6e8976428d
This fixes some common incompatibilities with eager mode execution. PiperOrigin-RevId: 319258451 Change-Id: I93c66eb3b8c75f75fd9c1deb9526fbd937b93805
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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self.evaluate(variables.global_variables_initializer())
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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.assertEqual(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.assertEqual(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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self.evaluate(variables.global_variables_initializer())
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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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self.evaluate(variables.global_variables_initializer())
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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.assertEqual(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.assertEqual(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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