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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.
# ==============================================================================
"""Common code used for testing `Predictor`s."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from tensorflow.contrib.learn.python.learn.estimators import constants
from tensorflow.contrib.learn.python.learn.estimators import estimator as contrib_estimator
from tensorflow.contrib.learn.python.learn.estimators import model_fn as contrib_model_fn
from tensorflow.contrib.learn.python.learn.utils import input_fn_utils
from tensorflow.python.estimator import estimator as core_estimator
from tensorflow.python.estimator import model_fn
from tensorflow.python.estimator.export import export_lib
from tensorflow.python.estimator.export import export_output
from tensorflow.python.framework import constant_op
from tensorflow.python.framework import ops
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import control_flow_ops
from tensorflow.python.ops import math_ops
from tensorflow.python.saved_model import signature_constants
def get_arithmetic_estimator(core=True, model_dir=None):
"""Returns an `Estimator` that performs basic arithmetic.
Args:
core: if `True`, returns a `tensorflow.python.estimator.Estimator`.
Otherwise, returns a `tensorflow.contrib.learn.Estimator`.
model_dir: directory in which to export checkpoints and saved models.
Returns:
An `Estimator` that performs arithmetic operations on its inputs.
"""
def _model_fn(features, labels, mode):
_ = labels
x = features['x']
y = features['y']
with ops.name_scope('outputs'):
predictions = {'sum': math_ops.add(x, y, name='sum'),
'product': math_ops.multiply(x, y, name='product'),
'difference': math_ops.subtract(x, y, name='difference')}
if core:
export_outputs = {k: export_output.PredictOutput({k: v})
for k, v in predictions.items()}
export_outputs[signature_constants.
DEFAULT_SERVING_SIGNATURE_DEF_KEY] = export_outputs['sum']
return model_fn.EstimatorSpec(mode=mode,
predictions=predictions,
export_outputs=export_outputs,
loss=constant_op.constant(0),
train_op=control_flow_ops.no_op())
else:
output_alternatives = {k: (constants.ProblemType.UNSPECIFIED, {k: v})
for k, v in predictions.items()}
return contrib_model_fn.ModelFnOps(
mode=mode,
predictions=predictions,
output_alternatives=output_alternatives,
loss=constant_op.constant(0),
train_op=control_flow_ops.no_op())
if core:
return core_estimator.Estimator(_model_fn)
else:
return contrib_estimator.Estimator(_model_fn, model_dir=model_dir)
def get_arithmetic_input_fn(core=True, train=False):
"""Returns a input functions or serving input receiver function."""
def _input_fn():
with ops.name_scope('inputs'):
x = array_ops.placeholder_with_default(0.0, shape=[], name='x')
y = array_ops.placeholder_with_default(0.0, shape=[], name='y')
label = constant_op.constant(0.0)
features = {'x': x, 'y': y}
if core:
if train:
return features, label
return export_lib.ServingInputReceiver(
features=features,
receiver_tensors=features)
else:
if train:
return features, label
return input_fn_utils.InputFnOps(
features=features,
labels={},
default_inputs=features)
return _input_fn
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