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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...] or the dictionary of Matrices.
Can be iterator that returns arrays of features or dictionary of 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] or the dictionary of same.
Can be iterator that returns array of labels or dictionary of 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 - `Tensor` or dictionary of string feature name to `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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