| # Copyright 2017 The TensorFlow Authors. All Rights Reserved. |
| # |
| # Licensed under the Apache License, Version 2.0 (the "License"); |
| # you may not use this file except in compliance with the License. |
| # You may obtain a copy of the License at |
| # |
| # http://www.apache.org/licenses/LICENSE-2.0 |
| # |
| # Unless required by applicable law or agreed to in writing, software |
| # distributed under the License is distributed on an "AS IS" BASIS, |
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. |
| # See the License for the specific language governing permissions and |
| # limitations under the License. |
| # ============================================================================== |
| """Tests for Ftrl optimizer.""" |
| |
| import numpy as np |
| |
| from tensorflow.compiler.tests import xla_test |
| from tensorflow.python.framework import constant_op |
| from tensorflow.python.ops import resource_variable_ops |
| from tensorflow.python.ops import variables |
| from tensorflow.python.platform import test |
| from tensorflow.python.training import adagrad |
| from tensorflow.python.training import ftrl |
| from tensorflow.python.training import gradient_descent |
| |
| class FtrlOptimizerTest(xla_test.XLATestCase): |
| |
| def initVariableAndGradient(self, dtype): |
| var0 = resource_variable_ops.ResourceVariable([0.0, 0.0], dtype=dtype) |
| var1 = resource_variable_ops.ResourceVariable([0.0, 0.0], dtype=dtype) |
| grads0 = constant_op.constant([0.1, 0.2], dtype=dtype) |
| grads1 = constant_op.constant([0.02, 0.04], dtype=dtype) |
| |
| return var0, var1, grads0, grads1 |
| |
| def equivAdagradTest_FtrlPart(self, steps, dtype): |
| var0, var1, grads0, grads1 = self.initVariableAndGradient(dtype) |
| opt = ftrl.FtrlOptimizer( |
| 3.0, |
| learning_rate_power=-0.5, # using Adagrad learning rate |
| initial_accumulator_value=0.1, |
| l1_regularization_strength=0.0, |
| l2_regularization_strength=0.0) |
| ftrl_update = opt.apply_gradients(zip([grads0, grads1], [var0, var1])) |
| self.evaluate(variables.global_variables_initializer()) |
| # Fetch params to validate initial values |
| self.assertAllClose([0.0, 0.0], self.evaluate(var0)) |
| self.assertAllClose([0.0, 0.0], self.evaluate(var1)) |
| |
| # Run Ftrl for a few steps |
| for _ in range(steps): |
| ftrl_update.run() |
| |
| return self.evaluate(var0), self.evaluate(var1) |
| |
| def equivAdagradTest_AdagradPart(self, steps, dtype): |
| var0, var1, grads0, grads1 = self.initVariableAndGradient(dtype) |
| opt = adagrad.AdagradOptimizer(3.0, initial_accumulator_value=0.1) |
| adagrad_update = opt.apply_gradients(zip([grads0, grads1], [var0, var1])) |
| self.evaluate(variables.global_variables_initializer()) |
| # Fetch params to validate initial values |
| self.assertAllClose([0.0, 0.0], self.evaluate(var0)) |
| self.assertAllClose([0.0, 0.0], self.evaluate(var1)) |
| |
| # Run Adagrad for a few steps |
| for _ in range(steps): |
| adagrad_update.run() |
| |
| return self.evaluate(var0), self.evaluate(var1) |
| |
| def equivGradientDescentTest_FtrlPart(self, steps, dtype): |
| var0, var1, grads0, grads1 = self.initVariableAndGradient(dtype) |
| opt = ftrl.FtrlOptimizer( |
| 3.0, |
| learning_rate_power=-0.0, # using Fixed learning rate |
| initial_accumulator_value=0.1, |
| l1_regularization_strength=0.0, |
| l2_regularization_strength=0.0) |
| ftrl_update = opt.apply_gradients(zip([grads0, grads1], [var0, var1])) |
| self.evaluate(variables.global_variables_initializer()) |
