text: # 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 test for learning rate decay."""
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  import tensorflow.compat.v2 as tf
  
  import math
  from keras import combinations
  from keras import keras_parameterized
  from keras.optimizer_v2 import legacy_learning_rate_decay as learning_rate_decay
  
  
  @combinations.generate(combinations.combine(mode=["graph", "eager"]))
  class LRDecayTest(keras_parameterized.TestCase):
  
    def testContinuous(self):
      self.evaluate(tf.compat.v1.global_variables_initializer())
      step = 5
      decayed_lr = tf.compat.v1.train.exponential_decay(0.05, step, 10, 0.96)
      expected = .05 * 0.96**(5.0 / 10.0)
      self.assertAllClose(self.evaluate(decayed_lr), expected, 1e-6)
  
    def testStaircase(self):
      if tf.executing_eagerly():
        step = tf.Variable(0)
        self.evaluate(tf.compat.v1.global_variables_initializer())
        decayed_lr = tf.compat.v1.train.exponential_decay(
            .1, step, 3, 0.96, staircase=True)
  
        # No change to learning rate due to staircase
        expected = .1
        self.evaluate(step.assign(1))
        self.assertAllClose(self.evaluate(decayed_lr), expected, 1e-6)
  
        expected = .1
        self.evaluate(step.assign(2))
        self.assertAllClose(self.evaluate(decayed_lr), .1, 1e-6)
  
        # Decayed learning rate
        expected = .1 * 0.96 ** (100 // 3)
        self.evaluate(step.assign(100))
        self.assertAllClose(self.evaluate(decayed_lr), expected, 1e-6)
  
    def testVariables(self):
      step = tf.compat.v1.Variable(1)
  
      decayed_lr = tf.compat.v1.train.exponential_decay(
          .1, step, 3, 0.96, staircase=True)
      self.evaluate(tf.compat.v1.global_variables_initializer())
      # No change to learning rate
      assign_1 = step.assign(1)
      if not tf.executing_eagerly():
        self.evaluate(assign_1.op)
      self.assertAllClose(self.evaluate(decayed_lr), .1, 1e-6)
      assign_2 = step.assign(2)
      if not tf.executing_eagerly():
        self.evaluate(assign_2.op)
      self.assertAllClose(self.evaluate(decayed_lr), .1, 1e-6)
      # Decayed learning rate
      assign_100 = step.assign(100)
      if not tf.executing_eagerly():
        self.evaluate(assign_100.op)
      expected = .1 * 0.96**(100 // 3)
      self.assertAllClose(self.evaluate(decayed_lr), expected, 1e-6)
  
    def testPiecewiseConstant(self):
      x = tf.Variable(-999)
      decayed_lr = tf.compat.v1.train.piecewise_constant(
          x, [100, 110, 120], [1.0, 0.1, 0.01, 0.001])
  
      self.evaluate(tf.compat.v1.global_variables_initializer())
  
      self.assertAllClose(self.evaluate(decayed_lr), 1.0, 1e-6)
      self.evaluate(x.assign(100))
      self.assertAllClose(self.evaluate(decayed_lr), 1.0, 1e-6)
      self.evaluate(x.assign(105))
      self.assertAllClose(self.evaluate(decayed_lr), 0.1, 1e-6)
      self.evaluate(x.assign(110))
      self.assertAllClose(self.evaluate(decayed_lr), 0.1, 1e-6)
      self.evaluate(x.assign(120))
      self.assertAllClose(self.evaluate(decayed_lr), 0.01, 1e-6)
      self.evaluate(x.assign(999))
      self.assertAllClose(self.evaluate(decayed_lr), 0.001, 1e-6)
  
    def testPiecewiseConstantEdgeCases(self):
      x_int = tf.Variable(0, dtype=tf.int32)
      boundaries, values = [-1.0, 1.0], [1, 2, 3]
      with self.assertRaises(ValueError):
        decayed_lr = tf.compat.v1.train.piecewise_constant(
            x_int, boundaries, values)
        if tf.executing_eagerly():
          decayed_lr()
  
      x = tf.Variable(0.0)
      boundaries, values = [-1.0, 1.0], [1.0, 2, 3]
      with self.assertRaises(ValueError):
        decayed_lr = tf.compat.v1.train.piecewise_constant(
            x, boundaries, values)
        if tf.executing_eagerly():
          decayed_lr()
  
      # Test that ref types are valid.
      if not tf.executing_eagerly():
        x = tf.compat.v1.Variable(0.0, use_resource=False)
        x_ref = x.op.outputs[0]   # float32_ref tensor should be accepted
        boundaries, values = [1.0, 2.0], [1, 2, 3]
        tf.compat.v1.train.piecewise_constant(x_ref, boundaries, values)
  
