Train and evaluate TensorFlow models.
class tf.contrib.learn.BaseEstimator
Abstract BaseEstimator class to train and evaluate TensorFlow models.
Concrete implementation of this class should provide the following functions:
 _get_train_ops
 _get_eval_ops
 _get_predict_ops
Estimator
implemented below is a good example of how to use this class.
tf.contrib.learn.BaseEstimator.__init__(model_dir=None, config=None)
{:#BaseEstimator.init}
Initializes a BaseEstimator instance.
Args:
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.config
: A RunConfig instance.
tf.contrib.learn.BaseEstimator.__repr__()
{:#BaseEstimator.repr}
tf.contrib.learn.BaseEstimator.config
tf.contrib.learn.BaseEstimator.evaluate(x=None, y=None, input_fn=None, feed_fn=None, batch_size=None, steps=None, metrics=None, name=None)
See Evaluable
.
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
.
tf.contrib.learn.BaseEstimator.export(*args, **kwargs)
Exports inference graph into given dir. (deprecated arguments)
SOME ARGUMENTS ARE DEPRECATED. They will be removed after 20160923. Instructions for updating: The signature of the input_fn accepted by export is changing to be consistent with what's used by tf.Learn Estimator's train/evaluate. input_fn and input_feature_key will become required args, and use_deprecated_input_fn will default to False and be removed altogether.
Args:
export_dir: A string containing a directory to write the exported graph
and checkpoints.
input_fn: If `use_deprecated_input_fn` is true, then a function that given
`Tensor` of `Example` strings, parses it into features that are then
passed to the model. Otherwise, a function that takes no argument and
returns a tuple of (features, targets), where features is a dict of
string key to `Tensor` and targets is a `Tensor` that's currently not
used (and so can be `None`).
input_feature_key: Only used if `use_deprecated_input_fn` is false. String
key into the features dict returned by `input_fn` that corresponds toa
the raw `Example` strings `Tensor` that the exported model will take as
input.
use_deprecated_input_fn: Determines the signature format of `input_fn`.
signature_fn: Function that returns a default signature and a named
signature map, given `Tensor` of `Example` strings, `dict` of `Tensor`s
for features and `Tensor` or `dict` of `Tensor`s for predictions.
prediction_key: The key for a tensor in the `predictions` dict (output
from the `model_fn`) to use as the `predictions` input to the
`signature_fn`. Optional. If `None`, predictions will pass to
`signature_fn` without filtering.
default_batch_size: Default batch size of the `Example` placeholder.
exports_to_keep: Number of exports to keep.
Returns:
The string path to the exported directory. NB: this functionality was
added ca. 2016/09/25; clients that depend on the return value may need
to handle the case where this function returns None because subclasses
are not returning a value.
tf.contrib.learn.BaseEstimator.fit(x=None, y=None, input_fn=None, steps=None, batch_size=None, monitors=None, max_steps=None)
See Trainable
.
Raises:
ValueError
: Ifx
ory
are notNone
whileinput_fn
is notNone
.ValueError
: If bothsteps
andmax_steps
are notNone
.
tf.contrib.learn.BaseEstimator.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.
tf.contrib.learn.BaseEstimator.get_variable_names()
Returns list of all variable names in this model.
Returns:
List of names.
tf.contrib.learn.BaseEstimator.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.
tf.contrib.learn.BaseEstimator.model_dir
tf.contrib.learn.BaseEstimator.partial_fit(x=None, y=None, input_fn=None, steps=1, batch_size=None, monitors=None)
Incremental fit on a batch of samples.
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 outofcore/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 targets. The training target 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.
tf.contrib.learn.BaseEstimator.predict(*args, **kwargs)
Returns predictions for given features. (deprecated arguments)
SOME ARGUMENTS ARE DEPRECATED. They will be removed after 20160915. 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.
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).
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`.
tf.contrib.learn.BaseEstimator.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.
class tf.contrib.learn.Estimator
Estimator class is the basic TensorFlow model trainer/evaluator.
tf.contrib.learn.Estimator.__init__(model_fn=None, model_dir=None, config=None, params=None, feature_engineering_fn=None)
{:#Estimator.init}
Constructs an Estimator instance.
Args:

model_fn
: Model function, takes features and targets tensors or dicts of tensors and returns predictions and loss tensors. Supports next three signatures for the function:(features, targets) > (predictions, loss, train_op)
(features, targets, mode) > (predictions, loss, train_op)
(features, targets, mode, params) > (predictions, loss, train_op)
Where
features
are singleTensor
ordict
ofTensor
s (depending on data passed tofit
),targets
areTensor
ordict
ofTensor
s (for multihead models). If mode isModeKeys.INFER
,targets=None
will be passed. If themodel_fn
's signature does not acceptmode
, themodel_fn
must still be able to handletargets=None
.mode
represents if this training, evaluation or prediction. SeeModeKeys
.params
is adict
of hyperparameters. Will receive what is passed to Estimator inparams
parameter. This allows to configure Estimators from hyper parameter tunning.

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. config
: Configuration object.params
:dict
of hyper parameters that will be passed intomodel_fn
. Keys are names of parameters, values are basic python types.feature_engineering_fn
: Feature engineering function. Takes features and targets which are the output ofinput_fn
and returns features and targets which will be fed intomodel_fn
. Please checkmodel_fn
for a definition of features and targets.
Raises:
ValueError
: parameters ofmodel_fn
don't matchparams
.
tf.contrib.learn.Estimator.__repr__()
{:#Estimator.repr}
tf.contrib.learn.Estimator.config
tf.contrib.learn.Estimator.evaluate(x=None, y=None, input_fn=None, feed_fn=None, batch_size=None, steps=None, metrics=None, name=None)
See Evaluable
.
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
.
tf.contrib.learn.Estimator.export(*args, **kwargs)
Exports inference graph into given dir. (deprecated arguments)
SOME ARGUMENTS ARE DEPRECATED. They will be removed after 20160923. Instructions for updating: The signature of the input_fn accepted by export is changing to be consistent with what's used by tf.Learn Estimator's train/evaluate. input_fn and input_feature_key will become required args, and use_deprecated_input_fn will default to False and be removed altogether.
