6e8976428d
This fixes some common incompatibilities with eager mode execution. PiperOrigin-RevId: 319258451 Change-Id: I93c66eb3b8c75f75fd9c1deb9526fbd937b93805
166 lines
6.9 KiB
Python
166 lines
6.9 KiB
Python
# Copyright 2016 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 AdagradDA optimizer."""
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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 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_da
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class AdagradDAOptimizerTest(xla_test.XLATestCase):
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def testAdagradDAWithoutRegularizationBasic1(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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global_step = resource_variable_ops.ResourceVariable(
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0, dtype=dtypes.int64)
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var0 = resource_variable_ops.ResourceVariable([0.0, 0.0], dtype=dtype)
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var1 = resource_variable_ops.ResourceVariable([0.0, 0.0], dtype=dtype)
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grads0 = constant_op.constant([0.1, 0.2], dtype=dtype)
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grads1 = constant_op.constant([0.01, 0.02], dtype=dtype)
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opt = adagrad_da.AdagradDAOptimizer(
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3.0,
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global_step,
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initial_gradient_squared_accumulator_value=0.1,
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l1_regularization_strength=0.0,
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l2_regularization_strength=0.0)
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update = opt.apply_gradients(
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zip([grads0, grads1], [var0, var1]), global_step=global_step)
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self.evaluate(variables.global_variables_initializer())
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self.assertAllClose([0.0, 0.0], self.evaluate(var0))
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self.assertAllClose([0.0, 0.0], self.evaluate(var1))
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# Run a step of AdagradDA
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update.run()
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# Let g be the gradient accumulator, gg be the gradient squared
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# accumulator, T be the global step, lr be the learning rate,
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# and k the initial gradient squared accumulator value.
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# w = \dfrac{sign(-g)*lr*|g - l1*T|_{+}}{l2*T*lr + \sqrt{k+gg})}
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# For -0.1*3.0*(0.1 - 0)/(0 + sqrt(0.1 + 0.1*0.1)) = -0.904534
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# similarly for others.
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self.assertAllCloseAccordingToType(
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np.array([-0.904534, -1.603567]), self.evaluate(var0))
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self.assertAllCloseAccordingToType(
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np.array([-0.094821, -0.189358]), self.evaluate(var1))
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def testAdagradDAwithoutRegularizationBasic2(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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global_step = resource_variable_ops.ResourceVariable(
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0, dtype=dtypes.int64)
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var0 = resource_variable_ops.ResourceVariable([1.0, 2.0], dtype=dtype)
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var1 = resource_variable_ops.ResourceVariable([4.0, 3.0], dtype=dtype)
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grads0 = constant_op.constant([0.1, 0.2], dtype=dtype)
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grads1 = constant_op.constant([0.01, 0.02], dtype=dtype)
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opt = adagrad_da.AdagradDAOptimizer(
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3.0,
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global_step,
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initial_gradient_squared_accumulator_value=0.1,
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l1_regularization_strength=0.0,
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l2_regularization_strength=0.0)
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update = opt.apply_gradients(
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zip([grads0, grads1], [var0, var1]), global_step=global_step)
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self.evaluate(variables.global_variables_initializer())
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self.assertAllCloseAccordingToType([1.0, 2.0], self.evaluate(var0))
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self.assertAllCloseAccordingToType([4.0, 3.0], self.evaluate(var1))
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# Run a step of AdagradDA
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update.run()
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self.assertAllCloseAccordingToType(
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np.array([-0.904534, -1.603567]), self.evaluate(var0))
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self.assertAllCloseAccordingToType(
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np.array([-0.094821, -0.189358]), self.evaluate(var1))
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def testAdagradDAWithL1(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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global_step = resource_variable_ops.ResourceVariable(
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0, dtype=dtypes.int64)
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var0 = resource_variable_ops.ResourceVariable([1.0, 2.0], dtype=dtype)
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var1 = resource_variable_ops.ResourceVariable([4.0, 3.0], dtype=dtype)
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grads0 = constant_op.constant([0.1, 0.2], dtype=dtype)
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grads1 = constant_op.constant([0.01, 0.02], dtype=dtype)
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opt = adagrad_da.AdagradDAOptimizer(
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3.0,
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global_step,
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initial_gradient_squared_accumulator_value=0.1,
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l1_regularization_strength=0.001,
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l2_regularization_strength=0.0)
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update = opt.apply_gradients(
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zip([grads0, grads1], [var0, var1]), global_step=global_step)
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self.evaluate(variables.global_variables_initializer())
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self.assertAllCloseAccordingToType([1.0, 2.0], self.evaluate(var0))
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self.assertAllCloseAccordingToType([4.0, 3.0], self.evaluate(var1))
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# Run a step of AdagradDA
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update.run()
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self.assertAllCloseAccordingToType(
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np.array([-0.895489, -1.59555]), self.evaluate(var0))
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self.assertAllCloseAccordingToType(
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np.array([-0.085339, -0.17989]), self.evaluate(var1))
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def testAdagradDAWithL1_L2(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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global_step = resource_variable_ops.ResourceVariable(
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0, dtype=dtypes.int64)
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var0 = resource_variable_ops.ResourceVariable([1.0, 2.0], dtype=dtype)
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var1 = resource_variable_ops.ResourceVariable([4.0, 3.0], dtype=dtype)
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grads0 = constant_op.constant([0.1, 0.2], dtype=dtype)
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grads1 = constant_op.constant([0.01, 0.02], dtype=dtype)
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opt = adagrad_da.AdagradDAOptimizer(
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3.0,
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global_step,
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initial_gradient_squared_accumulator_value=0.1,
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l1_regularization_strength=0.001,
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l2_regularization_strength=2.0)
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update = opt.apply_gradients(
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zip([grads0, grads1], [var0, var1]), global_step=global_step)
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self.evaluate(variables.global_variables_initializer())
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self.assertAllCloseAccordingToType([1.0, 2.0], self.evaluate(var0))
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self.assertAllCloseAccordingToType([4.0, 3.0], self.evaluate(var1))
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# Run a step of AdagradDA
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update.run()
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self.assertAllCloseAccordingToType(
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np.array([-0.046907, -0.093659]), self.evaluate(var0))
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self.assertAllCloseAccordingToType(
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np.array([-0.004275, -0.009023]), self.evaluate(var1))
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if __name__ == "__main__":
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test.main()
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