      # Test casting boundaries from int32 to int64.
      x_int64 = tf.Variable(0, dtype=tf.int64)
      boundaries, values = [1, 2, 3], [0.4, 0.5, 0.6, 0.7]
      decayed_lr = tf.compat.v1.train.piecewise_constant(
          x_int64, boundaries, values)
  
      self.evaluate(tf.compat.v1.global_variables_initializer())
      self.assertAllClose(self.evaluate(decayed_lr), 0.4, 1e-6)
      self.evaluate(x_int64.assign(1))
      self.assertAllClose(self.evaluate(decayed_lr), 0.4, 1e-6)
      self.evaluate(x_int64.assign(2))
      self.assertAllClose(self.evaluate(decayed_lr), 0.5, 1e-6)
      self.evaluate(x_int64.assign(3))
      self.assertAllClose(self.evaluate(decayed_lr), 0.6, 1e-6)
      self.evaluate(x_int64.assign(4))
      self.assertAllClose(self.evaluate(decayed_lr), 0.7, 1e-6)
  
  
  @combinations.generate(combinations.combine(mode=["graph", "eager"]))
  class LinearDecayTest(keras_parameterized.TestCase):
  
    def testHalfWay(self):
      step = 5
      lr = 0.05
      end_lr = 0.0
      decayed_lr = tf.compat.v1.train.polynomial_decay(lr, step, 10, end_lr)
      expected = lr * 0.5
      self.assertAllClose(self.evaluate(decayed_lr), expected, 1e-6)
  
    def testEnd(self):
      step = 10
      lr = 0.05
      end_lr = 0.001
      decayed_lr = tf.compat.v1.train.polynomial_decay(lr, step, 10, end_lr)
      expected = end_lr
      self.assertAllClose(self.evaluate(decayed_lr), expected, 1e-6)
  
    def testHalfWayWithEnd(self):
      step = 5
      lr = 0.05
      end_lr = 0.001
      decayed_lr = tf.compat.v1.train.polynomial_decay(lr, step, 10, end_lr)
      expected = (lr + end_lr) * 0.5
      self.assertAllClose(self.evaluate(decayed_lr), expected, 1e-6)
  
    def testBeyondEnd(self):
      step = 15
      lr = 0.05
      end_lr = 0.001
      decayed_lr = tf.compat.v1.train.polynomial_decay(lr, step, 10, end_lr)
      expected = end_lr
      self.assertAllClose(self.evaluate(decayed_lr), expected, 1e-6)
  
    def testBeyondEndWithCycle(self):
      step = 15
      lr = 0.05
      end_lr = 0.001
      decayed_lr = tf.compat.v1.train.polynomial_decay(
          lr, step, 10, end_lr, cycle=True)
      expected = (lr - end_lr) * 0.25 + end_lr
      self.assertAllClose(self.evaluate(decayed_lr), expected, 1e-6)
  
  
  @combinations.generate(combinations.combine(mode=["graph", "eager"]))
  class SqrtDecayTest(keras_parameterized.TestCase):
  
    def testHalfWay(self):
      step = 5
      lr = 0.05
      end_lr = 0.0
      power = 0.5
      decayed_lr = tf.compat.v1.train.polynomial_decay(
          lr, step, 10, end_lr, power=power)
      expected = lr * 0.5**power
      self.assertAllClose(self.evaluate(decayed_lr), expected, 1e-6)
  
    def testEnd(self):
      step = 10
      lr = 0.05
      end_lr = 0.001
      power = 0.5
      decayed_lr = tf.compat.v1.train.polynomial_decay(
          lr, step, 10, end_lr, power=power)
      expected = end_lr
      self.assertAllClose(self.evaluate(decayed_lr), expected, 1e-6)
  
    def testHalfWayWithEnd(self):
      step = 5
      lr = 0.05
      end_lr = 0.001
      power = 0.5
      decayed_lr = tf.compat.v1.train.polynomial_decay(
          lr, step, 10, end_lr, power=power)
      expected = (lr - end_lr) * 0.5**power + end_lr
      self.assertAllClose(self.evaluate(decayed_lr), expected, 1e-6)
  
    def testBeyondEnd(self):
      step = 15
      lr = 0.05
      end_lr = 0.001
      power = 0.5
      decayed_lr = tf.compat.v1.train.polynomial_decay(
          lr, step, 10, end_lr, power=power)
      expected = end_lr
      self.assertAllClose(self.evaluate(decayed_lr), expected, 1e-6)
  
    def testBeyondEndWithCycle(self):
      step = 15
      lr = 0.05
      end_lr = 0.001
      power = 0.5
      decayed_lr = tf.compat.v1.train.polynomial_decay(
          lr, step, 10, end_lr, power=power, cycle=True)
      expected = (lr - end_lr) * 0.25**power + end_lr
      self.assertAllClose(self.evaluate(decayed_lr), expected, 1e-6)
  
