6e8976428d
This fixes some common incompatibilities with eager mode execution. PiperOrigin-RevId: 319258451 Change-Id: I93c66eb3b8c75f75fd9c1deb9526fbd937b93805
129 lines
5.5 KiB
Python
129 lines
5.5 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 Adagrad."""
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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.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 adagrad
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class AdagradOptimizerTest(xla_test.XLATestCase):
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def testBasic(self):
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for dtype in self.float_types | self.complex_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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ada_opt = adagrad.AdagradOptimizer(3.0, initial_accumulator_value=0.1)
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ada_update = ada_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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# 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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# Run 3 steps of adagrad
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for _ in range(3):
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ada_update.run()
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# Validate updated params
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self.assertAllCloseAccordingToType(
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np.array([-1.6026098728179932, -0.6026098728179932]),
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self.evaluate(var0),
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float_rtol=1e-5)
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self.assertAllCloseAccordingToType(
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np.array([2.715679168701172, 3.715679168701172]),
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self.evaluate(var1),
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float_rtol=1e-5)
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def testTensorLearningRate(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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ada_opt = adagrad.AdagradOptimizer(
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constant_op.constant(3.0), initial_accumulator_value=0.1)
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ada_update = ada_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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# 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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# Run 3 steps of adagrad
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for _ in range(3):
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ada_update.run()
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# Validate updated params
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self.assertAllCloseAccordingToType(
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np.array([-1.6026098728179932, -0.6026098728179932]),
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self.evaluate(var0),
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float_rtol=1e-5)
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self.assertAllCloseAccordingToType(
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np.array([2.715679168701172, 3.715679168701172]),
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self.evaluate(var1),
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float_rtol=1e-5)
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def testSharing(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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ada_opt = adagrad.AdagradOptimizer(3.0)
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# Apply the optimizer twice. Both applications will use
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# the same accums.
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ada_update1 = ada_opt.apply_gradients(
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zip([grads0, grads1], [var0, var1]))
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ada_update2 = ada_opt.apply_gradients(
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zip([grads0, grads1], [var0, var1]))
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self.assertEqual(["accumulator"], ada_opt.get_slot_names())
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slot0 = ada_opt.get_slot(var0, "accumulator")
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self.assertEqual(slot0.get_shape(), var0.get_shape())
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slot1 = ada_opt.get_slot(var1, "accumulator")
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self.assertEqual(slot1.get_shape(), var1.get_shape())
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self.evaluate(variables.global_variables_initializer())
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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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# Mix the first and the second adagrad for 3 steps.
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ada_update1.run()
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ada_update2.run()
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ada_update1.run()
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# Validate updated params (the same as with only 1 Adagrad).
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self.assertAllCloseAccordingToType(
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np.array([-1.6026098728179932, -0.6026098728179932]),
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self.evaluate(var0),
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float_rtol=1e-5)
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self.assertAllCloseAccordingToType(
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np.array([2.715679168701172, 3.715679168701172]),
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self.evaluate(var1),
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float_rtol=1e-5)
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if __name__ == "__main__":
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test.main()
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