# 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.""" import numpy as np from tensorflow.python.framework import constant_op from tensorflow.python.framework import dtypes from tensorflow.python.framework import indexed_slices 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 = indexed_slices.IndexedSlices( constant_op.constant([0.1], shape=[1, 1], dtype=dtype), constant_op.constant([0]), constant_op.constant([2, 1])) grads1 = indexed_slices.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 = indexed_slices.IndexedSlices( constant_op.constant([0.1], shape=[1, 1], dtype=dtype), constant_op.constant([0]), constant_op.constant([2, 1])) grads1 = indexed_slices.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()