tf.contrib.learn.Evaluable

class tf.contrib.learn.Evaluable

See the guide: Learn (contrib) > Estimators

Interface for objects that are evaluatable by, e.g., Experiment.

Properties

model_dir

Returns a path in which the eval process will look for checkpoints.

Methods

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)

Evaluates given model with provided evaluation data.

Stop conditions - we evaluate on the given input data until one of the following: - If steps is provided, and steps batches of size batch_size are processed. - If input_fn is provided, and it raises an end-of-input exception (OutOfRangeError or StopIteration). - If x is provided, and all items in x have been processed.

The return value is a dict containing the metrics specified in metrics, as well as an entry global_step which contains the value of the global step for which this evaluation was performed.

Args:

• x: Matrix of shape [n_samples, n_features...] or dictionary of many matrices containing the input samples for fitting the model. Can be iterator that returns arrays of features or dictionary of array of features. If set, input_fn must be None.
• y: Vector or matrix [n_samples] or [n_samples, n_outputs] containing the label values (class labels in classification, real numbers in regression) or dictionary of multiple vectors/matrices. Can be iterator that returns array of targets or dictionary of array of targets. If set, input_fn must be None. Note: For classification, label values must be integers representing the class index (i.e. values from 0 to n_classes-1).
• input_fn: Input function returning a tuple of: features - Dictionary of string feature name to Tensor or Tensor. labels - Tensor or dictionary of Tensor with labels. If input_fn is set, x, y, and batch_size must be None. If steps is not provided, this should raise OutOfRangeError or StopIteration after the desired amount of data (e.g., one epoch) has been provided. See "Stop conditions" above for specifics.
• feed_fn: Function creating a feed dict every time it is called. Called once per iteration. Must be None if input_fn is provided.
• batch_size: minibatch size to use on the input, defaults to first dimension of x, if specified. Must be None if input_fn is provided.
• steps: Number of steps for which to evaluate model. If None, evaluate until x is consumed or input_fn raises an end-of-input exception. See "Stop conditions" above for specifics.

• metrics: Dict of metrics to run. If None, the default metric functions are used; if {}, no metrics are used. Otherwise, metrics should map friendly names for the metric to a MetricSpec object defining which model outputs to evaluate against which labels with which metric function.

  Metric ops should support streaming, e.g., returning update_op and value tensors. For example, see the options defined in ../../../metrics/python/ops/metrics_ops.py. name: Name of the evaluation if user needs to run multiple evaluations on different data sets, such as on training data vs test data. checkpoint_path: Path of a specific checkpoint to evaluate. If None, the latest checkpoint in model_dir is used. * hooks: List of SessionRunHook subclass instances. Used for callbacks inside the evaluation call.

Returns:

Returns dict with evaluation results.

Class Members

__init__

Defined in tensorflow/contrib/learn/python/learn/evaluable.py.