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Interface for objects that are evaluatable by, e.g., `Experiment`.
- - -
#### `tf.contrib.learn.Evaluable.evaluate(x=None, y=None, input_fn=None, feed_fn=None, batch_size=None, steps=None, metrics=None, name=None, checkpoint_path=None)` {#Evaluable.evaluate}
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:
* <b>`x`</b>: 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`.
* <b>`y`</b>: 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).
* <b>`input_fn`</b>: 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.
* <b>`feed_fn`</b>: Function creating a feed dict every time it is called. Called
once per iteration. Must be `None` if `input_fn` is provided.
* <b>`batch_size`</b>: minibatch size to use on the input, defaults to first
dimension of `x`, if specified. Must be `None` if `input_fn` is
provided.
* <b>`steps`</b>: 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.
* <b>`metrics`</b>: 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`.
* <b>`name`</b>: Name of the evaluation if user needs to run multiple evaluations on
different data sets, such as on training data vs test data.
* <b>`checkpoint_path`</b>: Path of a specific checkpoint to evaluate. If `None`, the
latest checkpoint in `model_dir` is used.
##### Returns:
Returns `dict` with evaluation results.
- - -
#### `tf.contrib.learn.Evaluable.model_dir` {#Evaluable.model_dir}
Returns a path in which the eval process will look for checkpoints.
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