Args:
export_dir: A string containing a directory to write the exported graph
and checkpoints.
input_fn: If `use_deprecated_input_fn` is true, then a function that given
`Tensor` of `Example` strings, parses it into features that are then
passed to the model. Otherwise, a function that takes no argument and
returns a tuple of (features, targets), where features is a dict of
string key to `Tensor` and targets is a `Tensor` that's currently not
used (and so can be `None`).
input_feature_key: Only used if `use_deprecated_input_fn` is false. String
key into the features dict returned by `input_fn` that corresponds toa
the raw `Example` strings `Tensor` that the exported model will take as
input.
use_deprecated_input_fn: Determines the signature format of `input_fn`.
signature_fn: Function that returns a default signature and a named
signature map, given `Tensor` of `Example` strings, `dict` of `Tensor`s
for features and `Tensor` or `dict` of `Tensor`s for predictions.
prediction_key: The key for a tensor in the `predictions` dict (output
from the `model_fn`) to use as the `predictions` input to the
`signature_fn`. Optional. If `None`, predictions will pass to
`signature_fn` without filtering.
default_batch_size: Default batch size of the `Example` placeholder.
exports_to_keep: Number of exports to keep.
Returns:
The string path to the exported directory. NB: this functionality was
added ca. 2016/09/25; clients that depend on the return value may need
to handle the case where this function returns None because subclasses
are not returning a value.
tf.contrib.learn.Estimator.fit(x=None, y=None, input_fn=None, steps=None, batch_size=None, monitors=None, max_steps=None)
See Trainable
.
Raises:
ValueError
: Ifx
ory
are notNone
whileinput_fn
is notNone
.ValueError
: If bothsteps
andmax_steps
are notNone
.
tf.contrib.learn.Estimator.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.
tf.contrib.learn.Estimator.get_variable_names()
Returns list of all variable names in this model.
Returns:
List of names.
tf.contrib.learn.Estimator.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.
tf.contrib.learn.Estimator.model_dir
tf.contrib.learn.Estimator.partial_fit(x=None, y=None, input_fn=None, steps=1, batch_size=None, monitors=None)
Incremental fit on a batch of samples.
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 outofcore/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 targets. The training target 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.
tf.contrib.learn.Estimator.predict(*args, **kwargs)
Returns predictions for given features. (deprecated arguments)
SOME ARGUMENTS ARE DEPRECATED. They will be removed after 20160915. 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.
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).
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`.
tf.contrib.learn.Estimator.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.
class tf.contrib.learn.ModeKeys
Standard names for model modes.
The following standard keys are defined:
TRAIN
: training mode.EVAL
: evaluation mode.INFER
: inference mode.
class tf.contrib.learn.DNNClassifier
A classifier for TensorFlow DNN models.
Example:
education = sparse_column_with_hash_bucket(column_name="education",
hash_bucket_size=1000)
occupation = sparse_column_with_hash_bucket(column_name="occupation",
hash_bucket_size=1000)
education_emb = embedding_column(sparse_id_column=education, dimension=16,
combiner="sum")
occupation_emb = embedding_column(sparse_id_column=occupation, dimension=16,
combiner="sum")
estimator = DNNClassifier(
feature_columns=[education_emb, occupation_emb],
hidden_units=[1024, 512, 256])
# Or estimator using the ProximalAdagradOptimizer optimizer with
# regularization.
estimator = DNNClassifier(
feature_columns=[education_emb, occupation_emb],
hidden_units=[1024, 512, 256],
optimizer=tf.train.ProximalAdagradOptimizer(
learning_rate=0.1,
l1_regularization_strength=0.001
))
# Input builders
def input_fn_train: # returns x, Y
pass
estimator.fit(input_fn=input_fn_train)
def input_fn_eval: # returns x, Y
pass
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
:
 if
weight_column_name
is notNone
, a feature withkey=weight_column_name
whose value is aTensor
.  for each
column
infeature_columns
:  if
column
is aSparseColumn
, a feature withkey=column.name
whosevalue
is aSparseTensor
.  if
column
is aWeightedSparseColumn
, two features: the first withkey
the id column name, the second withkey
the weight column name. Both features'value
must be aSparseTensor
.  if
column
is aRealValuedColumn
, a feature withkey=column.name
whosevalue
is aTensor
.
tf.contrib.learn.DNNClassifier.__init__(hidden_units, feature_columns, model_dir=None, n_classes=2, weight_column_name=None, optimizer=None, activation_fn=relu, dropout=None, gradient_clip_norm=None, enable_centered_bias=None, config=None)
{:#DNNClassifier.init}
Initializes a DNNClassifier instance.
Args:
hidden_units
: List of hidden units per layer. All layers are fully connected. Ex.[64, 32]
means first layer has 64 nodes and second one has 32.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
.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 target classes. Default is binary classification. It must be greater than 1.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
: An instance oftf.Optimizer
used to train the model. IfNone
, will use an Adagrad optimizer.activation_fn
: Activation function applied to each layer. IfNone
, will usetf.nn.relu
.dropout
: When notNone
, the probability we will drop out a given coordinate.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.config
:RunConfig
object to configure the runtime settings.
Returns:
A DNNClassifier
estimator.
Raises:
ValueError
: Ifn_classes
< 2.
tf.contrib.learn.DNNClassifier.bias_
DEPRECATED FUNCTION
THIS FUNCTION IS DEPRECATED. It will be removed after 20161013. Instructions for updating: This method inspects the private state of the object, and should not be used
tf.contrib.learn.DNNClassifier.config
tf.contrib.learn.DNNClassifier.evaluate(x=None, y=None, input_fn=None, feed_fn=None, batch_size=None, steps=None, metrics=None, name=None)
See evaluable.Evaluable.
tf.contrib.learn.DNNClassifier.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.
tf.contrib.learn.DNNClassifier.fit(x=None, y=None, input_fn=None, steps=None, batch_size=None, monitors=None, max_steps=None)
See trainable.Trainable.
tf.contrib.learn.DNNClassifier.get_variable_names()
Returns list of all variable names in this model.
Returns:
List of names.
tf.contrib.learn.DNNClassifier.get_variable_value(name)
Returns value of the variable given by name.
Args:
name
: string, name of the tensor.
Returns:
Tensor
object.
tf.contrib.learn.DNNClassifier.model_dir
tf.contrib.learn.DNNClassifier.predict(*args, **kwargs)
Returns predicted classes for given features. (deprecated arguments)
SOME ARGUMENTS ARE DEPRECATED. They will be removed after 20160915. 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.
Args:
x: features.
input_fn: Input function. If set, x must be None.
batch_size: Override default batch size.
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 (or an iterable of predicted classes if
as_iterable is True).
tf.contrib.learn.DNNClassifier.predict_proba(*args, **kwargs)
Returns prediction probabilities for given features. (deprecated arguments)
SOME ARGUMENTS ARE DEPRECATED. They will be removed after 20160915. 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.
Args:
x: features.
input_fn: Input function. If set, x and y must be None.
batch_size: Override default batch size.
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 probabilities (or an iterable of predicted
probabilities if as_iterable is True).
tf.contrib.learn.DNNClassifier.weights_
DEPRECATED FUNCTION
THIS FUNCTION IS DEPRECATED. It will be removed after 20161013. Instructions for updating: This method inspects the private state of the object, and should not be used
class tf.contrib.learn.DNNRegressor
A regressor for TensorFlow DNN models.
