| # Copyright 2015 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. |
| # ============================================================================== |
| """Functional tests for Ftrl operations.""" |
| |
| from __future__ import absolute_import |
| from __future__ import division |
| from __future__ import print_function |
| |
| import numpy as np |
| |
| from tensorflow.python.framework import constant_op |
| from tensorflow.python.framework import dtypes |
| from tensorflow.python.framework import ops |
| from tensorflow.python.ops import embedding_ops |
| from tensorflow.python.ops import math_ops |
| 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(test.TestCase): |
| |
| def doTestFtrlwithoutRegularization(self, use_resource=False): |
| # The v1 optimizers do not support eager execution |
| with ops.Graph().as_default(): |
| for dtype in [dtypes.half, dtypes.float32]: |
| with self.cached_session(): |
| if use_resource: |
| var0 = resource_variable_ops.ResourceVariable([0.0, 0.0], |
| dtype=dtype) |
| var1 = resource_variable_ops.ResourceVariable([0.0, 0.0], |
| dtype=dtype) |
| else: |
| var0 = variables.Variable([0.0, 0.0], dtype=dtype) |
| var1 = variables.Variable([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) |
| update = opt.apply_gradients(zip([grads0, grads1], [var0, var1])) |
| self.evaluate(variables.global_variables_initializer()) |
| |
| v0_val, v1_val = self.evaluate([var0, var1]) |
| self.assertAllClose([0.0, 0.0], v0_val) |
| self.assertAllClose([0.0, 0.0], v1_val) |
| |
| # Run 3 steps FTRL |
| for _ in range(3): |
| update.run() |
| |
| v0_val, v1_val = self.evaluate([var0, var1]) |
| self.assertAllCloseAccordingToType( |
| np.array([-2.60260963, -4.29698515]), v0_val, half_rtol=1e-2) |
| self.assertAllCloseAccordingToType( |
| np.array([-0.28432083, -0.56694895]), v1_val) |
| |
| def testFtrlWithoutRegularization(self): |
| self.doTestFtrlwithoutRegularization(use_resource=False) |
| |
| def testResourceFtrlWithoutRegularization(self): |
| self.doTestFtrlwithoutRegularization(use_resource=True) |
| |
| def testFtrlwithoutRegularization2(self): |
| # The v1 optimizers do not support eager execution |
| with ops.Graph().as_default(): |
| for dtype in [dtypes.half, dtypes.float32]: |
| with self.cached_session(): |
| var0 = variables.Variable([1.0, 2.0], dtype=dtype) |
| var1 = variables.Variable([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) |
| update = opt.apply_gradients(zip([grads0, grads1], [var0, var1])) |
| self.evaluate(variables.global_variables_initializer()) |
| |
| v0_val, v1_val = self.evaluate([var0, var1]) |
| self.assertAllCloseAccordingToType([1.0, 2.0], v0_val) |
| self.assertAllCloseAccordingToType([4.0, 3.0], v1_val) |
| |
| # Run 3 steps FTRL |
| for _ in range(3): |
| update.run() |
| v0_val, v1_val = self.evaluate([var0, var1]) |
| self.assertAllCloseAccordingToType( |
| np.array([-2.55607247, -3.98729396]), v0_val) |
| self.assertAllCloseAccordingToType( |
| np.array([-0.28232238, -0.56096673]), v1_val) |
| |
| def testMinimizeSparseResourceVariable(self): |
| # The v1 optimizers do not support eager execution |
| with ops.Graph().as_default(): |
| for dtype in [dtypes.half, dtypes.float32, dtypes.float64]: |
| with self.cached_session(): |
| var0 = resource_variable_ops.ResourceVariable([[1.0, 2.0]], |
| dtype=dtype) |
| x = constant_op.constant([[4.0], [5.0]], dtype=dtype) |
| pred = math_ops.matmul(embedding_ops.embedding_lookup([var0], [0]), x) |
| loss = pred * pred |
| sgd_op = ftrl.FtrlOptimizer(1.0).minimize(loss) |
| self.evaluate(variables.global_variables_initializer()) |
| # Fetch params to validate initial values |
| self.assertAllCloseAccordingToType([[1.0, 2.0]], self.evaluate(var0)) |
| # Run 1 step of sgd |
| sgd_op.run() |
| # Validate updated params |
| self.assertAllCloseAccordingToType([[0, 1]], |
| self.evaluate(var0), |
| atol=0.01) |
| |
| def testFtrlWithL1(self): |
