6e8976428d
This fixes some common incompatibilities with eager mode execution. PiperOrigin-RevId: 319258451 Change-Id: I93c66eb3b8c75f75fd9c1deb9526fbd937b93805
357 lines
15 KiB
Python
357 lines
15 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 Ftrl 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.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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from tensorflow.python.training import ftrl
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from tensorflow.python.training import gradient_descent
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class FtrlOptimizerTest(xla_test.XLATestCase):
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def initVariableAndGradient(self, dtype):
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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([0.02, 0.04], dtype=dtype)
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return var0, var1, grads0, grads1
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def equivAdagradTest_FtrlPart(self, steps, dtype):
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var0, var1, grads0, grads1 = self.initVariableAndGradient(dtype)
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opt = ftrl.FtrlOptimizer(
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3.0,
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learning_rate_power=-0.5, # using Adagrad learning rate
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initial_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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ftrl_update = opt.apply_gradients(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([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 Ftrl for a few steps
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for _ in range(steps):
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ftrl_update.run()
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return self.evaluate(var0), self.evaluate(var1)
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def equivAdagradTest_AdagradPart(self, steps, dtype):
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var0, var1, grads0, grads1 = self.initVariableAndGradient(dtype)
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opt = adagrad.AdagradOptimizer(3.0, initial_accumulator_value=0.1)
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adagrad_update = opt.apply_gradients(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([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 Adagrad for a few steps
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for _ in range(steps):
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adagrad_update.run()
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return self.evaluate(var0), self.evaluate(var1)
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def equivGradientDescentTest_FtrlPart(self, steps, dtype):
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var0, var1, grads0, grads1 = self.initVariableAndGradient(dtype)
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opt = ftrl.FtrlOptimizer(
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3.0,
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learning_rate_power=-0.0, # using Fixed learning rate
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initial_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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ftrl_update = opt.apply_gradients(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([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 Ftrl for a few steps
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for _ in range(steps):
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ftrl_update.run()
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return self.evaluate(var0), self.evaluate(var1)
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def equivGradientDescentTest_GradientDescentPart(self, steps, dtype):
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var0, var1, grads0, grads1 = self.initVariableAndGradient(dtype)
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opt = gradient_descent.GradientDescentOptimizer(3.0, name="sgd")
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sgd_update = opt.apply_gradients(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([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 GradientDescent for a few steps
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for _ in range(steps):
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sgd_update.run()
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return self.evaluate(var0), self.evaluate(var1)
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def testFtrlwithoutRegularization(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([0.0, 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 = ftrl.FtrlOptimizer(
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3.0,
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initial_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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ftrl_update = opt.apply_gradients(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([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 3 steps FTRL
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for _ in range(3):
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ftrl_update.run()
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# Validate updated params
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self.assertAllCloseAccordingToType(
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np.array([-2.60260963, -4.29698515]),
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self.evaluate(var0),
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float_rtol=1e-4,
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half_rtol=1e-2)
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self.assertAllCloseAccordingToType(
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np.array([-0.28432083, -0.56694895]),
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self.evaluate(var1),
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float_rtol=1e-5,
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half_rtol=1e-2)
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def testFtrlwithoutRegularization2(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([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 = ftrl.FtrlOptimizer(
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3.0,
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initial_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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ftrl_update = opt.apply_gradients(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([4.0, 3.0], self.evaluate(var1))
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# Run 3 steps FTRL
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for _ in range(3):
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ftrl_update.run()
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# Validate updated params
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self.assertAllCloseAccordingToType(
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np.array([-2.55607247, -3.98729396]),
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self.evaluate(var0),
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1e-5,
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1e-5,
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float_rtol=1e-4)
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self.assertAllCloseAccordingToType(
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np.array([-0.28232238, -0.56096673]), self.evaluate(var1), 1e-5,
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1e-5)
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def testFtrlWithL1(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([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 = ftrl.FtrlOptimizer(
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3.0,
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initial_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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ftrl_update = opt.apply_gradients(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([4.0, 3.0], self.evaluate(var1))
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# Run 10 steps FTRL
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for _ in range(10):
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ftrl_update.run()
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# Validate updated params
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self.assertAllCloseAccordingToType(
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np.array([-7.66718769, -10.91273689]),
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self.evaluate(var0),
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rtol=1e-4,
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bfloat16_rtol=1e-1,
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bfloat16_atol=1e-1)
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self.assertAllCloseAccordingToType(
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np.array([-0.93460727, -1.86147261]),
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self.evaluate(var1),
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rtol=1e-4)
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def testFtrlWithL1_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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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 = ftrl.FtrlOptimizer(
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3.0,
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initial_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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ftrl_update = opt.apply_gradients(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([4.0, 3.0], self.evaluate(var1))
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# Run 10 steps FTRL
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for _ in range(10):
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ftrl_update.run()
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# Validate updated params
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self.assertAllCloseAccordingToType(
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np.array([-0.24059935, -0.46829352]),
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self.evaluate(var0),
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rtol=1e-5)
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self.assertAllCloseAccordingToType(
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np.array([-0.02406147, -0.04830509]),
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self.evaluate(var1),
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rtol=1e-5)
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def testFtrlWithL1_L2_L2Shrinkage(self):
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"""Test the new FTRL op with support for l2 shrinkage.