Example:
education = sparse_column_with_hash_bucket(column_name="education",
hash_bucket_size=1000)
occupation = sparse_column_with_hash_bucket(column_name="occupation",
hash_bucket_size=1000)
education_emb = embedding_column(sparse_id_column=education, dimension=16,
combiner="sum")
occupation_emb = embedding_column(sparse_id_column=occupation, dimension=16,
combiner="sum")
estimator = DNNRegressor(
feature_columns=[education_emb, occupation_emb],
hidden_units=[1024, 512, 256])
# Or estimator using the ProximalAdagradOptimizer optimizer with
# regularization.
estimator = DNNRegressor(
feature_columns=[education_emb, occupation_emb],
hidden_units=[1024, 512, 256],
optimizer=tf.train.ProximalAdagradOptimizer(
learning_rate=0.1,
l1_regularization_strength=0.001
))
# Input builders
def input_fn_train: # returns x, Y
pass
estimator.fit(input_fn=input_fn_train)
def input_fn_eval: # returns x, Y
pass
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
:
 if
weight_column_name
is notNone
, a feature withkey=weight_column_name
whose value is aTensor
.  for each
column
infeature_columns
:  if
column
is aSparseColumn
, a feature withkey=column.name
whosevalue
is aSparseTensor
.  if
column
is aWeightedSparseColumn
, two features: the first withkey
the id column name, the second withkey
the weight column name. Both features'value
must be aSparseTensor
.  if
column
is aRealValuedColumn
, a feature withkey=column.name
whosevalue
is aTensor
.
tf.contrib.learn.DNNRegressor.__init__(hidden_units, feature_columns, model_dir=None, weight_column_name=None, optimizer=None, activation_fn=relu, dropout=None, gradient_clip_norm=None, enable_centered_bias=None, config=None)
{:#DNNRegressor.init}
Initializes a DNNRegressor
instance.
Args:
hidden_units
: List of hidden units per layer. All layers are fully connected. Ex.[64, 32]
means first layer has 64 nodes and second one has 32.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
.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.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
: An instance oftf.Optimizer
used to train the model. IfNone
, will use an Adagrad optimizer.activation_fn
: Activation function applied to each layer. IfNone
, will usetf.nn.relu
.dropout
: When notNone
, the probability we will drop out a given coordinate.gradient_clip_norm
: Afloat
> 0. If provided, gradients are clipped to their global norm with this clipping ratio. Seetf.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.config
:RunConfig
object to configure the runtime settings.
Returns:
A DNNRegressor
estimator.
tf.contrib.learn.DNNRegressor.__repr__()
{:#DNNRegressor.repr}
tf.contrib.learn.DNNRegressor.bias_
tf.contrib.learn.DNNRegressor.config
tf.contrib.learn.DNNRegressor.dnn_bias_
Returns bias of deep neural network part.
tf.contrib.learn.DNNRegressor.dnn_weights_
Returns weights of deep neural network part.
tf.contrib.learn.DNNRegressor.evaluate(x=None, y=None, input_fn=None, feed_fn=None, batch_size=None, steps=None, metrics=None, name=None)
See Evaluable
.
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
.
tf.contrib.learn.DNNRegressor.export(*args, **kwargs)
Exports inference graph into given dir. (deprecated arguments)
SOME ARGUMENTS ARE DEPRECATED. They will be removed after 20160923. Instructions for updating: The signature of the input_fn accepted by export is changing to be consistent with what's used by tf.Learn Estimator's train/evaluate. input_fn and input_feature_key will become required args, and use_deprecated_input_fn will default to False and be removed altogether.
Args:
export_dir: A string containing a directory to write the exported graph
and checkpoints.
input_fn: If `use_deprecated_input_fn` is true, then a function that given
`Tensor` of `Example` strings, parses it into features that are then
passed to the model. Otherwise, a function that takes no argument and
returns a tuple of (features, targets), where features is a dict of
string key to `Tensor` and targets is a `Tensor` that's currently not
used (and so can be `None`).
input_feature_key: Only used if `use_deprecated_input_fn` is false. String
key into the features dict returned by `input_fn` that corresponds toa
the raw `Example` strings `Tensor` that the exported model will take as
input.
use_deprecated_input_fn: Determines the signature format of `input_fn`.
signature_fn: Function that returns a default signature and a named
signature map, given `Tensor` of `Example` strings, `dict` of `Tensor`s
for features and `Tensor` or `dict` of `Tensor`s for predictions.
prediction_key: The key for a tensor in the `predictions` dict (output
from the `model_fn`) to use as the `predictions` input to the
`signature_fn`. Optional. If `None`, predictions will pass to
`signature_fn` without filtering.
default_batch_size: Default batch size of the `Example` placeholder.
exports_to_keep: Number of exports to keep.
Returns:
The string path to the exported directory. NB: this functionality was
added ca. 2016/09/25; clients that depend on the return value may need
to handle the case where this function returns None because subclasses
are not returning a value.
tf.contrib.learn.DNNRegressor.fit(x=None, y=None, input_fn=None, steps=None, batch_size=None, monitors=None, max_steps=None)
See Trainable
.
Raises:
ValueError
: Ifx
ory
are notNone
whileinput_fn
is notNone
.ValueError
: If bothsteps
andmax_steps
are notNone
.
tf.contrib.learn.DNNRegressor.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.
tf.contrib.learn.DNNRegressor.get_variable_names()
Returns list of all variable names in this model.
Returns:
List of names.
tf.contrib.learn.DNNRegressor.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.
tf.contrib.learn.DNNRegressor.linear_bias_
Returns bias of the linear part.
tf.contrib.learn.DNNRegressor.linear_weights_
Returns weights per feature of the linear part.
tf.contrib.learn.DNNRegressor.model_dir
tf.contrib.learn.DNNRegressor.partial_fit(x=None, y=None, input_fn=None, steps=1, batch_size=None, monitors=None)
Incremental fit on a batch of samples.
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 outofcore/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 targets. The training target 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.
tf.contrib.learn.DNNRegressor.predict(*args, **kwargs)
Returns predictions for given features. (deprecated arguments)
SOME ARGUMENTS ARE DEPRECATED. They will be removed after 20160915. 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.
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).
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`.
tf.contrib.learn.DNNRegressor.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.
tf.contrib.learn.DNNRegressor.weights_
class tf.contrib.learn.TensorFlowEstimator
Base class for all TensorFlow estimators.
tf.contrib.learn.TensorFlowEstimator.__init__(model_fn, n_classes, batch_size=32, steps=200, optimizer='Adagrad', learning_rate=0.1, clip_gradients=5.0, class_weight=None, continue_training=False, config=None, verbose=1)
{:#TensorFlowEstimator.init}
Initializes a TensorFlowEstimator instance.
