• Home
  • Line#
  • Scopes#
  • Navigate#
  • Raw
  • Download
1# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
2#
3# Licensed under the Apache License, Version 2.0 (the "License");
4# you may not use this file except in compliance with the License.
5# You may obtain a copy of the License at
6#
7#     http://www.apache.org/licenses/LICENSE-2.0
8#
9# Unless required by applicable law or agreed to in writing, software
10# distributed under the License is distributed on an "AS IS" BASIS,
11# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
12# See the License for the specific language governing permissions and
13# limitations under the License.
14# ==============================================================================
15"""`Trainable` interface (deprecated).
16
17This module and all its submodules are deprecated. See
18[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md)
19for migration instructions.
20"""
21
22from __future__ import absolute_import
23from __future__ import division
24from __future__ import print_function
25
26import abc
27
28import six
29
30
31@six.add_metaclass(abc.ABCMeta)
32class Trainable(object):
33  """Interface for objects that are trainable by, e.g., `Experiment`.
34
35  THIS CLASS IS DEPRECATED.
36  """
37
38  @abc.abstractmethod
39  def fit(self,
40          x=None,
41          y=None,
42          input_fn=None,
43          steps=None,
44          batch_size=None,
45          monitors=None,
46          max_steps=None):
47    """Trains a model given training data `x` predictions and `y` labels.
48
49    Args:
50      x: Matrix of shape [n_samples, n_features...] or the dictionary of
51        Matrices.
52         Can be iterator that returns arrays of features or dictionary of arrays
53           of features.
54         The training input samples for fitting the model. If set, `input_fn`
55           must be `None`.
56      y: Vector or matrix [n_samples] or [n_samples, n_outputs] or the
57        dictionary of same.
58         Can be iterator that returns array of labels or dictionary of array of
59           labels.
60         The training label values (class labels in classification, real numbers
61           in regression).
62         If set, `input_fn` must be `None`. Note: For classification, label
63           values must
64         be integers representing the class index (i.e. values from 0 to
65         n_classes-1).
66      input_fn: Input function returning a tuple of:
67          features - `Tensor` or dictionary of string feature name to `Tensor`.
68          labels - `Tensor` or dictionary of `Tensor` with labels.
69        If input_fn is set, `x`, `y`, and `batch_size` must be `None`.
70      steps: Number of steps for which to train model. If `None`, train forever.
71        'steps' works incrementally. If you call two times fit(steps=10) then
72        training occurs in total 20 steps. If you don't want to have incremental
73        behavior please set `max_steps` instead. If set, `max_steps` must be
74        `None`.
75      batch_size: minibatch size to use on the input, defaults to first
76        dimension of `x`. Must be `None` if `input_fn` is provided.
77      monitors: List of `BaseMonitor` subclass instances. Used for callbacks
78        inside the training loop.
79      max_steps: Number of total steps for which to train model. If `None`,
80        train forever. If set, `steps` must be `None`.
81
82        Two calls to `fit(steps=100)` means 200 training
83        iterations. On the other hand, two calls to `fit(max_steps=100)` means
84        that the second call will not do any iteration since first call did
85        all 100 steps.
86
87    Returns:
88      `self`, for chaining.
89    """
90    raise NotImplementedError
91