tf.contrib.learn.DNNRegressor

A regressor for TensorFlow DNN models.

Inherits From: Estimator

tf.contrib.learn.DNNRegressor(
    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
)

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

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.compat.v1.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 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
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 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.
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 of tf.Optimizer used to train the model. If None, will use an Adagrad optimizer.
activation_fn	Activation function applied to each layer. If None, will use tf.nn.relu. Note that a string containing the unqualified name of the op may also be provided, e.g., "relu", "tanh", or "sigmoid".
dropout	When not None, 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.
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_dimension	Number of regression targets per example. This is the size of the last dimension of the labels and logits Tensor objects (typically, these have shape [batch_size, label_dimension]).
embedding_lr_multipliers	Optional. A dictionary from EbeddingColumn to a float 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.

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
)

See evaluable.Evaluable.

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
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 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 of str, name of the output to predict. If None, 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

View source

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

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

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

View source

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.