Args:
model_fn
: Model function, that takes inputx
,y
tensors and outputs prediction and loss tensors.n_classes
: Number of classes in the target.batch_size
: Mini batch size.steps
: Number of steps to run over data.optimizer
: Optimizer name (or class), for example "SGD", "Adam", "Adagrad".
learning_rate
: If this is constant float value, no decay function is used. Instead, a customized decay function can be passed that accepts global_step as parameter and returns a Tensor. e.g. exponential decay function:python def exp_decay(global_step): return tf.train.exponential_decay( learning_rate=0.1, global_step, decay_steps=2, decay_rate=0.001)

clip_gradients
: Clip norm of the gradients to this value to stop gradient explosion. class_weight
: None or list of n_classes floats. Weight associated with classes for loss computation. If not given, all classes are supposed to have weight one.continue_training
: when continue_training is True, once initialized model will be continuely trained on every call of fit.config
: RunConfig object that controls the configurations of the session, e.g. num_cores, gpu_memory_fraction, etc.
verbose
: Controls the verbosity, possible values: 0: the algorithm and debug information is muted.
 1: trainer prints the progress.
 2: log device placement is printed.
tf.contrib.learn.TensorFlowEstimator.__repr__()
{:#TensorFlowEstimator.repr}
tf.contrib.learn.TensorFlowEstimator.config
tf.contrib.learn.TensorFlowEstimator.evaluate(x=None, y=None, input_fn=None, feed_fn=None, batch_size=None, steps=None, metrics=None, name=None)
Evaluates given model with provided evaluation data.
See superclass Estimator for more details.
Args:
x
: features.y
: targets.input_fn
: Input function.feed_fn
: Function creating a feed dict every time it is called.batch_size
: minibatch size to use on the input.steps
: Number of steps for which to evaluate model.metrics
: Dict of metric ops to run. If None, the default metrics are used.name
: Name of the evaluation.
Returns:
Returns dict
with evaluation results.
tf.contrib.learn.TensorFlowEstimator.export(*args, **kwargs)
Exports inference graph into given dir. (deprecated arguments)
SOME ARGUMENTS ARE DEPRECATED. They will be removed after 20160923. Instructions for updating: The signature of the input_fn accepted by export is changing to be consistent with what's used by tf.Learn Estimator's train/evaluate. input_fn and input_feature_key will become required args, and use_deprecated_input_fn will default to False and be removed altogether.
Args:
export_dir: A string containing a directory to write the exported graph
and checkpoints.
input_fn: If `use_deprecated_input_fn` is true, then a function that given
`Tensor` of `Example` strings, parses it into features that are then
passed to the model. Otherwise, a function that takes no argument and
returns a tuple of (features, targets), where features is a dict of
string key to `Tensor` and targets is a `Tensor` that's currently not
used (and so can be `None`).
input_feature_key: Only used if `use_deprecated_input_fn` is false. String
key into the features dict returned by `input_fn` that corresponds toa
the raw `Example` strings `Tensor` that the exported model will take as
input.
use_deprecated_input_fn: Determines the signature format of `input_fn`.
signature_fn: Function that returns a default signature and a named
signature map, given `Tensor` of `Example` strings, `dict` of `Tensor`s
for features and `Tensor` or `dict` of `Tensor`s for predictions.
prediction_key: The key for a tensor in the `predictions` dict (output
from the `model_fn`) to use as the `predictions` input to the
`signature_fn`. Optional. If `None`, predictions will pass to
`signature_fn` without filtering.
default_batch_size: Default batch size of the `Example` placeholder.
exports_to_keep: Number of exports to keep.
Returns:
The string path to the exported directory. NB: this functionality was
added ca. 2016/09/25; clients that depend on the return value may need
to handle the case where this function returns None because subclasses
are not returning a value.
tf.contrib.learn.TensorFlowEstimator.fit(x, y, steps=None, monitors=None, logdir=None)
Neural network model from provided model_fn
and training data.
Args:

x
: matrix or tensor of shape [n_samples, n_features...]. Can be iterator that returns arrays of features. The training input samples for fitting the model. 
y
: vector or matrix [n_samples] or [n_samples, n_outputs]. Can be iterator that returns array of targets. The training target values (class labels in classification, real numbers in regression). 
steps
: int, number of steps to train. If None or 0, train forself.steps
. monitors
: List ofBaseMonitor
objects to print training progress and invoke early stopping.logdir
: the directory to save the log file that can be used for optional visualization.
Returns:
Returns self.
tf.contrib.learn.TensorFlowEstimator.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.
tf.contrib.learn.TensorFlowEstimator.get_tensor(name)
Returns tensor by name.
Args:
name
: string, name of the tensor.
Returns:
Tensor.
tf.contrib.learn.TensorFlowEstimator.get_variable_names()
Returns list of all variable names in this model.
Returns:
List of names.
tf.contrib.learn.TensorFlowEstimator.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.
tf.contrib.learn.TensorFlowEstimator.model_dir
tf.contrib.learn.TensorFlowEstimator.partial_fit(x, y)
Incremental fit on a batch of samples.
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 outofcore/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 or tensor of shape [n_samples, n_features...]. Can be iterator that returns arrays of features. The training input samples for fitting the model. 
y
: vector or matrix [n_samples] or [n_samples, n_outputs]. Can be iterator that returns array of targets. The training target values (class label in classification, real numbers in regression).
Returns:
Returns self.
tf.contrib.learn.TensorFlowEstimator.predict(x, axis=1, batch_size=None)
Predict class or regression for x
.
For a classification model, the predicted class for each sample in x
is
returned. For a regression model, the predicted value based on x
is
returned.
Args:
x
: arraylike matrix, [n_samples, n_features...] or iterator.axis
: Which axis to argmax for classification. By default axis 1 (next after batch) is used. Use 2 for sequence predictions.batch_size
: If test set is too big, use batch size to split it into mini batches. By default the batch_size member variable is used.
Returns:
y
: array of shape [n_samples]. The predicted classes or predicted value.
tf.contrib.learn.TensorFlowEstimator.predict_proba(x, batch_size=None)
Predict class probability of the input samples x
.
Args:
x
: arraylike matrix, [n_samples, n_features...] or iterator.batch_size
: If test set is too big, use batch size to split it into mini batches. By default the batch_size member variable is used.
Returns:
y
: array of shape [n_samples, n_classes]. The predicted probabilities for each class.
tf.contrib.learn.TensorFlowEstimator.restore(cls, path, config=None)
Restores model from give path.
