# 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 Adagrad 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 proximal_adagrad


class ProximalAdagradOptimizerTest(xla_test.XLATestCase):

  def testResourceProximalAdagradwithoutRegularization(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_adagrad.ProximalAdagradOptimizer(
          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())

      self.assertAllClose([0.0, 0.0], self.evaluate(var0))
      self.assertAllClose([0.0, 0.0], self.evaluate(var1))

      # Run 3 steps Proximal Adagrad.
      for _ in range(3):
        update.run()

      self.assertAllClose(
          np.array([-2.60260963, -4.29698515]), self.evaluate(var0))
      self.assertAllClose(
          np.array([-0.28432083, -0.56694895]), self.evaluate(var1))
      opt_vars = opt.variables()
      self.assertStartsWith(opt_vars[0].name, var0._shared_name)
      self.assertStartsWith(opt_vars[1].name, var1._shared_name)
      self.assertEqual(2, len(opt_vars))

  def testProximalAdagradwithoutRegularization2(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_adagrad.ProximalAdagradOptimizer(
          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())

      self.assertAllClose([1.0, 2.0], self.evaluate(var0))
      self.assertAllClose([4.0, 3.0], self.evaluate(var1))

      # Run 3 steps Proximal Adagrad.
      for _ in range(3):
        update.run()
      self.assertAllClose(np.array([-1.60261, -2.296985]), self.evaluate(var0))
      self.assertAllClose(np.array([3.715679, 2.433051]), self.evaluate(var1))

  def testProximalAdagradWithL1(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_adagrad.ProximalAdagradOptimizer(
          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())

      self.assertAllClose([1.0, 2.0], self.evaluate(var0))
      self.assertAllClose([4.0, 3.0], self.evaluate(var1))

      # Run 10 steps Proximal Adagrad
      for _ in range(10):
        update.run()
      self.assertAllClose(np.array([-6.663634, -9.190331]), self.evaluate(var0))
      self.assertAllClose(np.array([2.959304, 1.029232]), self.evaluate(var1))

  def testProximalAdagradWithL1_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_adagrad.ProximalAdagradOptimizer(
          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())

      self.assertAllClose([1.0, 2.0], self.evaluate(var0))
      self.assertAllClose([4.0, 3.0], self.evaluate(var1))

      # Run 10 steps Proximal Adagrad.
      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 testEquivAdagradwithoutRegularization(self):
    with self.session(), self.test_scope():
      val0, val1 = self.applyOptimizer(
          proximal_adagrad.ProximalAdagradOptimizer(
              3.0,
              initial_accumulator_value=0.1,
              l1_regularization_strength=0.0,
              l2_regularization_strength=0.0))

    with self.session(), self.test_scope():
      val2, val3 = self.applyOptimizer(
          adagrad.AdagradOptimizer(
              3.0, initial_accumulator_value=0.1))

    self.assertAllClose(val0, val2)
    self.assertAllClose(val1, val3)


if __name__ == "__main__":
  test.main()