tf.contrib.learn.LinearClassifier

Linear classifier model.

Inherits From: Estimator

tf.contrib.learn.LinearClassifier(
    feature_columns, model_dir=None, n_classes=2, weight_column_name=None,
    optimizer=None, gradient_clip_norm=None, enable_centered_bias=False,
    _joint_weight=False, config=None, feature_engineering_fn=None, label_keys=None
)

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

Train a linear model to classify instances into one of multiple possible classes. When number of possible classes is 2, this is binary classification.

Example:

sparse_column_a = sparse_column_with_hash_bucket(...)
sparse_column_b = sparse_column_with_hash_bucket(...)

sparse_feature_a_x_sparse_feature_b = crossed_column(...)

# Estimator using the default optimizer.
estimator = LinearClassifier(
    feature_columns=[sparse_column_a, sparse_feature_a_x_sparse_feature_b])

# Or estimator using the FTRL optimizer with regularization.
estimator = LinearClassifier(
    feature_columns=[sparse_column_a, sparse_feature_a_x_sparse_feature_b],
    optimizer=tf.compat.v1.train.FtrlOptimizer(
      learning_rate=0.1,
      l1_regularization_strength=0.001
    ))

# Or estimator using the SDCAOptimizer.
estimator = LinearClassifier(
   feature_columns=[sparse_column_a, sparse_feature_a_x_sparse_feature_b],
   optimizer=tf.contrib.linear_optimizer.SDCAOptimizer(
     example_id_column='example_id',
     num_loss_partitions=...,
     symmetric_l2_regularization=2.0
   ))

# Input builders
def input_fn_train: # returns x, y (where y represents label's class index).
  ...
def input_fn_eval: # returns x, y (where y represents label's class index).
  ...
def input_fn_predict: # returns x, None.
  ...
estimator.fit(input_fn=input_fn_train)
estimator.evaluate(input_fn=input_fn_eval)
# predict_classes returns class indices.
estimator.predict_classes(input_fn=input_fn_predict)

If the user specifies label_keys in constructor, labels must be strings from the label_keys vocabulary. Example:

label_keys = ['label0', 'label1', 'label2']
estimator = LinearClassifier(
    n_classes=n_classes,
    feature_columns=[sparse_column_a, sparse_feature_a_x_sparse_feature_b],
    label_keys=label_keys)

def input_fn_train: # returns x, y (where y is one of label_keys).
  pass
estimator.fit(input_fn=input_fn_train)

def input_fn_eval: # returns x, y (where y is one of label_keys).
  pass
estimator.evaluate(input_fn=input_fn_eval)
def input_fn_predict: # returns x, None
# predict_classes returns one of label_keys.
estimator.predict_classes(input_fn=input_fn_predict)

Input of fit and evaluate should have following features, otherwise there will be a KeyError:

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 WeightedSparseColumn, two features: the first with key the id column name, the second with key the weight column name. Both features' value must be a SparseTensor.
- if column is a RealValuedColumn, a feature with key=column.name whose value is a Tensor.

Args
`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`.
`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.
`n_classes`	number of label classes. Default is binary classification. Note that class labels are integers representing the class index (i.e. values from 0 to n_classes-1). For arbitrary label values (e.g. string labels), convert to class indices first.
`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.
`optimizer`	The optimizer used to train the model. If specified, it should be either an instance of `tf.Optimizer` or the SDCAOptimizer. If `None`, the Ftrl optimizer will be used.
`gradient_clip_norm`	A `float` > 0. If provided, gradients are clipped to their global norm with this clipping ratio. See `tf.clip_by_global_norm` for more details.
`enable_centered_bias`	A bool. If True, estimator will learn a centered bias variable for each class. Rest of the model structure learns the residual after centered bias.
`_joint_weight`	If True, the weights for all columns will be stored in a single (possibly partitioned) variable. It's more efficient, but it's incompatible with SDCAOptimizer, and requires all feature columns are sparse and use the 'sum' combiner.
`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.
`label_keys`	Optional list of strings with size `[n_classes]` defining the label vocabulary. Only supported for `n_classes` > 2.

Raises
`ValueError`	if n_classes < 2.
`ValueError`	if enable_centered_bias=True and optimizer is SDCAOptimizer.

Attributes
`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.

Attributes

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`

View source

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)

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`

View source

export(
    export_dir, input_fn=None, input_feature_key=None, use_deprecated_input_fn=True,
    signature_fn=None, default_batch_size=1, exports_to_keep=None
)

See BaseEstimator.export. (deprecated)

`export_savedmodel`

View source

export_savedmodel(
    export_dir_base, serving_input_fn, default_output_alternative_key=None,
    assets_extra=None, as_text=False, checkpoint_path=None,
    graph_rewrite_specs=(GraphRewriteSpec((tag_constants.SERVING,), ()),),
    strip_default_attrs=False
)

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.

`fit`

View source

fit(
    x=None, y=None, input_fn=None, steps=None, batch_size=None, monitors=None,
    max_steps=None
)

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`

View source

get_params(
    deep=True
)

Get parameters for this estimator.

Args

Args
`deep`	boolean, optional If `True`, will return the parameters for this estimator and contained subobjects that are estimators.

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`

View source

get_variable_names()

Returns list of all variable names in this model.

Returns
List of names.

`get_variable_value`

View source

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`

View source

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)

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`

View source

predict(
    x=None, input_fn=None, batch_size=None, outputs=None, as_iterable=True
)

Returns predictions for given features. (deprecated argument values) (deprecated argument values)

By default, returns predicted classes. But this default will be dropped soon. Users should either pass outputs, or call predict_classes method.

Args
`x`	features.
`input_fn`	Input function. If set, x must be None.
`batch_size`	Override default batch size.
`outputs`	list of `str`, name of the output to predict. If `None`, returns classes.
`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
Numpy array of predicted classes with shape batch_size. Each predicted class is represented by its class index (i.e. integer from 0 to n_classes-1). If `outputs` is set, returns a dict of predictions.

`predict_classes`

View source

predict_classes(
    x=None, input_fn=None, batch_size=None, as_iterable=True
)

Returns predicted classes for given features. (deprecated argument values)

tf.contrib.learn.LinearClassifier

Example:

Args

Raises

Attributes

Methods

evaluate

export

export_savedmodel

fit

get_params

get_variable_names

get_variable_value

partial_fit

predict

predict_classes

`evaluate`

`export`

`export_savedmodel`

`fit`

`get_params`

`get_variable_names`

`get_variable_value`

`partial_fit`

`predict`

`predict_classes`