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=DNNClassifier(feature_columns=[sparse_feature_a_emb,sparse_feature_b_emb],hidden_units=[1024,512,256])# Or estimator using the ProximalAdagradOptimizer optimizer with# regularization.estimator=DNNClassifier(feature_columns=[sparse_feature_a_emb,sparse_feature_b_emb],hidden_units=[1024,512,256],optimizer=tf.compat.v1.train.ProximalAdagradOptimizer(learning_rate=0.1,l1_regularization_strength=0.001))# Input buildersdefinput_fn_train:# returns x, y (where y represents label's class index).passestimator.fit(input_fn=input_fn_train)definput_fn_eval:# returns x, y (where y represents label's class index).passestimator.evaluate(input_fn=input_fn_eval)definput_fn_predict:# returns x, Nonepass# predict_classes returns class indices.estimator.predict_classes(input_fn=input_fn_predict)
If the user specifies label_keys in constructor, labels must be strings from
the label_keys vocabulary. Example:
label_keys=['label0','label1','label2']estimator=DNNClassifier(feature_columns=[sparse_feature_a_emb,sparse_feature_b_emb],hidden_units=[1024,512,256],label_keys=label_keys)definput_fn_train:# returns x, y (where y is one of label_keys).passestimator.fit(input_fn=input_fn_train)definput_fn_eval:# returns x, y (where y is one of label_keys).passestimator.evaluate(input_fn=input_fn_eval)definput_fn_predict:# returns x, None# predict_classes returns one of label_keys.estimator.predict_classes(input_fn=input_fn_predict)
Input of fit and evaluate should have following features,
otherwise there will be a KeyError:
if weight_column_name is not None, a feature with
key=weight_column_name whose value is a Tensor.
for each column in feature_columns:
if column is a SparseColumn, a feature with key=column.name
whose value is a SparseTensor.
if column is a WeightedSparseColumn, two features: the first with
key the id column name, the second with key the weight column name.
Both features' value must be a SparseTensor.
if column is a RealValuedColumn, a feature with key=column.name
whose value is a Tensor.
Args
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.
n_classes
number of label classes. Default is binary classification.
It must be greater than 1. Note: Class labels are integers representing
the class index (i.e. values from 0 to n_classes-1). For arbitrary
label values (e.g. string labels), convert to class indices first.
weight_column_name
A string defining feature column name representing
weights. It is used to down weight or boost examples during training. It
will be multiplied by the loss of the example.
optimizer
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.
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.
Raises
ValueError
If n_classes < 2.
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.
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.
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.
Returns predictions for given features. (deprecated argument values) (deprecated argument values)
By default, returns predicted classes. But this default will be dropped
soon. Users should either pass outputs, or call predict_classes method.
Args
x
features.
input_fn
Input function. If set, x must be None.
batch_size
Override default batch size.
outputs
list of str, name of the output to predict.
If None, returns classes.
as_iterable
If True, return an iterable which keeps yielding predictions
for each example until inputs are exhausted. Note: The inputs must
terminate if you want the iterable to terminate (e.g. be sure to pass
num_epochs=1 if you are using something like read_batch_features).
Returns
Numpy array of predicted classes with shape batch_size. Each predicted class is
represented by its class index (i.e. integer from 0 to n_classes-1).
If outputs is set, returns a dict of predictions.
Returns predicted classes 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 classes with shape batch_size. Each predicted class is
represented by its class index (i.e. integer from 0 to n_classes-1).
Returns predicted probabilities for given features. (deprecated argument values)
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).
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.
View source on GitHub