1 files changed, 0 insertions, 119 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
deleted file mode 100644
index b06213f715..0000000000
--- a/tensorflow/contrib/opt/python/training/multitask_optimizer_wrapper_test.py
+++ /dev/null
@@ -1,119 +0,0 @@
-# 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()