Args:
path
: Path to the checkpoints and other model information.config
: RunConfig object that controls the configurations of the session, e.g. num_cores, gpu_memory_fraction, etc. This is allowed to be reconfigured.
Returns:
Estimator, object of the subclass of TensorFlowEstimator.
Raises:
ValueError
: ifpath
does not contain a model definition.
tf.contrib.learn.TensorFlowEstimator.save(path)
Saves checkpoints and graph to given path.
Args:
path
: Folder to save model to.
tf.contrib.learn.TensorFlowEstimator.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.
class tf.contrib.learn.LinearClassifier
Linear classifier model.
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:
education = sparse_column_with_hash_bucket(column_name="education",
hash_bucket_size=1000)
occupation = sparse_column_with_hash_bucket(column_name="occupation",
hash_bucket_size=1000)
education_x_occupation = crossed_column(columns=[education, occupation],
hash_bucket_size=10000)
# Estimator using the default optimizer.
estimator = LinearClassifier(
feature_columns=[occupation, education_x_occupation])
# Or estimator using the FTRL optimizer with regularization.
estimator = LinearClassifier(
feature_columns=[occupation, education_x_occupation],
optimizer=tf.train.FtrlOptimizer(
learning_rate=0.1,
l1_regularization_strength=0.001
))
# Or estimator using the SDCAOptimizer.
estimator = LinearClassifier(
feature_columns=[occupation, education_x_occupation],
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
...
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
:
 if
weight_column_name
is notNone
, a feature withkey=weight_column_name
whose value is aTensor
.  for each
column
infeature_columns
:  if
column
is aSparseColumn
, a feature withkey=column.name
whosevalue
is aSparseTensor
.  if
column
is aWeightedSparseColumn
, two features: the first withkey
the id column name, the second withkey
the weight column name. Both features'value
must be aSparseTensor
.  if
column
is aRealValuedColumn
, a feature withkey=column.name
whosevalue
is aTensor
.
tf.contrib.learn.LinearClassifier.__init__(feature_columns, model_dir=None, n_classes=2, weight_column_name=None, optimizer=None, gradient_clip_norm=None, enable_centered_bias=None, _joint_weight=False, config=None)
{:#LinearClassifier.init}
Construct a LinearClassifier
estimator object.
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 fromFeatureColumn
.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 target classes. Default is binary classification.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 oftf.Optimizer
or the SDCAOptimizer. IfNone
, the Ftrl optimizer will be used.gradient_clip_norm
: Afloat
> 0. If provided, gradients are clipped to their global norm with this clipping ratio. Seetf.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.
Returns:
A LinearClassifier
estimator.
Raises:
ValueError
: if n_classes < 2.
tf.contrib.learn.LinearClassifier.bias_
tf.contrib.learn.LinearClassifier.config
tf.contrib.learn.LinearClassifier.evaluate(x=None, y=None, input_fn=None, feed_fn=None, batch_size=None, steps=None, metrics=None, name=None)
See evaluable.Evaluable.
tf.contrib.learn.LinearClassifier.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.
tf.contrib.learn.LinearClassifier.fit(x=None, y=None, input_fn=None, steps=None, batch_size=None, monitors=None, max_steps=None)
See trainable.Trainable.
tf.contrib.learn.LinearClassifier.get_estimator()
tf.contrib.learn.LinearClassifier.get_variable_names()
tf.contrib.learn.LinearClassifier.get_variable_value(name)
tf.contrib.learn.LinearClassifier.model_dir
tf.contrib.learn.LinearClassifier.predict(x=None, input_fn=None, batch_size=None, as_iterable=False)
Runs inference to determine the predicted class.
tf.contrib.learn.LinearClassifier.predict_proba(x=None, input_fn=None, batch_size=None, outputs=None, as_iterable=False)
Runs inference to determine the class probability predictions.
tf.contrib.learn.LinearClassifier.weights_
class tf.contrib.learn.LinearRegressor
Linear regressor model.
Train a linear regression model to predict target variable value given observation of feature values.
Example:
education = sparse_column_with_hash_bucket(column_name="education",
hash_bucket_size=1000)
occupation = sparse_column_with_hash_bucket(column_name="occupation",
hash_bucket_size=1000)
education_x_occupation = crossed_column(columns=[education, occupation],
hash_bucket_size=10000)
estimator = LinearRegressor(
feature_columns=[occupation, education_x_occupation])
# 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:
 if
weight_column_name
is notNone
: key=weight_column_name, value=aTensor
 for column in
feature_columns
:  if isinstance(column,
SparseColumn
): key=column.name, value=aSparseTensor
 if isinstance(column,
WeightedSparseColumn
): {key=id column name, value=aSparseTensor
, key=weight column name, value=aSparseTensor
}  if isinstance(column,
RealValuedColumn
): key=column.name, value=aTensor
tf.contrib.learn.LinearRegressor.__init__(feature_columns, model_dir=None, weight_column_name=None, optimizer=None, gradient_clip_norm=None, enable_centered_bias=None, target_dimension=1, _joint_weights=False, config=None)
{:#LinearRegressor.init}
Construct a LinearRegressor
estimator object.
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 fromFeatureColumn
.model_dir
: Directory to save model parameters, graph, etc. This can also be used to load checkpoints from the directory into a estimator to continue training a previously saved model.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
: An instance oftf.Optimizer
used to train the model. IfNone
, will use an Ftrl optimizer.gradient_clip_norm
: Afloat
> 0. If provided, gradients are clipped to their global norm with this clipping ratio. Seetf.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.
target_dimension
: dimension of the target for multilabels. _joint_weights: If True use a single (possibly partitioned) variable to store the weights. It's faster, but requires all feature columns are sparse and have the 'sum' combiner. Incompatible with SDCAOptimizer. 
config
:RunConfig
object to configure the runtime settings.
Returns:
A LinearRegressor
estimator.
tf.contrib.learn.LinearRegressor.__repr__()
{:#LinearRegressor.repr}
tf.contrib.learn.LinearRegressor.bias_
tf.contrib.learn.LinearRegressor.config
tf.contrib.learn.LinearRegressor.dnn_bias_
Returns bias of deep neural network part.
tf.contrib.learn.LinearRegressor.dnn_weights_
Returns weights of deep neural network part.
tf.contrib.learn.LinearRegressor.evaluate(x=None, y=None, input_fn=None, feed_fn=None, batch_size=None, steps=None, metrics=None, name=None)
See Evaluable
.
