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November 2, 2016 16:00
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Example of making an interface in Python.
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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`. | |
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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