6762ca15c4
The session returned by cached_session uses soft placement, something we don't want for XLA_* devices. With soft placement ops lacking XLA kernels silently fall back and run on the CPU, misleading us into thinking we have more test coverage than we actually do. With this test some tests (rightly) start failing because they were testing ops with dtypes the XLA kernels do not support. I've removed these dtypes from the tests. This CL partially addresses b/132430685. It stubs out "cached_session" and "test_session" to raise errors, so we have more confidence that the compiler is being exercised. However, we still use XLA_* devices to exercise XLA, which has a different code path than xla.compile and tpu.rewrite. This needs to be incrementally fixed. PiperOrigin-RevId: 248437673
191 lines
8.7 KiB
Python
191 lines
8.7 KiB
Python
# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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"""Tests for Momentum."""
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import numpy as np
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from tensorflow.compiler.tests import xla_test
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from tensorflow.python.framework import constant_op
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from tensorflow.python.framework import dtypes
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from tensorflow.python.ops import array_ops
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from tensorflow.python.ops import resource_variable_ops
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from tensorflow.python.ops import variables
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from tensorflow.python.platform import test
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from tensorflow.python.training import momentum as momentum_lib
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class MomentumOptimizerTest(xla_test.XLATestCase):
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def _update_nesterov_momentum_numpy(self, var, accum, g, lr, momentum):
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var += accum * lr * momentum
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accum = accum * momentum + g
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var -= lr * accum
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var -= accum * lr * momentum
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return var, accum
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def testBasic(self):
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for dtype in self.float_types:
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with self.session(), self.test_scope():
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var0 = resource_variable_ops.ResourceVariable([1.0, 2.0], dtype=dtype)
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var1 = resource_variable_ops.ResourceVariable([3.0, 4.0], dtype=dtype)
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grads0 = constant_op.constant([0.1, 0.1], dtype=dtype)
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grads1 = constant_op.constant([0.01, 0.01], dtype=dtype)
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mom_opt = momentum_lib.MomentumOptimizer(
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learning_rate=2.0, momentum=0.9)
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mom_update = mom_opt.apply_gradients(
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zip([grads0, grads1], [var0, var1]))
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variables.global_variables_initializer().run()
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# Check we have slots
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self.assertEqual(["momentum"], mom_opt.get_slot_names())
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slot0 = mom_opt.get_slot(var0, "momentum")
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self.assertEquals(slot0.get_shape(), var0.get_shape())
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self.assertFalse(slot0 in variables.trainable_variables())
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slot1 = mom_opt.get_slot(var1, "momentum")
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self.assertEquals(slot1.get_shape(), var1.get_shape())
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self.assertFalse(slot1 in variables.trainable_variables())
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# Fetch params to validate initial values
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self.assertAllClose([1.0, 2.0], self.evaluate(var0))
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self.assertAllClose([3.0, 4.0], self.evaluate(var1))
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# Step 1: the momentum accumulators where 0. So we should see a normal
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# update: v -= grad * learning_rate
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mom_update.run()
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# Check that the momentum accumulators have been updated.
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self.assertAllCloseAccordingToType(
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np.array([0.1, 0.1]), self.evaluate(slot0))
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self.assertAllCloseAccordingToType(
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np.array([0.01, 0.01]), self.evaluate(slot1))
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# Check that the parameters have been updated.
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self.assertAllCloseAccordingToType(
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np.array([1.0 - (0.1 * 2.0), 2.0 - (0.1 * 2.0)]),
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self.evaluate(var0))
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self.assertAllCloseAccordingToType(
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np.array([3.0 - (0.01 * 2.0), 4.0 - (0.01 * 2.0)]),
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self.evaluate(var1))
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# Step 2: the momentum accumulators contain the previous update.
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mom_update.run()
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# Check that the momentum accumulators have been updated.
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self.assertAllCloseAccordingToType(
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np.array([(0.9 * 0.1 + 0.1), (0.9 * 0.1 + 0.1)]),
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self.evaluate(slot0))
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self.assertAllCloseAccordingToType(
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np.array([(0.9 * 0.01 + 0.01), (0.9 * 0.01 + 0.01)]),
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self.evaluate(slot1))
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# Check that the parameters have been updated.
