contrib.learn.DNNRegressor
tf.contrib.learn.DNNRegressor
class tf.contrib.learn.DNNRegressor
Defined in tensorflow/contrib/learn/python/learn/estimators/dnn.py
.
See the guide: Learn (contrib) > Estimators
A regressor for TensorFlow DNN models.
Example:
sparse_feature_a = sparse_column_with_hash_bucket(...) sparse_feature_b = sparse_column_with_hash_bucket(...) sparse_feature_a_emb = embedding_column(sparse_id_column=sparse_feature_a, ...) sparse_feature_b_emb = embedding_column(sparse_id_column=sparse_feature_b, ...) estimator = DNNRegressor( feature_columns=[sparse_feature_a, sparse_feature_b], hidden_units=[1024, 512, 256]) # Or estimator using the ProximalAdagradOptimizer optimizer with # regularization. estimator = DNNRegressor( feature_columns=[sparse_feature_a, sparse_feature_b], 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) def input_fn_predict: # returns x, None pass estimator.predict_scores(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 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
.
Properties
config
model_dir
Methods
__init__
__init__( hidden_units, feature_columns, model_dir=None, weight_column_name=None, optimizer=None, activation_fn=tf.nn.relu, dropout=None, gradient_clip_norm=None, enable_centered_bias=False, config=None, feature_engineering_fn=None, label_dimension=1, embedding_lr_multipliers=None, input_layer_min_slice_size=None )
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. -
feature_engineering_fn
: Feature engineering function. Takes features and labels which are the output ofinput_fn
and returns features and labels which will be fed into the model. -
label_dimension
: Number of regression targets per example. This is the size of the last dimension of the labels and logitsTensor
objects (typically, these have shape[batch_size, label_dimension]
). -
embedding_lr_multipliers
: Optional. A dictionary fromEbeddingColumn
to afloat
multiplier. Multiplier will be used to multiply with learning rate for the embedding variables. -
input_layer_min_slice_size
: Optional. The min slice size of input layer partitions. If not provided, will use the default of 64M.
Returns:
A DNNRegressor
estimator.
evaluate
evaluate( x=None, y=None, input_fn=None, feed_fn=None, batch_size=None, steps=None, metrics=None, name=None, checkpoint_path=None, hooks=None )
See evaluable.Evaluable.
export
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)
THIS FUNCTION IS DEPRECATED. It will be removed after 2017-03-25. Instructions for updating: Please use Estimator.export_savedmodel() instead.
export_savedmodel
export_savedmodel( export_dir_base, serving_input_fn, default_output_alternative_key=None, assets_extra=None, as_text=False, checkpoint_path=None )
Exports inference graph as a SavedModel into given dir.
Args:
-
export_dir_base
: A string containing a directory to write the exported graph and checkpoints. -
serving_input_fn
: A function that takes no argument and returns anInputFnOps
. -
default_output_alternative_key
: the name of the head to serve when none is specified. Not needed for single-headed models. -
assets_extra
: A dict specifying how to populate the assets.extra directory within the exported SavedModel. Each key should give the destination path (including the filename) relative to the assets.extra directory. The corresponding value gives the full path of the source file to be copied. For example, the simple case of copying a single file without renaming it is specified as{'my_asset_file.txt': '/path/to/my_asset_file.txt'}
. -
as_text
: whether to write the SavedModel proto in text format. -
checkpoint_path
: The checkpoint path to export. If None (the default), the most recent checkpoint found within the model directory is chosen.
Returns:
The string path to the exported directory.
Raises:
-
ValueError
: if an unrecognized export_type is requested.
fit
fit( x=None, y=None, input_fn=None, steps=None, batch_size=None, monitors=None, max_steps=None )
See Trainable
. (deprecated arguments)
SOME ARGUMENTS ARE DEPRECATED. They will be removed after 2016-12-01. Instructions for updating: Estimator is decoupled from Scikit Learn interface by moving into separate class SKCompat. Arguments x, y and batch_size are only available in the SKCompat class, Estimator will only accept input_fn. Example conversion: est = Estimator(...) -> est = SKCompat(Estimator(...))
Raises:
-
ValueError
: Ifx
ory
are notNone
whileinput_fn
is notNone
. -
ValueError
: If bothsteps
andmax_steps
are notNone
.
get_params
get_params(deep=True)
Get parameters for this estimator.
