Note: New users must set fix_global_step_increment_bug=True when creating an estimator.
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
head
A _Head object.
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.
linear_feature_columns
An iterable containing all the feature columns used by linear part of the model. All items in the set should 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 will use a single (possibly partitioned) variable to store all weights for the linear model. More efficient if there are many columns, however 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 should 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.
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.
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 the default value is False for backwards compatibility.
input_layer_partitioner
Optional. Partitioner for input layer.
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 into given dir. (deprecated)
Args
export_dir
A string containing a directory to write the exported graph and checkpoints.
input_fn
If use_deprecated_input_fn is true, then a function that given Tensor of Example strings, parses it into features that are then passed to the model. Otherwise, a function that takes no argument and returns a tuple of (features, labels), where features is a dict of string key to Tensor and labels is a Tensor that's currently not used (and so can be None).
input_feature_key
Only used if use_deprecated_input_fn is false. String key into the features dict returned by input_fn that corresponds to a the raw Example strings Tensor that the exported model will take as input. Can only be None if you're using a custom signature_fn that does not use the first arg (examples).
use_deprecated_input_fn
Determines the signature format of input_fn.
signature_fn
Function that returns a default signature and a named signature map, given Tensor of Example strings, dict of Tensors for features and Tensor or dict of Tensors for predictions.
prediction_key
The key for a tensor in the predictions dict (output from the model_fn) to use as the predictions input to the signature_fn. Optional. If None, predictions will pass to signature_fn without filtering.
default_batch_size
Default batch size of the Example placeholder.
exports_to_keep
Number of exports to keep.
checkpoint_path
the checkpoint path of the model to be exported. If it is None (which is default), will use the latest checkpoint in export_dir.
Returns
The string path to the exported directory. NB: this functionality was added ca. 2016/09/25; clients that depend on the return value may need to handle the case where this function returns None because subclasses are not returning a value.
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 arguments)
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.
input_fn
Input function. If set, x and 'batch_size' must be None.
batch_size
Override default batch size. If set, 'input_fn' must be 'None'.
outputs
list of str, name of the output to predict. If None, returns all.
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).
iterate_batches
If True, yield the whole batch at once instead of decomposing the batch into individual samples. Only relevant when as_iterable is True.
Returns
A numpy array of predicted classes or regression values if the constructor's model_fn returns a Tensor for predictions or a dict of numpy arrays if model_fn returns a dict. Returns an iterable of predictions if as_iterable is True.
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.