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self.assertAllCloseAccordingToType(
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np.array([
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1.0 - (0.1 * 2.0) - ((0.9 * 0.1 + 0.1) * 2.0),
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2.0 - (0.1 * 2.0) - ((0.9 * 0.1 + 0.1) * 2.0)
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]), self.evaluate(var0))
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self.assertAllCloseAccordingToType(
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np.array([
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2.98 - ((0.9 * 0.01 + 0.01) * 2.0),
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3.98 - ((0.9 * 0.01 + 0.01) * 2.0)
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]), self.evaluate(var1))
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def testNesterovMomentum(self):
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for dtype in self.float_types:
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with self.session(), self.test_scope():
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var0 = resource_variable_ops.ResourceVariable([0.1, 0.2], dtype=dtype)
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var1 = resource_variable_ops.ResourceVariable([0.3, 0.4], dtype=dtype)
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var0_np = np.array([0.1, 0.2], dtype=dtype)
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var1_np = np.array([0.3, 0.4], dtype=dtype)
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accum0_np = np.array([0.0, 0.0], dtype=dtype)
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accum1_np = np.array([0.0, 0.0], dtype=dtype)
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cost = 0.4 * var0 * var0 + 0.9 * var1
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global_step = resource_variable_ops.ResourceVariable(
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array_ops.zeros([], dtypes.int32), name="global_step")
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mom_op = momentum_lib.MomentumOptimizer(
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learning_rate=0.1, momentum=0.9, use_nesterov=True)
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opt_op = mom_op.minimize(cost, global_step, [var0, var1])
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variables.global_variables_initializer().run()
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for _ in range(1, 5):
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opt_op.run()
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var0_np, accum0_np = self._update_nesterov_momentum_numpy(
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var0_np, accum0_np, var0_np * 0.8, 0.1, 0.9)
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var1_np, accum1_np = self._update_nesterov_momentum_numpy(
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var1_np, accum1_np, 0.9, 0.1, 0.9)
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self.assertAllCloseAccordingToType(var0_np, self.evaluate(var0))
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self.assertAllCloseAccordingToType(var1_np, self.evaluate(var1))
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def testTensorLearningRateAndMomentum(self):
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for dtype in self.float_types:
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with self.session(), self.test_scope():
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var0 = resource_variable_ops.ResourceVariable([1.0, 2.0], dtype=dtype)
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var1 = resource_variable_ops.ResourceVariable([3.0, 4.0], dtype=dtype)
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grads0 = constant_op.constant([0.1, 0.1], dtype=dtype)
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grads1 = constant_op.constant([0.01, 0.01], dtype=dtype)
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mom_opt = momentum_lib.MomentumOptimizer(
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learning_rate=constant_op.constant(2.0),
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momentum=constant_op.constant(0.9))
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mom_update = mom_opt.apply_gradients(
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zip([grads0, grads1], [var0, var1]))
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variables.global_variables_initializer().run()
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# Check we have slots
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self.assertEqual(["momentum"], mom_opt.get_slot_names())
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slot0 = mom_opt.get_slot(var0, "momentum")
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self.assertEquals(slot0.get_shape(), var0.get_shape())
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self.assertFalse(slot0 in variables.trainable_variables())
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slot1 = mom_opt.get_slot(var1, "momentum")
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self.assertEquals(slot1.get_shape(), var1.get_shape())
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self.assertFalse(slot1 in variables.trainable_variables())
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# Fetch params to validate initial values
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self.assertAllClose([1.0, 2.0], self.evaluate(var0))
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self.assertAllClose([3.0, 4.0], self.evaluate(var1))
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# Step 1: the momentum accumulators where 0. So we should see a normal
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# update: v -= grad * learning_rate
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mom_update.run()
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# Check that the momentum accumulators have been updated.
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self.assertAllCloseAccordingToType(
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np.array([0.1, 0.1]), self.evaluate(slot0))
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self.assertAllCloseAccordingToType(
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np.array([0.01, 0.01]), self.evaluate(slot1))
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# Check that the parameters have been updated.
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self.assertAllCloseAccordingToType(
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np.array([1.0 - (0.1 * 2.0), 2.0 - (0.1 * 2.0)]),
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self.evaluate(var0))
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self.assertAllCloseAccordingToType(
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np.array([3.0 - (0.01 * 2.0), 4.0 - (0.01 * 2.0)]),
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self.evaluate(var1))
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# Step 2: the momentum accumulators contain the previous update.
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mom_update.run()
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# Check that the momentum accumulators have been updated.
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self.assertAllCloseAccordingToType(
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np.array([(0.9 * 0.1 + 0.1), (0.9 * 0.1 + 0.1)]),
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self.evaluate(slot0))
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self.assertAllCloseAccordingToType(
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np.array([(0.9 * 0.01 + 0.01), (0.9 * 0.01 + 0.01)]),
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self.evaluate(slot1))
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# Check that the parameters have been updated.
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self.assertAllCloseAccordingToType(
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np.array([
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1.0 - (0.1 * 2.0) - ((0.9 * 0.1 + 0.1) * 2.0),
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2.0 - (0.1 * 2.0) - ((0.9 * 0.1 + 0.1) * 2.0)
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]), self.evaluate(var0))
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self.assertAllCloseAccordingToType(
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np.array([
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2.98 - ((0.9 * 0.01 + 0.01) * 2.0),
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3.98 - ((0.9 * 0.01 + 0.01) * 2.0)
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]), self.evaluate(var1))
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if __name__ == "__main__":
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test.main()
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