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
.
tf.contrib.learn.LinearRegressor.export(*args, **kwargs)
Exports inference graph into given dir. (deprecated arguments)
SOME ARGUMENTS ARE DEPRECATED. They will be removed after 20160923. Instructions for updating: The signature of the input_fn accepted by export is changing to be consistent with what's used by tf.Learn Estimator's train/evaluate. input_fn and input_feature_key will become required args, and use_deprecated_input_fn will default to False and be removed altogether.
Args:
export_dir: A string containing a directory to write the exported graph
and checkpoints.
input_fn: If `use_deprecated_input_fn` is true, then a function that given
`Tensor` of `Example` strings, parses it into features that are then
passed to the model. Otherwise, a function that takes no argument and
returns a tuple of (features, targets), where features is a dict of
string key to `Tensor` and targets is a `Tensor` that's currently not
used (and so can be `None`).
input_feature_key: Only used if `use_deprecated_input_fn` is false. String
key into the features dict returned by `input_fn` that corresponds toa
the raw `Example` strings `Tensor` that the exported model will take as
input.
use_deprecated_input_fn: Determines the signature format of `input_fn`.
signature_fn: Function that returns a default signature and a named
signature map, given `Tensor` of `Example` strings, `dict` of `Tensor`s
for features and `Tensor` or `dict` of `Tensor`s for predictions.
prediction_key: The key for a tensor in the `predictions` dict (output
from the `model_fn`) to use as the `predictions` input to the
`signature_fn`. Optional. If `None`, predictions will pass to
`signature_fn` without filtering.
default_batch_size: Default batch size of the `Example` placeholder.
exports_to_keep: Number of exports to keep.
Returns:
The string path to the exported directory. NB: this functionality was
added ca. 2016/09/25; clients that depend on the return value may need
to handle the case where this function returns None because subclasses
are not returning a value.
tf.contrib.learn.LinearRegressor.fit(x=None, y=None, input_fn=None, steps=None, batch_size=None, monitors=None, max_steps=None)
See Trainable
.
Raises:
ValueError
: Ifx
ory
are notNone
whileinput_fn
is notNone
.ValueError
: If bothsteps
andmax_steps
are notNone
.
tf.contrib.learn.LinearRegressor.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.
tf.contrib.learn.LinearRegressor.get_variable_names()
Returns list of all variable names in this model.
Returns:
List of names.
tf.contrib.learn.LinearRegressor.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.
tf.contrib.learn.LinearRegressor.linear_bias_
Returns bias of the linear part.
tf.contrib.learn.LinearRegressor.linear_weights_
Returns weights per feature of the linear part.
tf.contrib.learn.LinearRegressor.model_dir
tf.contrib.learn.LinearRegressor.partial_fit(x=None, y=None, input_fn=None, steps=1, batch_size=None, monitors=None)
Incremental fit on a batch of samples.
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 outofcore/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 targets. The training target 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.
tf.contrib.learn.LinearRegressor.predict(*args, **kwargs)
Returns predictions for given features. (deprecated arguments)
SOME ARGUMENTS ARE DEPRECATED. They will be removed after 20160915. 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.
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).
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`.
tf.contrib.learn.LinearRegressor.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.
tf.contrib.learn.LinearRegressor.weights_
class tf.contrib.learn.TensorFlowRNNClassifier
TensorFlow RNN Classifier model.
tf.contrib.learn.TensorFlowRNNClassifier.__init__(rnn_size, n_classes, cell_type='gru', num_layers=1, input_op_fn=null_input_op_fn, initial_state=None, bidirectional=False, sequence_length=None, attn_length=None, attn_size=None, attn_vec_size=None, batch_size=32, steps=50, optimizer='Adagrad', learning_rate=0.1, class_weight=None, clip_gradients=5.0, continue_training=False, config=None, verbose=1)
{:#TensorFlowRNNClassifier.init}
Initializes a TensorFlowRNNClassifier instance.
Args:
rnn_size
: The size for rnn cell, e.g. size of your word embeddings.cell_type
: The type of rnn cell, including rnn, gru, and lstm.num_layers
: The number of layers of the rnn model.input_op_fn
: Function that will transform the input tensor, such as creating word embeddings, byte list, etc. This takes an argument x for input and returns transformed x.bidirectional
: boolean, Whether this is a bidirectional rnn.sequence_length
: If sequence_length is provided, dynamic calculation is performed. This saves computational time when unrolling past max sequence length.initial_state
: An initial state for the RNN. This must be a tensor of appropriate type and shape [batch_size x cell.state_size].attn_length
: integer, the size of attention vector attached to rnn cells.attn_size
: integer, the size of an attention window attached to rnn cells.attn_vec_size
: integer, the number of convolutional features calculated on attention state and the size of the hidden layer built from base cell state.n_classes
: Number of classes in the target.batch_size
: Mini batch size.steps
: Number of steps to run over data.optimizer
: Optimizer name (or class), for example "SGD", "Adam", "Adagrad".
learning_rate
: If this is constant float value, no decay function is used. Instead, a customized decay function can be passed that accepts global_step as parameter and returns a Tensor. e.g. exponential decay function:python def exp_decay(global_step): return tf.train.exponential_decay( learning_rate=0.1, global_step, decay_steps=2, decay_rate=0.001)

class_weight
: None or list of n_classes floats. Weight associated with classes for loss computation. If not given, all classes are supposed to have weight one. continue_training
: when continue_training is True, once initialized model will be continuely trained on every call of fit.config
: RunConfig object that controls the configurations of the session, e.g. num_cores, gpu_memory_fraction, etc.
tf.contrib.learn.TensorFlowRNNClassifier.__repr__()
{:#TensorFlowRNNClassifier.repr}
tf.contrib.learn.TensorFlowRNNClassifier.bias_
Returns bias of the rnn layer.
tf.contrib.learn.TensorFlowRNNClassifier.config
tf.contrib.learn.TensorFlowRNNClassifier.evaluate(x=None, y=None, input_fn=None, feed_fn=None, batch_size=None, steps=None, metrics=None, name=None)
Evaluates given model with provided evaluation data.
See superclass Estimator for more details.
Args:
x
: features.y
: targets.input_fn
: Input function.feed_fn
: Function creating a feed dict every time it is called.batch_size
: minibatch size to use on the input.steps
: Number of steps for which to evaluate model.metrics
: Dict of metric ops to run. If None, the default metrics are used.name
: Name of the evaluation.
Returns:
Returns dict
with evaluation results.
tf.contrib.learn.TensorFlowRNNClassifier.export(*args, **kwargs)
Exports inference graph into given dir. (deprecated arguments)
SOME ARGUMENTS ARE DEPRECATED. They will be removed after 20160923. Instructions for updating: The signature of the input_fn accepted by export is changing to be consistent with what's used by tf.Learn Estimator's train/evaluate. input_fn and input_feature_key will become required args, and use_deprecated_input_fn will default to False and be removed altogether.