| # The v1 optimizers do not support eager execution |
| with ops.Graph().as_default(): |
| for dtype in [dtypes.half, dtypes.float32]: |
| with self.cached_session(): |
| var0 = variables.Variable([1.0, 2.0], dtype=dtype) |
| var1 = variables.Variable([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) |
| update = opt.apply_gradients(zip([grads0, grads1], [var0, var1])) |
| self.evaluate(variables.global_variables_initializer()) |
| |
| v0_val, v1_val = self.evaluate([var0, var1]) |
| self.assertAllCloseAccordingToType([1.0, 2.0], v0_val) |
| self.assertAllCloseAccordingToType([4.0, 3.0], v1_val) |
| |
| # Run 10 steps FTRL |
| for _ in range(10): |
| update.run() |
| v0_val, v1_val = self.evaluate([var0, var1]) |
| self.assertAllCloseAccordingToType( |
| np.array([-7.66718769, -10.91273689]), v0_val) |
| self.assertAllCloseAccordingToType( |
| np.array([-0.93460727, -1.86147261]), v1_val) |
| |
| def testFtrlWithBeta(self): |
| # The v1 optimizers do not support eager execution |
| with ops.Graph().as_default(): |
| for dtype in [dtypes.half, dtypes.float32]: |
| with self.cached_session(): |
| var0 = variables.Variable([1.0, 2.0], dtype=dtype) |
| var1 = variables.Variable([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, beta=0.1) |
| update = opt.apply_gradients(zip([grads0, grads1], [var0, var1])) |
| self.evaluate(variables.global_variables_initializer()) |
| |
| v0_val, v1_val = self.evaluate([var0, var1]) |
| self.assertAllCloseAccordingToType([1.0, 2.0], v0_val) |
| self.assertAllCloseAccordingToType([4.0, 3.0], v1_val) |
| |
| # Run 10 steps FTRL |
| for _ in range(10): |
| update.run() |
| v0_val, v1_val = self.evaluate([var0, var1]) |
| self.assertAllCloseAccordingToType( |
| np.array([-6.096838, -9.162214]), v0_val) |
| self.assertAllCloseAccordingToType( |
| np.array([-0.717741, -1.425132]), v1_val) |
| |
| def testFtrlWithL2_Beta(self): |
| # The v1 optimizers do not support eager execution |
| with ops.Graph().as_default(): |
| for dtype in [dtypes.half, dtypes.float32]: |
| with self.cached_session(): |
| var0 = variables.Variable([1.0, 2.0], dtype=dtype) |
| var1 = variables.Variable([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=0.1, |
| beta=0.1) |
| update = opt.apply_gradients(zip([grads0, grads1], [var0, var1])) |
| self.evaluate(variables.global_variables_initializer()) |
| |
| v0_val, v1_val = self.evaluate([var0, var1]) |
| self.assertAllCloseAccordingToType([1.0, 2.0], v0_val) |
| self.assertAllCloseAccordingToType([4.0, 3.0], v1_val) |
| |
| # Run 10 steps FTRL |
| for _ in range(10): |
| update.run() |
| v0_val, v1_val = self.evaluate([var0, var1]) |
| self.assertAllCloseAccordingToType( |
| np.array([-2.735487, -4.704625]), v0_val) |
| self.assertAllCloseAccordingToType( |
| np.array([-0.294335, -0.586556]), v1_val) |
| |
| def testFtrlWithL1_L2(self): |
| # The v1 optimizers do not support eager execution |
| with ops.Graph().as_default(): |
| for dtype in [dtypes.half, dtypes.float32]: |
| with self.cached_session(): |
| var0 = variables.Variable([1.0, 2.0], dtype=dtype) |
| var1 = variables.Variable([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) |
| update = opt.apply_gradients(zip([grads0, grads1], [var0, var1])) |
| self.evaluate(variables.global_variables_initializer()) |
| |
| v0_val, v1_val = self.evaluate([var0, var1]) |
| self.assertAllCloseAccordingToType([1.0, 2.0], v0_val) |
| self.assertAllCloseAccordingToType([4.0, 3.0], v1_val) |
| |
| # Run 10 steps FTRL |
| for _ in range(10): |
| update.run() |
| |
| v0_val, v1_val = self.evaluate([var0, var1]) |
| self.assertAllCloseAccordingToType( |
| np.array([-0.24059935, -0.46829352]), v0_val) |
| self.assertAllCloseAccordingToType( |
| np.array([-0.02406147, -0.04830509]), v1_val) |
| |
| 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. |
| """ |
| # The v1 optimizers do not support eager execution |
| with ops.Graph().as_default(): |