| # Fetch params to validate initial values |
| self.assertAllClose([0.0, 0.0], self.evaluate(var0)) |
| self.assertAllClose([0.0, 0.0], self.evaluate(var1)) |
| |
| # Run Ftrl for a few steps |
| for _ in range(steps): |
| ftrl_update.run() |
| |
| return self.evaluate(var0), self.evaluate(var1) |
| |
| def equivGradientDescentTest_GradientDescentPart(self, steps, dtype): |
| var0, var1, grads0, grads1 = self.initVariableAndGradient(dtype) |
| opt = gradient_descent.GradientDescentOptimizer(3.0, name="sgd") |
| sgd_update = opt.apply_gradients(zip([grads0, grads1], [var0, var1])) |
| self.evaluate(variables.global_variables_initializer()) |
| # Fetch params to validate initial values |
| self.assertAllClose([0.0, 0.0], self.evaluate(var0)) |
| self.assertAllClose([0.0, 0.0], self.evaluate(var1)) |
| |
| # Run GradientDescent for a few steps |
| for _ in range(steps): |
| sgd_update.run() |
| |
| return self.evaluate(var0), self.evaluate(var1) |
| |
| def testFtrlwithoutRegularization(self): |
| for dtype in self.float_types: |
| with self.session(), self.test_scope(): |
| var0 = resource_variable_ops.ResourceVariable([0.0, 0.0], dtype=dtype) |
| var1 = resource_variable_ops.ResourceVariable([0.0, 0.0], dtype=dtype) |
| grads0 = constant_op.constant([0.1, 0.2], dtype=dtype) |
| grads1 = constant_op.constant([0.01, 0.02], dtype=dtype) |
| opt = ftrl.FtrlOptimizer( |
| 3.0, |
| initial_accumulator_value=0.1, |
| l1_regularization_strength=0.0, |
| l2_regularization_strength=0.0) |
| ftrl_update = opt.apply_gradients(zip([grads0, grads1], [var0, var1])) |
| self.evaluate(variables.global_variables_initializer()) |
| # Fetch params to validate initial values |
| self.assertAllClose([0.0, 0.0], self.evaluate(var0)) |
| self.assertAllClose([0.0, 0.0], self.evaluate(var1)) |
| |
| # Run 3 steps FTRL |
| for _ in range(3): |
| ftrl_update.run() |
| |
| # Validate updated params |
| self.assertAllCloseAccordingToType( |
| np.array([-2.60260963, -4.29698515]), |
| self.evaluate(var0), |
| float_rtol=1e-4, |
| half_rtol=1e-2) |
| self.assertAllCloseAccordingToType( |
| np.array([-0.28432083, -0.56694895]), |
| self.evaluate(var1), |
| float_rtol=1e-5, |
| half_rtol=1e-2) |
| |
| def testFtrlwithoutRegularization2(self): |
| for dtype in self.float_types: |
| with self.session(), self.test_scope(): |
| var0 = resource_variable_ops.ResourceVariable([1.0, 2.0], dtype=dtype) |
| var1 = resource_variable_ops.ResourceVariable([4.0, 3.0], dtype=dtype) |
| grads0 = constant_op.constant([0.1, 0.2], dtype=dtype) |
| grads1 = constant_op.constant([0.01, 0.02], dtype=dtype) |
| opt = ftrl.FtrlOptimizer( |
| 3.0, |
| initial_accumulator_value=0.1, |
| l1_regularization_strength=0.0, |
| l2_regularization_strength=0.0) |
| ftrl_update = opt.apply_gradients(zip([grads0, grads1], [var0, var1])) |
| self.evaluate(variables.global_variables_initializer()) |
| # Fetch params to validate initial values |
| self.assertAllClose([1.0, 2.0], self.evaluate(var0)) |
| self.assertAllClose([4.0, 3.0], self.evaluate(var1)) |
| |
| # Run 3 steps FTRL |
| for _ in range(3): |
| ftrl_update.run() |
| |
| # Validate updated params |
| self.assertAllCloseAccordingToType( |
| np.array([-2.55607247, -3.98729396]), |
| self.evaluate(var0), |
| 1e-5, |
| 1e-5, |
| float_rtol=1e-4) |
| self.assertAllCloseAccordingToType( |
| np.array([-0.28232238, -0.56096673]), self.evaluate(var1), 1e-5, |
| 1e-5) |
| |
| def testFtrlWithL1(self): |
| for dtype in self.float_types: |
