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# Copyright 2017 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 MultitaskOptimizerWrapper."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from tensorflow.contrib.opt.python.training import multitask_optimizer_wrapper
from tensorflow.python.framework import constant_op
from tensorflow.python.framework import dtypes
from tensorflow.python.ops import variables
from tensorflow.python.platform import test
from tensorflow.python.training import momentum
import numpy as np
import six
class MultitaskOptimizerWrapperTest(test.TestCase):
"""
Tests for the multitask optimizer wrapper.
"""
def testWrapper(self):
with self.test_session():
var0 = variables.Variable([1.0, 2.0], dtype=dtypes.float32)
var1 = variables.Variable([3.0, 4.0], dtype=dtypes.float32)
grads0 = constant_op.constant([0.1, 0.1], dtype=dtypes.float32)
grads1 = constant_op.constant([0.01, 0.01], dtype=dtypes.float32)
grads_allzero = constant_op.constant([0.0, 0.0], dtype=dtypes.float32)
mom_opt_impl = momentum.MomentumOptimizer(
learning_rate=2.0, momentum=0.9)
mom_opt = multitask_optimizer_wrapper.MultitaskOptimizerWrapper(
mom_opt_impl)
mom_update = mom_opt.apply_gradients(
zip([grads0, grads1], [var0, var1]))
mom_update_partial = mom_opt.apply_gradients(
zip([grads_allzero, grads1], [var0, var1]))
mom_update_no_action = mom_opt.apply_gradients(
zip([grads_allzero, grads_allzero], [var0, var1]))
self.evaluate(variables.global_variables_initializer())
# Fetch params to validate initial values
self.assertAllClose([1.0, 2.0], self.evaluate(var0))
self.assertAllClose([3.0, 4.0], self.evaluate(var1))
self.assertEqual(["momentum"], mom_opt.get_slot_names())
slot0 = mom_opt.get_slot(var0, "momentum")
self.assertEquals(slot0.get_shape(), var0.get_shape())
slot1 = mom_opt.get_slot(var1, "momentum")
self.assertEquals(slot1.get_shape(), var1.get_shape())
# Step 1: normal momentum update.
self.evaluate(mom_update)
# Check that the momentum accumulators have been updated.
self.assertAllCloseAccordingToType(np.array([0.1, 0.1]),
self.evaluate(slot0))
self.assertAllCloseAccordingToType(np.array([0.01, 0.01]),
self.evaluate(slot1))
# Check that the parameters have been updated.
self.assertAllCloseAccordingToType(
np.array([1.0 - (0.1 * 2.0), 2.0 - (0.1 * 2.0)]),
self.evaluate(var0))
self.assertAllCloseAccordingToType(
np.array([3.0 - (0.01 * 2.0), 4.0 - (0.01 * 2.0)]),
self.evaluate(var1))
# Step 2: momentum update that changes only slot1 but not slot0.
self.evaluate(mom_update_partial)
# Check that only the relevant momentum accumulator has been updated.
self.assertAllCloseAccordingToType(np.array([0.1, 0.1]),
self.evaluate(slot0))
self.assertAllCloseAccordingToType(
np.array([(0.9 * 0.01 + 0.01), (0.9 * 0.01 + 0.01)]),
self.evaluate(slot1))
# Step 3: momentum update that does not change anything.
self.evaluate(mom_update_no_action)
# Check that the momentum accumulators have *NOT* been updated.
self.assertAllCloseAccordingToType(np.array([0.1, 0.1]),
self.evaluate(slot0))
self.assertAllCloseAccordingToType(
np.array([(0.9 * 0.01 + 0.01), (0.9 * 0.01 + 0.01)]),
self.evaluate(slot1))
def testGradientClipping(self):
with self.test_session():
var0 = variables.Variable([1.0, 2.0], dtype=dtypes.float32)
var1 = variables.Variable([3.0, 4.0], dtype=dtypes.float32)
var2 = variables.Variable([3.0, 4.0], dtype=dtypes.float32)
var3 = variables.Variable([3.0, 4.0], dtype=dtypes.float32)
grads0 = constant_op.constant([10.0, 15.0], dtype=dtypes.float32)
grads1 = constant_op.constant([0.0, 5.0], dtype=dtypes.float32)
grads2 = constant_op.constant([0.0, 0.0], dtype=dtypes.float32)
grads3 = None
varlist = [var0, var1, var2, var3]
gradients = [grads0, grads1, grads2, grads3]
clipped_gradvars, global_norm = multitask_optimizer_wrapper.clip_gradients_by_global_norm(
six.moves.zip(gradients, varlist), clip_norm=1.0)
clipped_grads = list(six.moves.zip(*clipped_gradvars))[0]
reference_global_norm = np.sqrt(np.sum(np.square([10.0, 15.0, 0.0, 5.0])))
self.assertAllCloseAccordingToType(
self.evaluate(global_norm), reference_global_norm)
self.assertAllCloseAccordingToType(
self.evaluate(clipped_grads[2]), np.array([0., 0.]))
self.assertEqual(clipped_grads[3], None)
if __name__ == "__main__":
test.main()
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