  
  @combinations.generate(combinations.combine(mode=["graph", "eager"]))
  class PolynomialDecayTest(keras_parameterized.TestCase):
  
    def testBeginWithCycle(self):
      lr = 0.001
      decay_steps = 10
      step = 0
      decayed_lr = tf.compat.v1.train.polynomial_decay(
          lr, step, decay_steps, cycle=True)
      expected = lr
      self.assertAllClose(self.evaluate(decayed_lr), expected, 1e-6)
  
  
  @combinations.generate(combinations.combine(mode=["graph", "eager"]))
  class ExponentialDecayTest(keras_parameterized.TestCase):
  
    def testDecay(self):
      initial_lr = 0.1
      k = 10
      decay_rate = 0.96
      step = tf.Variable(0)
      decayed_lr = tf.compat.v1.train.natural_exp_decay(initial_lr, step, k,
                                                         decay_rate)
  
      self.evaluate(tf.compat.v1.global_variables_initializer())
      for i in range(k + 1):
        expected = initial_lr * math.exp(-i / k * decay_rate)
        self.assertAllClose(self.evaluate(decayed_lr), expected, 1e-6)
        self.evaluate(step.assign_add(1))
  
    def testStaircase(self):
      initial_lr = 0.1
      k = 10
      decay_rate = 0.96
      step = tf.Variable(0)
      decayed_lr = tf.compat.v1.train.natural_exp_decay(
          initial_lr, step, k, decay_rate, staircase=True)
  
      self.evaluate(tf.compat.v1.global_variables_initializer())
      for i in range(k + 1):
        expected = initial_lr * math.exp(-decay_rate * (i // k))
        self.assertAllClose(self.evaluate(decayed_lr), expected, 1e-6)
        self.evaluate(step.assign_add(1))
  
  
  @combinations.generate(combinations.combine(mode=["graph", "eager"]))
  class InverseDecayTest(keras_parameterized.TestCase):
  
    def testDecay(self):
      initial_lr = 0.1
      k = 10
      decay_rate = 0.96
      step = tf.Variable(0)
      decayed_lr = tf.compat.v1.train.inverse_time_decay(initial_lr, step, k,
                                                          decay_rate)
  
      self.evaluate(tf.compat.v1.global_variables_initializer())
      for i in range(k + 1):
        expected = initial_lr / (1 + i / k * decay_rate)
        self.assertAllClose(self.evaluate(decayed_lr), expected, 1e-6)
        self.evaluate(step.assign_add(1))
  
    def testStaircase(self):
      initial_lr = 0.1
      k = 10
      decay_rate = 0.96
      step = tf.Variable(0)
      decayed_lr = tf.compat.v1.train.inverse_time_decay(
          initial_lr, step, k, decay_rate, staircase=True)
  
      self.evaluate(tf.compat.v1.global_variables_initializer())
      for i in range(k + 1):
        expected = initial_lr / (1 + decay_rate * (i // k))
        self.assertAllClose(self.evaluate(decayed_lr), expected, 1e-6)
        self.evaluate(step.assign_add(1))
  
  
  @combinations.generate(combinations.combine(mode=["graph", "eager"]))
  class CosineDecayTest(keras_parameterized.TestCase):
  
    def np_cosine_decay(self, step, decay_steps, alpha=0.0):
      step = min(step, decay_steps)
      completed_fraction = step / decay_steps
      decay = 0.5 * (1.0 + math.cos(math.pi * completed_fraction))
      return (1.0 - alpha) * decay + alpha
  
    def testDecay(self):
      num_training_steps = 1000
      initial_lr = 1.0
      for step in range(0, 1500, 250):
        decayed_lr = tf.compat.v1.train.cosine_decay(initial_lr, step,
                                                      num_training_steps)
        expected = self.np_cosine_decay(step, num_training_steps)
        self.assertAllClose(self.evaluate(decayed_lr), expected, 1e-6)
  
    def testAlpha(self):
      num_training_steps = 1000
      initial_lr = 1.0
      alpha = 0.1
      for step in range(0, 1500, 250):
        decayed_lr = tf.compat.v1.train.cosine_decay(initial_lr, step,
                                                      num_training_steps, alpha)
        expected = self.np_cosine_decay(step, num_training_steps, alpha)
        self.assertAllClose(self.evaluate(decayed_lr), expected, 1e-6)
  