| with self.session(), self.test_scope(): |
| var0 = resource_variable_ops.ResourceVariable([1.0, 2.0], dtype=dtype) |
| var1 = resource_variable_ops.ResourceVariable([4.0, 3.0], dtype=dtype) |
| grads0 = constant_op.constant([0.1, 0.2], dtype=dtype) |
| grads1 = constant_op.constant([0.01, 0.02], dtype=dtype) |
| opt = ftrl.FtrlOptimizer( |
| 3.0, |
| initial_accumulator_value=0.1, |
| l1_regularization_strength=0.001, |
| l2_regularization_strength=0.0) |
| ftrl_update = opt.apply_gradients(zip([grads0, grads1], [var0, var1])) |
| self.evaluate(variables.global_variables_initializer()) |
| # Fetch params to validate initial values |
| self.assertAllClose([1.0, 2.0], self.evaluate(var0)) |
| self.assertAllClose([4.0, 3.0], self.evaluate(var1)) |
| |
| # Run 10 steps FTRL |
| for _ in range(10): |
| ftrl_update.run() |
| |
| # Validate updated params |
| self.assertAllCloseAccordingToType( |
| np.array([-7.66718769, -10.91273689]), |
| self.evaluate(var0), |
| rtol=1e-4, |
| bfloat16_rtol=1e-1, |
| bfloat16_atol=1e-1) |
| self.assertAllCloseAccordingToType( |
| np.array([-0.93460727, -1.86147261]), |
| self.evaluate(var1), |
| rtol=1e-4) |
| |
| def testFtrlWithL1_L2(self): |
| for dtype in self.float_types: |
| with self.session(), self.test_scope(): |
| var0 = resource_variable_ops.ResourceVariable([1.0, 2.0], dtype=dtype) |
| var1 = resource_variable_ops.ResourceVariable([4.0, 3.0], dtype=dtype) |
| grads0 = constant_op.constant([0.1, 0.2], dtype=dtype) |
| grads1 = constant_op.constant([0.01, 0.02], dtype=dtype) |
| opt = ftrl.FtrlOptimizer( |
| 3.0, |
| initial_accumulator_value=0.1, |
| l1_regularization_strength=0.001, |
| l2_regularization_strength=2.0) |
| ftrl_update = opt.apply_gradients(zip([grads0, grads1], [var0, var1])) |
| self.evaluate(variables.global_variables_initializer()) |
| # Fetch params to validate initial values |
| self.assertAllClose([1.0, 2.0], self.evaluate(var0)) |
| self.assertAllClose([4.0, 3.0], self.evaluate(var1)) |
| |
| # Run 10 steps FTRL |
| for _ in range(10): |
| ftrl_update.run() |
| |
| # Validate updated params |
| self.assertAllCloseAccordingToType( |
| np.array([-0.24059935, -0.46829352]), |
| self.evaluate(var0), |
| rtol=1e-5) |
| self.assertAllCloseAccordingToType( |
| np.array([-0.02406147, -0.04830509]), |
| self.evaluate(var1), |
| rtol=1e-5) |
| |
| def testFtrlWithL1_L2_L2Shrinkage(self): |
| """Test the new FTRL op with support for l2 shrinkage. |
| |
| The addition of this parameter which places a constant pressure on weights |
| towards the origin causes the gradient descent trajectory to differ. The |
| weights will tend to have smaller magnitudes with this parameter set. |
| """ |
| for dtype in self.float_types: |
| with self.session(), self.test_scope(): |
| var0 = resource_variable_ops.ResourceVariable([1.0, 2.0], dtype=dtype) |
| var1 = resource_variable_ops.ResourceVariable([4.0, 3.0], dtype=dtype) |
| grads0 = constant_op.constant([0.1, 0.2], dtype=dtype) |
| grads1 = constant_op.constant([0.01, 0.02], dtype=dtype) |
| opt = ftrl.FtrlOptimizer( |
| 3.0, |
| initial_accumulator_value=0.1, |
| l1_regularization_strength=0.001, |
| l2_regularization_strength=2.0, |
| l2_shrinkage_regularization_strength=0.1) |
| ftrl_update = opt.apply_gradients(zip([grads0, grads1], [var0, var1])) |
| self.evaluate(variables.global_variables_initializer()) |
| # Fetch params to validate initial values |
| self.assertAllCloseAccordingToType([1.0, 2.0], self.evaluate(var0)) |
