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 = DNNLinearCombinedRegressor(
# common settings
weight_column_name=weight_column_name,
# wide settings
linear_feature_columns=[sparse_feature_a_x_sparse_feature_b],
linear_optimizer=tf.compat.v1.train.FtrlOptimizer(...),
# deep settings
dnn_feature_columns=[sparse_feature_a_emb, sparse_feature_b_emb],
dnn_hidden_units=[1000, 500, 100],
dnn_optimizer=tf.compat.v1.train.ProximalAdagradOptimizer(...))
# To apply L1 and L2 regularization, you can set optimizers as follows:
tf.compat.v1.train.ProximalAdagradOptimizer(
learning_rate=0.1,
l1_regularization_strength=0.001,
l2_regularization_strength=0.001)
# It is same for FtrlOptimizer.
# Input builders
def input_fn_train: # returns x, y
...
def input_fn_eval: # returns x, y
...
def input_fn_predict: # returns x, None
...
estimator.train(input_fn_train)
estimator.evaluate(input_fn_eval)
estimator.predict(input_fn_predict)
Input of fit, train, 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 `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
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.
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 that 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.
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]).
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.
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.
Raises
ValueError
If both linear_feature_columns and dnn_features_columns are
empty at the same time.
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 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.
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].
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