| for dtype in [dtypes.half, dtypes.float32]: |
| with self.cached_session(): |
| var0 = variables.Variable([1.0, 2.0], dtype=dtype) |
| var1 = variables.Variable([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) |
| update = opt.apply_gradients(zip([grads0, grads1], [var0, var1])) |
| self.evaluate(variables.global_variables_initializer()) |
| |
| v0_val, v1_val = self.evaluate([var0, var1]) |
| self.assertAllCloseAccordingToType([1.0, 2.0], v0_val) |
| self.assertAllCloseAccordingToType([4.0, 3.0], v1_val) |
| |
| # Run 10 steps FTRL |
| for _ in range(10): |
| update.run() |
| |
| v0_val, v1_val = self.evaluate([var0, var1]) |
| self.assertAllCloseAccordingToType( |
| np.array([-0.22578995, -0.44345796]), v0_val) |
| self.assertAllCloseAccordingToType( |
| np.array([-0.14378493, -0.13229476]), v1_val) |
| |
| def testFtrlWithL1_L2_L2ShrinkageSparse(self): |
| """Tests the new FTRL op with support for l2 shrinkage on sparse grads.""" |
| # The v1 optimizers do not support eager execution |
| with ops.Graph().as_default(): |
| for dtype in [dtypes.half, dtypes.float32]: |
| with self.cached_session(): |
| var0 = variables.Variable([[1.0], [2.0]], dtype=dtype) |
| var1 = variables.Variable([[4.0], [3.0]], dtype=dtype) |
| grads0 = ops.IndexedSlices( |
| constant_op.constant([0.1], shape=[1, 1], dtype=dtype), |
| constant_op.constant([0]), constant_op.constant([2, 1])) |
| grads1 = ops.IndexedSlices( |
| constant_op.constant([0.02], shape=[1, 1], dtype=dtype), |
| constant_op.constant([1]), constant_op.constant([2, 1])) |
| |
| 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) |
| update = opt.apply_gradients(zip([grads0, grads1], [var0, var1])) |
| self.evaluate(variables.global_variables_initializer()) |
| |
| v0_val, v1_val = self.evaluate([var0, var1]) |
| self.assertAllCloseAccordingToType([[1.0], [2.0]], v0_val) |
| self.assertAllCloseAccordingToType([[4.0], [3.0]], v1_val) |
| |
| # Run 10 steps FTRL |
| for _ in range(10): |
| update.run() |
| |
| v0_val, v1_val = self.evaluate([var0, var1]) |
| self.assertAllCloseAccordingToType([[-0.22578995], [2.]], v0_val) |
| self.assertAllCloseAccordingToType([[4.], [-0.13229476]], v1_val) |
| |
| def testFtrlWithL2ShrinkageDoesNotChangeLrSchedule(self): |
| """Verifies that l2 shrinkage in FTRL does not change lr schedule.""" |
| # The v1 optimizers do not support eager execution |
| with ops.Graph().as_default(): |
| for dtype in [dtypes.half, dtypes.float32]: |
| with self.cached_session(): |
| var0 = variables.Variable([1.0, 2.0], dtype=dtype) |
| var1 = variables.Variable([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()) |
| |
| v0_val, v1_val = self.evaluate([var0, var1]) |
| self.assertAllCloseAccordingToType([1.0, 2.0], v0_val) |
| self.assertAllCloseAccordingToType([1.0, 2.0], v1_val) |
| |
| # Run 10 steps FTRL |
| for _ in range(10): |
| update0.run() |
| update1.run() |
| |
| v0_val, v1_val = self.evaluate([var0, var1]) |
| # var0 is experiencing L2 shrinkage so it should be smaller than var1 |
| # in magnitude. |
| self.assertTrue((v0_val**2 < v1_val**2).all()) |
| accum0 = list(self.evaluate(opt0._slots)["accum"].values())[0] |
| accum1 = list(self.evaluate(opt1._slots)["accum"].values())[0] |
| # L2 shrinkage should not change how we update grad accumulator. |
| self.assertAllCloseAccordingToType(accum0, accum1) |
| |
| def applyOptimizer(self, opt, dtype, steps=5, is_sparse=False): |
| if is_sparse: |
| var0 = variables.Variable([[0.0], [0.0]], dtype=dtype) |
| var1 = variables.Variable([[0.0], [0.0]], dtype=dtype) |
| grads0 = ops.IndexedSlices( |
| constant_op.constant([0.1], shape=[1, 1], dtype=dtype), |
| constant_op.constant([0]), constant_op.constant([2, 1])) |
| grads1 = ops.IndexedSlices( |
| constant_op.constant([0.02], shape=[1, 1], dtype=dtype), |
| constant_op.constant([1]), constant_op.constant([2, 1])) |
| else: |
| var0 = variables.Variable([0.0, 0.0], dtype=dtype) |
