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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.
# ==============================================================================
"""`Trainable` interface."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import abc
class Trainable(object):
"""Interface for objects that are trainable by, e.g., `Experiment`.
"""
__metaclass__ = abc.ABCMeta
@abc.abstractmethod
def fit(self, x=None, y=None, input_fn=None, steps=None, batch_size=None,
monitors=None, max_steps=None):
"""Trains a model given training data `x` predictions and `y` labels.
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`. 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`.
steps: Number of steps for which to train model. If `None`, train forever.
'steps' works incrementally. If you call two times fit(steps=10) then
training occurs in total 20 steps. If you don't want to have incremental
behaviour please set `max_steps` instead. If set, `max_steps` must be
`None`.
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.
max_steps: Number of total steps for which to train model. If `None`,
train forever. If set, `steps` must be `None`.
Two calls to `fit(steps=100)` means 200 training
iterations. On the other hand, two calls to `fit(max_steps=100)` means
that the second call will not do any iteration since first call did
all 100 steps.
Returns:
`self`, for chaining.
"""
raise NotImplementedError
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