diff options
Diffstat (limited to 'tensorflow/contrib/opt/python/training/multitask_optimizer_wrapper_test.py')
-rw-r--r-- | tensorflow/contrib/opt/python/training/multitask_optimizer_wrapper_test.py | 119 |
1 files changed, 119 insertions, 0 deletions
diff --git a/tensorflow/contrib/opt/python/training/multitask_optimizer_wrapper_test.py b/tensorflow/contrib/opt/python/training/multitask_optimizer_wrapper_test.py new file mode 100644 index 0000000000..b06213f715 --- /dev/null +++ b/tensorflow/contrib/opt/python/training/multitask_optimizer_wrapper_test.py @@ -0,0 +1,119 @@ +# 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() |