| # 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. |
| # ============================================================================== |
| """Tests for Proximal Gradient Descent 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 gradient_descent |
| from tensorflow.python.training import proximal_gradient_descent |
| |
| |
| class ProximalGradientDescentOptimizerTest(xla_test.XLATestCase): |
| |
| def testResourceProximalGradientDescentwithoutRegularization(self): |
| with self.session(), self.test_scope(): |
| var0 = resource_variable_ops.ResourceVariable([0.0, 0.0]) |
| var1 = resource_variable_ops.ResourceVariable([0.0, 0.0]) |
| grads0 = constant_op.constant([0.1, 0.2]) |
| grads1 = constant_op.constant([0.01, 0.02]) |
| opt = proximal_gradient_descent.ProximalGradientDescentOptimizer( |
| 3.0, 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()) |
| |
| self.assertAllClose([0.0, 0.0], self.evaluate(var0)) |
| self.assertAllClose([0.0, 0.0], self.evaluate(var1)) |
| |
| # Run 3 steps Proximal Gradient Descent. |
| for _ in range(3): |
| update.run() |
| |
| self.assertAllClose(np.array([-0.9, -1.8]), self.evaluate(var0)) |
| self.assertAllClose(np.array([-0.09, -0.18]), self.evaluate(var1)) |
| |
| def testProximalGradientDescentwithoutRegularization2(self): |
| with self.session(), self.test_scope(): |
| var0 = resource_variable_ops.ResourceVariable([1.0, 2.0]) |
| var1 = resource_variable_ops.ResourceVariable([4.0, 3.0]) |
| grads0 = constant_op.constant([0.1, 0.2]) |
| grads1 = constant_op.constant([0.01, 0.02]) |
| |
| opt = proximal_gradient_descent.ProximalGradientDescentOptimizer( |
| 3.0, 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()) |
| |
| self.assertAllClose([1.0, 2.0], self.evaluate(var0)) |
| self.assertAllClose([4.0, 3.0], self.evaluate(var1)) |
| |
| # Run 3 steps Proximal Gradient Descent |
| for _ in range(3): |
| update.run() |
| |
| self.assertAllClose(np.array([0.1, 0.2]), self.evaluate(var0)) |
| self.assertAllClose(np.array([3.91, 2.82]), self.evaluate(var1)) |
| |
| def testProximalGradientDescentWithL1(self): |
| with self.session(), self.test_scope(): |
| var0 = resource_variable_ops.ResourceVariable([1.0, 2.0]) |
| var1 = resource_variable_ops.ResourceVariable([4.0, 3.0]) |
| grads0 = constant_op.constant([0.1, 0.2]) |
| grads1 = constant_op.constant([0.01, 0.02]) |
| |
| opt = proximal_gradient_descent.ProximalGradientDescentOptimizer( |
| 3.0, 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()) |
| |
| self.assertAllClose([1.0, 2.0], self.evaluate(var0)) |
| self.assertAllClose([4.0, 3.0], self.evaluate(var1)) |
| |
| # Run 10 steps proximal gradient descent. |
| for _ in range(10): |
| update.run() |
| |
| self.assertAllClose(np.array([-1.988, -3.988001]), self.evaluate(var0)) |
| self.assertAllClose(np.array([3.67, 2.37]), self.evaluate(var1)) |
| |
| def testProximalGradientDescentWithL1_L2(self): |
| with self.session(), self.test_scope(): |
| var0 = resource_variable_ops.ResourceVariable([1.0, 2.0]) |
| var1 = resource_variable_ops.ResourceVariable([4.0, 3.0]) |
| grads0 = constant_op.constant([0.1, 0.2]) |
| grads1 = constant_op.constant([0.01, 0.02]) |
| |
| opt = proximal_gradient_descent.ProximalGradientDescentOptimizer( |
| 3.0, 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()) |
| |
| self.assertAllClose([1.0, 2.0], self.evaluate(var0)) |
| self.assertAllClose([4.0, 3.0], self.evaluate(var1)) |
| |
| # Run 10 steps Proximal Gradient Descent |
| for _ in range(10): |
| update.run() |
| |
| self.assertAllClose(np.array([-0.0495, -0.0995]), self.evaluate(var0)) |
| self.assertAllClose(np.array([-0.0045, -0.0095]), self.evaluate(var1)) |
| |
| def applyOptimizer(self, opt, steps=5): |
| var0 = resource_variable_ops.ResourceVariable([1.0, 2.0]) |
| var1 = resource_variable_ops.ResourceVariable([3.0, 4.0]) |
| grads0 = constant_op.constant([0.1, 0.2]) |
| grads1 = constant_op.constant([0.01, 0.02]) |
| |
| update = opt.apply_gradients(zip([grads0, grads1], [var0, var1])) |
| self.evaluate(variables.global_variables_initializer()) |
| |
| self.assertAllClose([1.0, 2.0], self.evaluate(var0)) |
| self.assertAllClose([3.0, 4.0], self.evaluate(var1)) |
| |
| # Run ProximalAdagrad for a few steps |
| for _ in range(steps): |
| update.run() |
| |
| return self.evaluate(var0), self.evaluate(var1) |
| |
| def testEquivGradientDescentwithoutRegularization(self): |
| with self.session(), self.test_scope(): |
| val0, val1 = self.applyOptimizer( |
| proximal_gradient_descent.ProximalGradientDescentOptimizer( |
| 3.0, |
| l1_regularization_strength=0.0, |
| l2_regularization_strength=0.0)) |
| |
| with self.session(), self.test_scope(): |
| val2, val3 = self.applyOptimizer( |
| gradient_descent.GradientDescentOptimizer(3.0)) |
| |
| self.assertAllClose(val0, val2) |
| self.assertAllClose(val1, val3) |
| |
| |
| if __name__ == "__main__": |
| test.main() |