Args:
export_dir: A string containing a directory to write the exported graph
and checkpoints.
input_fn: If `use_deprecated_input_fn` is true, then a function that given
`Tensor` of `Example` strings, parses it into features that are then
passed to the model. Otherwise, a function that takes no argument and
returns a tuple of (features, targets), where features is a dict of
string key to `Tensor` and targets is a `Tensor` that's currently not
used (and so can be `None`).
input_feature_key: Only used if `use_deprecated_input_fn` is false. String
key into the features dict returned by `input_fn` that corresponds toa
the raw `Example` strings `Tensor` that the exported model will take as
input.
use_deprecated_input_fn: Determines the signature format of `input_fn`.
signature_fn: Function that returns a default signature and a named
signature map, given `Tensor` of `Example` strings, `dict` of `Tensor`s
for features and `Tensor` or `dict` of `Tensor`s for predictions.
prediction_key: The key for a tensor in the `predictions` dict (output
from the `model_fn`) to use as the `predictions` input to the
`signature_fn`. Optional. If `None`, predictions will pass to
`signature_fn` without filtering.
default_batch_size: Default batch size of the `Example` placeholder.
exports_to_keep: Number of exports to keep.
Returns:
The string path to the exported directory. NB: this functionality was
added ca. 2016/09/25; clients that depend on the return value may need
to handle the case where this function returns None because subclasses
are not returning a value.
tf.contrib.learn.TensorFlowRNNClassifier.fit(x, y, steps=None, monitors=None, logdir=None)
Neural network model from provided model_fn
and training data.
Args:

x
: matrix or tensor of shape [n_samples, n_features...]. Can be iterator that returns arrays of features. The training input samples for fitting the model. 
y
: vector or matrix [n_samples] or [n_samples, n_outputs]. Can be iterator that returns array of targets. The training target values (class labels in classification, real numbers in regression). 
steps
: int, number of steps to train. If None or 0, train forself.steps
. monitors
: List ofBaseMonitor
objects to print training progress and invoke early stopping.logdir
: the directory to save the log file that can be used for optional visualization.
Returns:
Returns self.
tf.contrib.learn.TensorFlowRNNClassifier.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.
tf.contrib.learn.TensorFlowRNNClassifier.get_tensor(name)
Returns tensor by name.
Args:
name
: string, name of the tensor.
Returns:
Tensor.
tf.contrib.learn.TensorFlowRNNClassifier.get_variable_names()
Returns list of all variable names in this model.
Returns:
List of names.
tf.contrib.learn.TensorFlowRNNClassifier.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.
tf.contrib.learn.TensorFlowRNNClassifier.model_dir
tf.contrib.learn.TensorFlowRNNClassifier.partial_fit(x, y)
Incremental fit on a batch of samples.
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 outofcore/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 or tensor of shape [n_samples, n_features...]. Can be iterator that returns arrays of features. The training input samples for fitting the model. 
y
: vector or matrix [n_samples] or [n_samples, n_outputs]. Can be iterator that returns array of targets. The training target values (class label in classification, real numbers in regression).
Returns:
Returns self.
tf.contrib.learn.TensorFlowRNNClassifier.predict(x, axis=1, batch_size=None)
Predict class or regression for x
.
For a classification model, the predicted class for each sample in x
is
returned. For a regression model, the predicted value based on x
is
returned.
Args:
x
: arraylike matrix, [n_samples, n_features...] or iterator.axis
: Which axis to argmax for classification. By default axis 1 (next after batch) is used. Use 2 for sequence predictions.batch_size
: If test set is too big, use batch size to split it into mini batches. By default the batch_size member variable is used.
Returns:
y
: array of shape [n_samples]. The predicted classes or predicted value.
tf.contrib.learn.TensorFlowRNNClassifier.predict_proba(x, batch_size=None)
Predict class probability of the input samples x
.
Args:
x
: arraylike matrix, [n_samples, n_features...] or iterator.batch_size
: If test set is too big, use batch size to split it into mini batches. By default the batch_size member variable is used.
Returns:
y
: array of shape [n_samples, n_classes]. The predicted probabilities for each class.
tf.contrib.learn.TensorFlowRNNClassifier.restore(cls, path, config=None)
Restores model from give path.
Args:
path
: Path to the checkpoints and other model information.config
: RunConfig object that controls the configurations of the session, e.g. num_cores, gpu_memory_fraction, etc. This is allowed to be reconfigured.
Returns:
Estimator, object of the subclass of TensorFlowEstimator.
Raises:
ValueError
: ifpath
does not contain a model definition.
tf.contrib.learn.TensorFlowRNNClassifier.save(path)
Saves checkpoints and graph to given path.
Args:
path
: Folder to save model to.
tf.contrib.learn.TensorFlowRNNClassifier.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.
tf.contrib.learn.TensorFlowRNNClassifier.weights_
Returns weights of the rnn layer.
class tf.contrib.learn.TensorFlowRNNRegressor
TensorFlow RNN Regressor model.
tf.contrib.learn.TensorFlowRNNRegressor.__init__(rnn_size, cell_type='gru', num_layers=1, input_op_fn=null_input_op_fn, initial_state=None, bidirectional=False, sequence_length=None, attn_length=None, attn_size=None, attn_vec_size=None, n_classes=0, batch_size=32, steps=50, optimizer='Adagrad', learning_rate=0.1, clip_gradients=5.0, continue_training=False, config=None, verbose=1)
{:#TensorFlowRNNRegressor.init}
Initializes a TensorFlowRNNRegressor instance.
Args:
rnn_size
: The size for rnn cell, e.g. size of your word embeddings.cell_type
: The type of rnn cell, including rnn, gru, and lstm.num_layers
: The number of layers of the rnn model.input_op_fn
: Function that will transform the input tensor, such as creating word embeddings, byte list, etc. This takes an argument x for input and returns transformed x.bidirectional
: boolean, Whether this is a bidirectional rnn.sequence_length
: If sequence_length is provided, dynamic calculation is performed. This saves computational time when unrolling past max sequence length.attn_length
: integer, the size of attention vector attached to rnn cells.attn_size
: integer, the size of an attention window attached to rnn cells.attn_vec_size
: integer, the number of convolutional features calculated on attention state and the size of the hidden layer built from base cell state.initial_state
: An initial state for the RNN. This must be a tensor of appropriate type and shape [batch_size x cell.state_size].batch_size
: Mini batch size.steps
: Number of steps to run over data.optimizer
: Optimizer name (or class), for example "SGD", "Adam", "Adagrad".
learning_rate
: If this is constant float value, no decay function is used. Instead, a customized decay function can be passed that accepts global_step as parameter and returns a Tensor. e.g. exponential decay function:python def exp_decay(global_step): return tf.train.exponential_decay( learning_rate=0.1, global_step, decay_steps=2, decay_rate=0.001)

continue_training
: when continue_training is True, once initialized model will be continuely trained on every call of fit. config
: RunConfig object that controls the configurations of the session, e.g. num_cores, gpu_memory_fraction, etc.
verbose
: Controls the verbosity, possible values: 0: the algorithm and debug information is muted.