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The addition of this parameter which places a constant pressure on weights
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towards the origin causes the gradient descent trajectory to differ. The
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weights will tend to have smaller magnitudes with this parameter set.
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"""
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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([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 = ftrl.FtrlOptimizer(
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3.0,
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initial_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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l2_shrinkage_regularization_strength=0.1)
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ftrl_update = opt.apply_gradients(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.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 10 steps FTRL
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for _ in range(10):
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ftrl_update.run()
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# Validate updated params
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self.assertAllCloseAccordingToType(
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np.array([-0.22578996, -0.44345799]),
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self.evaluate(var0),
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rtol=1e-4)
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self.assertAllCloseAccordingToType(
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np.array([-0.14378493, -0.13229476]),
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self.evaluate(var1),
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rtol=1e-4)
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def testFtrlWithL2ShrinkageDoesNotChangeLrSchedule(self):
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"""Verifies that l2 shrinkage in FTRL does not change lr schedule."""
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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([1.0, 2.0], dtype=dtype)
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grads0 = constant_op.constant([0.1, 0.2], dtype=dtype)
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grads1 = constant_op.constant([0.1, 0.2], dtype=dtype)
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opt0 = ftrl.FtrlOptimizer(
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3.0,
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initial_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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l2_shrinkage_regularization_strength=0.1)
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opt1 = ftrl.FtrlOptimizer(
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3.0,
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initial_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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update0 = opt0.apply_gradients([(grads0, var0)])
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update1 = opt1.apply_gradients([(grads1, var1)])
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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([1.0, 2.0], self.evaluate(var1))
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# Run 10 steps FTRL
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for _ in range(10):
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update0.run()
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update1.run()
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# var0 is experiencing L2 shrinkage so it should be smaller than var1
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# in magnitude.
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self.assertTrue((var0.eval()**2 < self.evaluate(var1)**2).all())
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accum0 = list(opt0._slots["accum"].values())[0].eval()
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accum1 = list(opt1._slots["accum"].values())[0].eval()
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# L2 shrinkage should not change how we update grad accumulator.
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self.assertAllCloseAccordingToType(accum0, accum1)
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# When variables are initialized with Zero, FTRL-Proximal has two properties:
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# 1. Without L1&L2 but with fixed learning rate, FTRL-Proximal is identical
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# with GradientDescent.
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# 2. Without L1&L2 but with adaptive learning rate, FTRL-Proximal is idential
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# with Adagrad.
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# So, basing on these two properties, we test if our implementation of
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# FTRL-Proximal performs same updates as Adagrad or GradientDescent.
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def testEquivAdagradwithoutRegularization(self):
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steps = 5
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for dtype in self.float_types:
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with self.session(), self.test_scope():
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val0, val1 = self.equivAdagradTest_FtrlPart(steps, dtype)
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with self.session(), self.test_scope():
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val2, val3 = self.equivAdagradTest_AdagradPart(steps, dtype)
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self.assertAllCloseAccordingToType(val0, val2, rtol=1e-4, half_rtol=1e-2)
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self.assertAllCloseAccordingToType(val1, val3, rtol=1e-4, half_rtol=1e-2)
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def testEquivGradientDescentwithoutRegularization(self):
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steps = 5
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for dtype in self.float_types:
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with self.session(), self.test_scope():
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val0, val1 = self.equivGradientDescentTest_FtrlPart(steps, dtype)
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with self.session(), self.test_scope():
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val2, val3 = self.equivGradientDescentTest_GradientDescentPart(
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steps, dtype)
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self.assertAllCloseAccordingToType(val0, val2, rtol=1e-5)
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self.assertAllCloseAccordingToType(val1, val3, rtol=1e-5)
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if __name__ == "__main__":
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test.main()
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