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A classifier for TensorFlow Linear and DNN joined training models.

Inherits From: Estimator

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


sparse_feature_a = sparse_column_with_hash_bucket(...)
sparse_feature_b = sparse_column_with_hash_bucket(...)

sparse_feature_a_x_sparse_feature_b = crossed_column(...)

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 = DNNLinearCombinedClassifier(
    # common settings
    # wide settings
    # deep settings
    dnn_feature_columns=[sparse_feature_a_emb, sparse_feature_b_emb],
    dnn_hidden_units=[1000, 500, 100],

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

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

label_keys = ['label0', 'label1', 'label2']
estimator = DNNLinearCombinedClassifier(
    dnn_feature_columns=[sparse_feature_a_emb, sparse_feature_b_emb],
    dnn_hidden_units=[1000, 500, 100],

def input_fn_train: # returns x, y (where y is one of label_keys).

def input_fn_eval: # returns x, y (where y is one of label_keys).
def input_fn_predict: # returns x, None
# predict_classes returns one of label_keys.

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 dnn_feature_columns + linear_feature_columns:
    • if column is a SparseColumn, a feature with 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 withkey=column.namewhosevalueis aTensor`.

model_dir Directory to save model parameters, graph and etc. This can also be used to load checkpoints from the directory into a estimator to continue training a previously saved model.
n_classes number of label classes. Default is binary classification. Note that class labels are integers representing the class index (i.e. values from 0 to n_classes-1). For arbitrary label values (e.g. string labels), convert to class indices first.
weight_column_name A string defining feature column name representing weights. It is used to down weight or boost examples during training. It will be multiplied by the loss of the example.
linear_feature_columns An iterable containing all the feature columns used by linear part of the model. All items in the set must be instances of classes derived from FeatureColumn.
linear_optimizer An instance of tf.Optimizer used to apply gradients to the linear part of the model. If None, will use a FTRL optimizer.
_joint_linear_weights If True a single (possibly partitioned) variable will be used to store the linear model weights. It's faster, but requires all columns are sparse and have the 'sum' combiner.
dnn_feature_columns An iterable containing all the feature columns used by deep part of the model. All items in the set must be instances of classes derived from FeatureColumn.
dnn_optimizer An instance of tf.Optimizer used to apply gradients to the deep part of the model. If None, will use an Adagrad optimizer.
dnn_hidden_units List of hidden units per layer. All layers are fully connected.
dnn_activation_fn Activation function applied to each layer. If None, will use tf.nn.relu.
dnn_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.
embedding_lr_multipliers Optional. A dictionary from EmbeddingColumn 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.
label_keys Optional list of strings with size [n_classes] defining the label vocabulary. Only supported for n_classes > 2.
fix_global_step_increment_bug If False, the estimator needs two fit steps to optimize both linear and dnn parts. If True, this bug is fixed. New users must set this to True, but it the default value is False for backwards compatibility.

ValueError If n_classes < 2.
ValueError If both linear_feature_columns and dnn_features_columns are empty at the same time.


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.



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See Evaluable. (deprecated arguments)

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


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See BasEstimator.export. (deprecated)


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Exports inference graph as a SavedModel into given dir.

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.

The string path to the exported directory.

ValueError if an unrecognized export_type is requested.


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See Trainable. (deprecated arguments)

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


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Get parameters for this estimator.

deep boolean, optional

If True, will return the parameters for this estimator and contained subobjects that are estimators.

params mapping of string to any Parameter names mapped to their values.


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Returns list of all variable names in this model.

List of names.


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Returns value of the variable given by name.

name string, name of the tensor.

Numpy array - value of the tensor.


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

This method is expected to be called several times consecutively on different or the same chunks of the dataset. This either can implement iterative training or out-of-core/online training.

This is especially useful when the whole dataset is too big to fit in memory at the same time. Or when model is taking long time to converge, and you want to split up training into subparts.

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.

self, for chaining.

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


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

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

x features.
input_fn Input function. If set, x must be None.
batch_size Override default batch size.
outputs list of str, name of the output to predict. If None, returns classes.
as_iterable If True, return an iterable which keeps yielding predictions for each example until inputs are exhausted. Note: The inputs must terminate if you want the iterable to terminate (e.g. be sure to pass num_epochs=1 if you are using something like read_batch_features).

Numpy array of predicted classes with shape batch_size. Each predicted class is represented by its class index (i.e. integer from 0 to n_classes-1). If outputs is set, returns a dict of predictions.