 1: trainer prints the progress.
 2: log device placement is printed.
tf.contrib.learn.TensorFlowRNNRegressor.__repr__()
{:#TensorFlowRNNRegressor.repr}
tf.contrib.learn.TensorFlowRNNRegressor.bias_
Returns bias of the rnn layer.
tf.contrib.learn.TensorFlowRNNRegressor.config
tf.contrib.learn.TensorFlowRNNRegressor.evaluate(x=None, y=None, input_fn=None, feed_fn=None, batch_size=None, steps=None, metrics=None, name=None)
Evaluates given model with provided evaluation data.
See superclass Estimator for more details.
Args:
x
: features.y
: targets.input_fn
: Input function.feed_fn
: Function creating a feed dict every time it is called.batch_size
: minibatch size to use on the input.steps
: Number of steps for which to evaluate model.metrics
: Dict of metric ops to run. If None, the default metrics are used.name
: Name of the evaluation.
Returns:
Returns dict
with evaluation results.
tf.contrib.learn.TensorFlowRNNRegressor.export(*args, **kwargs)
Exports inference graph into given dir. (deprecated arguments)
SOME ARGUMENTS ARE DEPRECATED. They will be removed after 20160923. Instructions for updating: The signature of the input_fn accepted by export is changing to be consistent with what's used by tf.Learn Estimator's train/evaluate. input_fn and input_feature_key will become required args, and use_deprecated_input_fn will default to False and be removed altogether.
Args:
export_dir: A string containing a directory to write the exported graph
and checkpoints.
input_fn: If `use_deprecated_input_fn` is true, then a function that given
`Tensor` of `Example` strings, parses it into features that are then
passed to the model. Otherwise, a function that takes no argument and
returns a tuple of (features, targets), where features is a dict of
string key to `Tensor` and targets is a `Tensor` that's currently not
used (and so can be `None`).
input_feature_key: Only used if `use_deprecated_input_fn` is false. String
key into the features dict returned by `input_fn` that corresponds toa
the raw `Example` strings `Tensor` that the exported model will take as
input.
use_deprecated_input_fn: Determines the signature format of `input_fn`.
signature_fn: Function that returns a default signature and a named
signature map, given `Tensor` of `Example` strings, `dict` of `Tensor`s
for features and `Tensor` or `dict` of `Tensor`s for predictions.
prediction_key: The key for a tensor in the `predictions` dict (output
from the `model_fn`) to use as the `predictions` input to the
`signature_fn`. Optional. If `None`, predictions will pass to
`signature_fn` without filtering.
default_batch_size: Default batch size of the `Example` placeholder.
exports_to_keep: Number of exports to keep.
Returns:
The string path to the exported directory. NB: this functionality was
added ca. 2016/09/25; clients that depend on the return value may need
to handle the case where this function returns None because subclasses
are not returning a value.
tf.contrib.learn.TensorFlowRNNRegressor.fit(x, y, steps=None, monitors=None, logdir=None)
Neural network model from provided model_fn
and training data.
Args:

x
: matrix or tensor of shape [n_samples, n_features...]. Can be iterator that returns arrays of features. The training input samples for fitting the model. 
y
: vector or matrix [n_samples] or [n_samples, n_outputs]. Can be iterator that returns array of targets. The training target values (class labels in classification, real numbers in regression). 
steps
: int, number of steps to train. If None or 0, train forself.steps
. monitors
: List ofBaseMonitor
objects to print training progress and invoke early stopping.logdir
: the directory to save the log file that can be used for optional visualization.
Returns:
Returns self.
tf.contrib.learn.TensorFlowRNNRegressor.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.
tf.contrib.learn.TensorFlowRNNRegressor.get_tensor(name)
Returns tensor by name.
Args:
name
: string, name of the tensor.
Returns:
Tensor.
tf.contrib.learn.TensorFlowRNNRegressor.get_variable_names()
Returns list of all variable names in this model.
Returns:
List of names.
tf.contrib.learn.TensorFlowRNNRegressor.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.
tf.contrib.learn.TensorFlowRNNRegressor.model_dir
tf.contrib.learn.TensorFlowRNNRegressor.partial_fit(x, y)
Incremental fit on a batch of samples.
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 outofcore/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 or tensor of shape [n_samples, n_features...]. Can be iterator that returns arrays of features. The training input samples for fitting the model. 
y
: vector or matrix [n_samples] or [n_samples, n_outputs]. Can be iterator that returns array of targets. The training target values (class label in classification, real numbers in regression).
Returns:
Returns self.
tf.contrib.learn.TensorFlowRNNRegressor.predict(x, axis=1, batch_size=None)
Predict class or regression for x
.
For a classification model, the predicted class for each sample in x
is
returned. For a regression model, the predicted value based on x
is
returned.
Args:
x
: arraylike matrix, [n_samples, n_features...] or iterator.axis
: Which axis to argmax for classification. By default axis 1 (next after batch) is used. Use 2 for sequence predictions.batch_size
: If test set is too big, use batch size to split it into mini batches. By default the batch_size member variable is used.
Returns:
y
: array of shape [n_samples]. The predicted classes or predicted value.
tf.contrib.learn.TensorFlowRNNRegressor.predict_proba(x, batch_size=None)
Predict class probability of the input samples x
.
Args:
x
: arraylike matrix, [n_samples, n_features...] or iterator.batch_size
: If test set is too big, use batch size to split it into mini batches. By default the batch_size member variable is used.
Returns:
y
: array of shape [n_samples, n_classes]. The predicted probabilities for each class.
tf.contrib.learn.TensorFlowRNNRegressor.restore(cls, path, config=None)
Restores model from give path.
Args:
path
: Path to the checkpoints and other model information.config
: RunConfig object that controls the configurations of the session, e.g. num_cores, gpu_memory_fraction, etc. This is allowed to be reconfigured.
Returns:
Estimator, object of the subclass of TensorFlowEstimator.
Raises:
ValueError
: ifpath
does not contain a model definition.
tf.contrib.learn.TensorFlowRNNRegressor.save(path)
Saves checkpoints and graph to given path.
Args:
path
: Folder to save model to.
tf.contrib.learn.TensorFlowRNNRegressor.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.
tf.contrib.learn.TensorFlowRNNRegressor.weights_
Returns weights of the rnn layer.