be instances of classes derived fromFeatureColumn
. -
weight_column_name
: A string defining feature column name representing weights. It is used to down weight or boost examples during training. It will be multiplied by the loss of the example. -
model_dir
: Directory to save model parameters, graph and etc. This can also be used to load checkpoints from the directory into a estimator to continue training a previously saved model. -
l1_regularization
: L1-regularization parameter. Refers to global L1 regularization (across all examples). -
l2_regularization
: L2-regularization parameter. Refers to global L2 regularization (across all examples). -
num_loss_partitions
: number of partitions of the (global) loss function optimized by the underlying optimizer (SDCAOptimizer). -
kernels
: A list of kernels for the SVM. Currently, no kernels are supported. Reserved for future use for non-linear SVMs. -
config
: RunConfig object to configure the runtime settings. -
feature_engineering_fn
: Feature engineering function. Takes features and labels which are the output ofinput_fn
and returns features and labels which will be fed into the model.
Raises:
-
ValueError
: if kernels passed is not None.
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)
SOME ARGUMENTS ARE DEPRECATED. They will be removed after 2016-12-01. Instructions for updating: Estimator is decoupled from Scikit Learn interface by moving into separate class SKCompat. Arguments x, y and batch_size are only available in the SKCompat class, Estimator will only accept input_fn. Example conversion: est = Estimator(...) -> est = SKCompat(Estimator(...))
Raises:
-
ValueError
: If at least one ofx
ory
is provided, and at least one ofinput_fn
orfeed_fn
is provided. Or ifmetrics
is notNone
ordict
.
export
export( export_dir, signature_fn=None, input_fn=None, default_batch_size=1, exports_to_keep=None )
See BaseEstimator.export. (deprecated)
THIS FUNCTION IS DEPRECATED. It will be removed after 2017-03-25. Instructions for updating: Please use Estimator.export_savedmodel() instead.
export_savedmodel
export_savedmodel( export_dir_base, serving_input_fn, default_output_alternative_key=None, assets_extra=None, as_text=False, checkpoint_path=None )
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 anInputFnOps
. -
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.
Returns:
The string path to the exported directory.
Raises:
-
ValueError
: if an unrecognized export_type is requested.
export_with_defaults
export_with_defaults( export_dir, signature_fn=None, input_fn=None, default_batch_size=1, exports_to_keep=None )
Same as BaseEstimator.export, but uses some defaults. (deprecated)
THIS FUNCTION IS DEPRECATED. It will be removed after 2017-03-25. Instructions for updating: Please use Estimator.export_savedmodel() instead.
fit
fit( x=None, y=None, input_fn=None, steps=None, batch_size=None, monitors=None, max_steps=None )
See Trainable
. (deprecated arguments)
SOME ARGUMENTS ARE DEPRECATED. They will be removed after 2016-12-01. Instructions for updating: Estimator is decoupled from Scikit Learn interface by moving into separate class SKCompat. Arguments x, y and batch_size are only available in the SKCompat class, Estimator will only accept input_fn. Example conversion: est = Estimator(...) -> est = SKCompat(Estimator(...))
Raises:
-
ValueError
: Ifx
ory
are notNone
whileinput_fn
is notNone
. -
ValueError
: If bothsteps
andmax_steps
are notNone
.
get_params
get_params(deep=True)
Get parameters for this estimator.
Args:
-
deep
: boolean, optionalIf
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.
get_variable_names
get_variable_names()
Returns list of all variable names in this model.
Returns:
List of names.
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.
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)
SOME ARGUMENTS ARE DEPRECATED. They will be removed after 2016-12-01. Instructions for updating: Estimator is decoupled from Scikit Learn interface by moving into separate class SKCompat. Arguments x, y and batch_size are only available in the SKCompat class, Estimator will only accept input_fn. Example conversion: est = Estimator(...) -> est = SKCompat(Estimator(...))
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 beNone
. -
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 beNone
. -
input_fn
: Input function. If set,x
,y
, andbatch_size
must beNone
. -
steps
: Number of steps for which to train model. IfNone
, train forever. -
batch_size
: minibatch size to use on the input, defaults to first dimension ofx
. Must beNone
ifinput_fn
is provided. -
monitors
: List ofBaseMonitor
subclass instances. Used for callbacks inside the training loop.
Returns:
self
, for chaining.
Raises:
-
ValueError
: If at least one ofx
andy
is provided, andinput_fn
is provided.
predict
predict( x=None, input_fn=None, batch_size=None, outputs=None, as_iterable=True )
Returns predictions for given features. (deprecated arguments)
SOME ARGUMENTS ARE DEPRECATED. They will be removed after 2016-12-01. Instructions for updating: Estimator is decoupled from Scikit Learn interface by moving into separate class SKCompat. Arguments x, y and batch_size are only available in the SKCompat class, Estimator will only accept input_fn. Example conversion: est = Estimator(...) -> est = SKCompat(Estimator(...))
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 beNone
. -
input_fn
: Input function. If set,x
and 'batch_size' must beNone
. -
batch_size
: Override default batch size. If set, 'input_fn' must be 'None'. -
outputs
: list ofstr
, name of the output to predict. IfNone
, 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).
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 bothNone
.
predict_classes
predict_classes( x=None, input_fn=None, batch_size=None, as_iterable=True )
Runs inference to determine the predicted class. (deprecated arguments)
SOME ARGUMENTS ARE DEPRECATED. 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.
predict_proba
predict_proba( x=None, input_fn=None, batch_size=None, outputs=None, as_iterable=True )
Runs inference to determine the class probability predictions. (deprecated arguments)
SOME ARGUMENTS ARE DEPRECATED. 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.
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.
© 2017 The TensorFlow Authors. All rights reserved.
Licensed under the Creative Commons Attribution License 3.0.
Code samples licensed under the Apache 2.0 License.
https://www.tensorflow.org/api_docs/python/tf/contrib/learn/SVM