| var1 = variables.Variable([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) |
| |
| update = opt.apply_gradients(zip([grads0, grads1], [var0, var1])) |
| self.evaluate(variables.global_variables_initializer()) |
| |
| sess = ops.get_default_session() |
| v0_val, v1_val = self.evaluate([var0, var1]) |
| if is_sparse: |
| self.assertAllCloseAccordingToType([[0.0], [0.0]], v0_val) |
| self.assertAllCloseAccordingToType([[0.0], [0.0]], v1_val) |
| else: |
| self.assertAllCloseAccordingToType([0.0, 0.0], v0_val) |
| self.assertAllCloseAccordingToType([0.0, 0.0], v1_val) |
| |
| # Run Ftrl for a few steps |
| for _ in range(steps): |
| update.run() |
| |
| v0_val, v1_val = self.evaluate([var0, var1]) |
| return v0_val, v1_val |
| |
| # 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 identical |
| # 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): |
| # The v1 optimizers do not support eager execution |
| with ops.Graph().as_default(): |
| for dtype in [dtypes.half, dtypes.float32]: |
| with self.cached_session(): |
| val0, val1 = self.applyOptimizer( |
| ftrl.FtrlOptimizer( |
| 3.0, |
| # Adagrad learning rate |
| learning_rate_power=-0.5, |
| initial_accumulator_value=0.1, |
| l1_regularization_strength=0.0, |
| l2_regularization_strength=0.0), |
| dtype) |
| |
| with self.cached_session(): |
| val2, val3 = self.applyOptimizer( |
| adagrad.AdagradOptimizer(3.0, initial_accumulator_value=0.1), |
| dtype) |
| |
| self.assertAllCloseAccordingToType(val0, val2, half_rtol=2e-3) |
| self.assertAllCloseAccordingToType(val1, val3, half_rtol=2e-3) |
| |
| def testEquivSparseAdagradwithoutRegularization(self): |
| # The v1 optimizers do not support eager execution |
| with ops.Graph().as_default(): |
| for dtype in [dtypes.half, dtypes.float32]: |
| with self.cached_session(): |
| val0, val1 = self.applyOptimizer( |
| ftrl.FtrlOptimizer( |
| 3.0, |
| # Adagrad learning rate |
| learning_rate_power=-0.5, |
| initial_accumulator_value=0.1, |
| l1_regularization_strength=0.0, |
| l2_regularization_strength=0.0), |
| dtype, |
| is_sparse=True) |
| |
| with self.cached_session(): |
| val2, val3 = self.applyOptimizer( |
| adagrad.AdagradOptimizer(3.0, initial_accumulator_value=0.1), |
| dtype, |
| is_sparse=True) |
| |
| self.assertAllCloseAccordingToType(val0, val2) |
| self.assertAllCloseAccordingToType(val1, val3) |
| |
| def testEquivSparseGradientDescentwithoutRegularization(self): |
| # The v1 optimizers do not support eager execution |
| with ops.Graph().as_default(): |
| for dtype in [dtypes.half, dtypes.float32]: |
| with self.cached_session(): |
| val0, val1 = self.applyOptimizer( |
| ftrl.FtrlOptimizer( |
| 3.0, |
| # Fixed learning rate |
| learning_rate_power=-0.0, |
| initial_accumulator_value=0.1, |
| l1_regularization_strength=0.0, |
| l2_regularization_strength=0.0), |
| dtype, |
| is_sparse=True) |
| |
| with self.cached_session(): |
| val2, val3 = self.applyOptimizer( |
| gradient_descent.GradientDescentOptimizer(3.0), |
| dtype, |
| is_sparse=True) |
| |
| self.assertAllCloseAccordingToType(val0, val2) |
| self.assertAllCloseAccordingToType(val1, val3) |
| |
| def testEquivGradientDescentwithoutRegularization(self): |
| # The v1 optimizers do not support eager execution |
| with ops.Graph().as_default(): |
| for dtype in [dtypes.half, dtypes.float32]: |
| with self.cached_session(): |
| val0, val1 = self.applyOptimizer( |
| ftrl.FtrlOptimizer( |
| 3.0, |
| # Fixed learning rate |
| learning_rate_power=-0.0, |
| initial_accumulator_value=0.1, |
| l1_regularization_strength=0.0, |
| l2_regularization_strength=0.0), |
| dtype) |
| |
| with self.cached_session(): |
| val2, val3 = self.applyOptimizer( |
| gradient_descent.GradientDescentOptimizer(3.0), dtype) |
| |
| self.assertAllCloseAccordingToType(val0, val2) |
| self.assertAllCloseAccordingToType(val1, val3) |
| |
| |
| if __name__ == "__main__": |
| test.main() |