  
  @combinations.generate(combinations.combine(mode=["graph", "eager"]))
  class CosineDecayRestartsTest(keras_parameterized.TestCase):
  
    def np_cosine_decay_restarts(self, step, decay_steps, t_mul=2.0, m_mul=1.0,
                                 alpha=0.0):
      fac = 1.0
      while step >= decay_steps:
        step -= decay_steps
        decay_steps *= t_mul
        fac *= m_mul
  
      completed_fraction = step / decay_steps
      decay = fac * 0.5 * (1.0 + math.cos(math.pi * completed_fraction))
      return (1.0 - alpha) * decay + alpha
  
    def testDecay(self):
      num_training_steps = 1000
      initial_lr = 1.0
      for step in range(0, 1500, 250):
        decayed_lr = tf.compat.v1.train.cosine_decay_restarts(
            initial_lr, step, num_training_steps)
        expected = self.np_cosine_decay_restarts(step, num_training_steps)
        self.assertAllClose(self.evaluate(decayed_lr), expected, 1e-6)
  
    def testAlpha(self):
      num_training_steps = 1000
      initial_lr = 1.0
      alpha = 0.1
      for step in range(0, 1500, 250):
        decayed_lr = tf.compat.v1.train.cosine_decay_restarts(
            initial_lr, step, num_training_steps, alpha=alpha)
        expected = self.np_cosine_decay_restarts(
            step, num_training_steps, alpha=alpha)
        self.assertAllClose(self.evaluate(decayed_lr), expected, 1e-6)
  
    def testMMul(self):
      num_training_steps = 1000
      initial_lr = 1.0
      m_mul = 0.9
      for step in range(0, 1500, 250):
        decayed_lr = tf.compat.v1.train.cosine_decay_restarts(
            initial_lr, step, num_training_steps, m_mul=m_mul)
        expected = self.np_cosine_decay_restarts(
            step, num_training_steps, m_mul=m_mul)
        self.assertAllClose(self.evaluate(decayed_lr), expected, 1e-6)
  
    def testTMul(self):
      num_training_steps = 1000
      initial_lr = 1.0
      t_mul = 1.0
      for step in range(0, 1500, 250):
        decayed_lr = tf.compat.v1.train.cosine_decay_restarts(
            initial_lr, step, num_training_steps, t_mul=t_mul)
        expected = self.np_cosine_decay_restarts(
            step, num_training_steps, t_mul=t_mul)
        self.assertAllClose(self.evaluate(decayed_lr), expected, 1e-6)
  
  
  @combinations.generate(combinations.combine(mode=["graph", "eager"]))
  class LinearCosineDecayTest(keras_parameterized.TestCase):
  
    def np_linear_cosine_decay(self,
                               step,
                               decay_steps,
                               alpha=0.0,
                               beta=0.001,
                               num_periods=0.5):
      step = min(step, decay_steps)
      linear_decayed = float(decay_steps - step) / decay_steps
      fraction = 2.0 * num_periods * step / float(decay_steps)
      cosine_decayed = 0.5 * (1.0 + math.cos(math.pi * fraction))
      return (alpha + linear_decayed) * cosine_decayed + beta
  
    def testDefaultDecay(self):
      num_training_steps = 1000
      initial_lr = 1.0
      for step in range(0, 1500, 250):
        decayed_lr = tf.compat.v1.train.linear_cosine_decay(
            initial_lr, step, num_training_steps)
        expected = self.np_linear_cosine_decay(step, num_training_steps)
        self.assertAllClose(self.evaluate(decayed_lr), expected, 1e-6)
  
    def testNonDefaultDecay(self):
      num_training_steps = 1000
      initial_lr = 1.0
      for step in range(0, 1500, 250):
        decayed_lr = tf.compat.v1.train.linear_cosine_decay(
            initial_lr,
            step,
            num_training_steps,
            alpha=0.1,
            beta=1e-4,
            num_periods=5)
        expected = self.np_linear_cosine_decay(
            step, num_training_steps, alpha=0.1, beta=1e-4, num_periods=5)
        self.assertAllClose(self.evaluate(decayed_lr), expected, 1e-6)
  
  
  @combinations.generate(combinations.combine(mode=["graph", "eager"]))
  class NoisyLinearCosineDecayTest(keras_parameterized.TestCase):
  
    def testDefaultNoisyLinearCosine(self):
      num_training_steps = 1000
      initial_lr = 1.0
      for step in range(0, 1500, 250):
        # No numerical check because of noise
        decayed_lr = tf.compat.v1.train.noisy_linear_cosine_decay(
            initial_lr, step, num_training_steps)
        # Cannot be deterministically tested
        self.evaluate(decayed_lr)
  
    def testNonDefaultNoisyLinearCosine(self):
      num_training_steps = 1000
      initial_lr = 1.0
      for step in range(0, 1500, 250):
        # No numerical check because of noise
        decayed_lr = tf.compat.v1.train.noisy_linear_cosine_decay(
            initial_lr,
            step,
            num_training_steps,
            initial_variance=0.5,
            variance_decay=0.1,
            alpha=0.1,
            beta=1e-4,
            num_periods=5)
        # Cannot be deterministically tested
        self.evaluate(decayed_lr)
  
  
  if __name__ == "__main__":
    tf.test.main()
  