| self.assertAllCloseAccordingToType([4.0, 3.0], self.evaluate(var1)) |
| |
| # Run 10 steps FTRL |
| for _ in range(10): |
| ftrl_update.run() |
| |
| # Validate updated params |
| self.assertAllCloseAccordingToType( |
| np.array([-0.22578996, -0.44345799]), |
| self.evaluate(var0), |
| rtol=1e-4) |
| self.assertAllCloseAccordingToType( |
| np.array([-0.14378493, -0.13229476]), |
| self.evaluate(var1), |
| rtol=1e-4) |
| |
| def testFtrlWithL2ShrinkageDoesNotChangeLrSchedule(self): |
| """Verifies that l2 shrinkage in FTRL does not change lr schedule.""" |
| for dtype in self.float_types: |
| with self.session(), self.test_scope(): |
| var0 = resource_variable_ops.ResourceVariable([1.0, 2.0], dtype=dtype) |
| var1 = resource_variable_ops.ResourceVariable([1.0, 2.0], dtype=dtype) |
| grads0 = constant_op.constant([0.1, 0.2], dtype=dtype) |
| grads1 = constant_op.constant([0.1, 0.2], dtype=dtype) |
| |
| opt0 = ftrl.FtrlOptimizer( |
| 3.0, |
| initial_accumulator_value=0.1, |
| l1_regularization_strength=0.001, |
| l2_regularization_strength=2.0, |
| l2_shrinkage_regularization_strength=0.1) |
| opt1 = ftrl.FtrlOptimizer( |
| 3.0, |
| initial_accumulator_value=0.1, |
| l1_regularization_strength=0.001, |
| l2_regularization_strength=2.0) |
| update0 = opt0.apply_gradients([(grads0, var0)]) |
| update1 = opt1.apply_gradients([(grads1, var1)]) |
| self.evaluate(variables.global_variables_initializer()) |
| |
| self.assertAllCloseAccordingToType([1.0, 2.0], self.evaluate(var0)) |
| self.assertAllCloseAccordingToType([1.0, 2.0], self.evaluate(var1)) |
| |
| # Run 10 steps FTRL |
| for _ in range(10): |
| update0.run() |
| update1.run() |
| |
| # var0 is experiencing L2 shrinkage so it should be smaller than var1 |
| # in magnitude. |
| self.assertTrue((var0.eval()**2 < self.evaluate(var1)**2).all()) |
| accum0 = list(opt0._slots["accum"].values())[0].eval() |
| accum1 = list(opt1._slots["accum"].values())[0].eval() |
| # L2 shrinkage should not change how we update grad accumulator. |
| self.assertAllCloseAccordingToType(accum0, accum1) |
| |
| # When variables are initialized with Zero, FTRL-Proximal has two properties: |
| # 1. Without L1&L2 but with fixed learning rate, FTRL-Proximal is identical |
| # with GradientDescent. |
| # 2. Without L1&L2 but with adaptive learning rate, FTRL-Proximal is idential |
| # with Adagrad. |
| # So, basing on these two properties, we test if our implementation of |
| # FTRL-Proximal performs same updates as Adagrad or GradientDescent. |
| def testEquivAdagradwithoutRegularization(self): |
| steps = 5 |
| for dtype in self.float_types: |
| with self.session(), self.test_scope(): |
| val0, val1 = self.equivAdagradTest_FtrlPart(steps, dtype) |
| with self.session(), self.test_scope(): |
| val2, val3 = self.equivAdagradTest_AdagradPart(steps, dtype) |
| |
| self.assertAllCloseAccordingToType(val0, val2, rtol=1e-4, half_rtol=1e-2) |
| self.assertAllCloseAccordingToType(val1, val3, rtol=1e-4, half_rtol=1e-2) |
| |
| def testEquivGradientDescentwithoutRegularization(self): |
| steps = 5 |
| for dtype in self.float_types: |
| with self.session(), self.test_scope(): |
| val0, val1 = self.equivGradientDescentTest_FtrlPart(steps, dtype) |
| with self.session(), self.test_scope(): |
| val2, val3 = self.equivGradientDescentTest_GradientDescentPart( |
| steps, dtype) |
| |
| self.assertAllCloseAccordingToType(val0, val2, rtol=1e-5) |
| self.assertAllCloseAccordingToType(val1, val3, rtol=1e-5) |
| |
| |
| if __name__ == "__main__": |
| test.main() |
