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tf.contrib.learn.SVM

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Support Vector Machine (SVM) model for binary classification.

Inherits From: Estimator

THIS CLASS IS DEPRECATED. See contrib/learn/README.md for general migration instructions.

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 an Estimator (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.name`
  whose `value` is a `Tensor`.

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 from FeatureColumn.
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 of input_fn and returns features and labels which will be fed into the model.

ValueError if kernels passed is not None.

config

model_dir Returns a path in which the eval process will look for checkpoints.
model_fn Returns the model_fn which is bound to self.params.

Methods

evaluate

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See Evaluable. (deprecated arguments)

Raises
ValueError If at least one of x or y is provided, and at least one of input_fn or feed_fn is provided. Or if metrics is not None or dict.

export

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See BaseEstimator.export. (deprecated)

export_savedmodel

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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 an InputFnOps.
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.
graph_rewrite_specs an iterable of GraphRewriteSpec. Each element will produce a separate MetaGraphDef within the exported SavedModel, tagged and rewritten as specified. Defaults to a single entry using the default serving tag ("serve") and no rewriting.
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 an unrecognized export_type is requested.

export_with_defaults

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Same as BaseEstimator.export, but uses some defaults. (deprecated)

fit

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See Trainable. (deprecated arguments)

Raises
ValueError If x or y are not None while input_fn is not None.
ValueError If both steps and max_steps are not None.

get_params

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Get parameters for this estimator.

Args
deep boolean, optional

If 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

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Returns list of all variable names in this model.

Returns
List of names.

get_variable_value

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Returns value of the variable given by name.

Args
name string, name of the tensor.

Returns
Numpy array - value of the tensor.

partial_fit

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Incremental fit on a batch of samples. (deprecated arguments)

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 be None.
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 be None.
input_fn Input function. If set, x, y, and batch_size must be None.
steps Number of steps for which to train model. If None, train forever.
batch_size minibatch size to use on the input, defaults to first dimension of x. Must be None if input_fn is provided.
monitors List of BaseMonitor subclass instances. Used for callbacks inside the training loop.

Returns
self, for chaining.

Raises
ValueError If at least one of x and y is provided, and input_fn is provided.

predict

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Returns predictions for given features. (deprecated arguments)

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 be None.
input_fn Input function. If set, x and 'batch_size' must be None.
batch_size Override default batch size. If set, 'input_fn' must be 'None'.
outputs list of str, name of the output to predict. If None, 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).
iterate_batches If True, yield the whole batch at once instead of decomposing the batch into individual samples. Only relevant when as_iterable is True.

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 both None.

predict_classes

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Runs inference to determine the predicted class. (deprecated argument values)

predict_proba

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Runs inference to determine the class probability predictions. (deprecated argument values)

set_params

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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.