Args:
-
deep
: boolean, optionalIf
True
, will return the parameters for this estimator and contained subobjects that are estimators.
Returns:
params : mapping of string to any Parameter names mapped to their values.
get_variable_names
get_variable_names()
Returns list of all variable names in this model.
Returns:
List of names.
get_variable_value
get_variable_value(name)
Returns value of the variable given by name.
Args:
-
name
: string, name of the tensor.
Returns:
Numpy array - value of the tensor.
partial_fit
partial_fit( x=None, y=None, input_fn=None, steps=1, batch_size=None, monitors=None )
Incremental fit on a batch of samples. (deprecated arguments)
SOME ARGUMENTS ARE DEPRECATED. They will be removed after 2016-12-01. Instructions for updating: Estimator is decoupled from Scikit Learn interface by moving into separate class SKCompat. Arguments x, y and batch_size are only available in the SKCompat class, Estimator will only accept input_fn. Example conversion: est = Estimator(...) -> est = SKCompat(Estimator(...))
This method is expected to be called several times consecutively on different or the same chunks of the dataset. This either can implement iterative training or out-of-core/online training.
This is especially useful when the whole dataset is too big to fit in memory at the same time. Or when model is taking long time to converge, and you want to split up training into subparts.
Args:
-
x
: Matrix of shape [n_samples, n_features...]. Can be iterator that returns arrays of features. The training input samples for fitting the model. If set,input_fn
must beNone
. -
y
: Vector or matrix [n_samples] or [n_samples, n_outputs]. Can be iterator that returns array of labels. The training label values (class labels in classification, real numbers in regression). If set,input_fn
must beNone
. -
input_fn
: Input function. If set,x
,y
, andbatch_size
must beNone
. -
steps
: Number of steps for which to train model. IfNone
, train forever. -
batch_size
: minibatch size to use on the input, defaults to first dimension ofx
. Must beNone
ifinput_fn
is provided. -
monitors
: List ofBaseMonitor
subclass instances. Used for callbacks inside the training loop.
Returns:
self
, for chaining.
Raises:
-
ValueError
: If at least one ofx
andy
is provided, andinput_fn
is provided.
predict
predict( x=None, input_fn=None, batch_size=None, outputs=None, as_iterable=True )
Returns predictions for given features. (deprecated arguments) (deprecated arguments)
SOME ARGUMENTS ARE DEPRECATED. They will be removed after 2016-09-15. Instructions for updating: The default behavior of predict() is changing. The default value for as_iterable will change to True, and then the flag will be removed altogether. The behavior of this flag is described below.
SOME ARGUMENTS ARE DEPRECATED. They will be removed after 2017-03-01. Instructions for updating: Please switch to predict_scores, or set outputs
argument.
By default, returns predicted scores. But this default will be dropped soon. Users should either pass outputs
, or call predict_scores
method.
Args:
-
x
: features. -
input_fn
: Input function. If set, x must be None. -
batch_size
: Override default batch size. -
outputs
: list ofstr
, name of the output to predict. IfNone
, returns scores. -
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 scores (or an iterable of predicted scores if as_iterable is True). If label_dimension == 1
, the shape of the output is [batch_size]
, otherwise the shape is [batch_size, label_dimension]
. If outputs
is set, returns a dict of predictions.
predict_scores
predict_scores( x=None, input_fn=None, batch_size=None, as_iterable=True )
Returns predicted scores for given features. (deprecated arguments)
SOME ARGUMENTS ARE DEPRECATED. They will be removed after 2016-09-15. Instructions for updating: The default behavior of predict() is changing. The default value for as_iterable will change to True, and then the flag will be removed altogether. The behavior of this flag is described below.
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 scores (or an iterable of predicted scores if as_iterable is True). If label_dimension == 1
, the shape of the output is [batch_size]
, otherwise the shape is [batch_size, label_dimension]
.
set_params
set_params(**params)
Set the parameters of this estimator.
The method works on simple estimators as well as on nested objects (such as pipelines). The former have parameters of the form <component>__<parameter>
so that it's possible to update each component of a nested object.
Args:
**params: Parameters.
Returns:
self
Raises:
-
ValueError
: If params contain invalid names.
© 2017 The TensorFlow Authors. All rights reserved.
Licensed under the Creative Commons Attribution License 3.0.
Code samples licensed under the Apache 2.0 License.
https://www.tensorflow.org/api_docs/python/tf/contrib/learn/DNNRegressor