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# Copyright 2018 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.
# ==============================================================================
"""Tests for running legacy optimizer code with DistributionStrategy."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from absl.testing import parameterized
import numpy
from tensorflow.contrib.distribute.python import combinations
from tensorflow.contrib.distribute.python.single_loss_example import minimize_loss_example
from tensorflow.python.eager import context
from tensorflow.python.eager import test
from tensorflow.python.ops import control_flow_ops
from tensorflow.python.ops import variables
class MinimizeLossOptimizerV2Test(test.TestCase, parameterized.TestCase):
@combinations.generate(
combinations.times(
combinations.distributions_and_v2_optimizers(),
combinations.combine(mode=["graph"], use_callable_loss=[True, False])
+ combinations.combine(mode=["eager"], use_callable_loss=[True])))
def testTrainNetwork(self, distribution, optimizer_fn,
use_callable_loss=True):
with distribution.scope():
model_fn, dataset_fn, layer = minimize_loss_example(
optimizer_fn, use_bias=True, use_callable_loss=use_callable_loss)
iterator = distribution.distribute_dataset(
dataset_fn).make_one_shot_iterator()
def run_step():
return control_flow_ops.group(distribution.unwrap(
distribution.call_for_each_tower(
model_fn, iterator.get_next(), run_concurrently=layer.built)))
if not context.executing_eagerly():
with self.cached_session() as sess:
run_step = sess.make_callable(run_step())
self.evaluate(variables.global_variables_initializer())
weights, biases = [], []
for _ in range(10):
run_step()
weights.append(self.evaluate(layer.kernel))
biases.append(self.evaluate(layer.bias))
error = abs(numpy.add(numpy.squeeze(weights), numpy.squeeze(biases)) - 1)
is_not_increasing = all(y <= x for x, y in zip(error, error[1:]))
self.assertTrue(is_not_increasing)
if __name__ == "__main__":
test.main()
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