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# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""`Evaluable` interface (deprecated).
This module and all its submodules are deprecated. See
[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md)
for migration instructions.
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import abc
class Evaluable(object):
"""Interface for objects that are evaluatable by, e.g., `Experiment`.
THIS CLASS IS DEPRECATED. See
[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md)
for general migration instructions.
"""
__metaclass__ = abc.ABCMeta
@abc.abstractproperty
def model_dir(self):
"""Returns a path in which the eval process will look for checkpoints."""
raise NotImplementedError
@abc.abstractmethod
def evaluate(self,
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.
"""
raise NotImplementedError