text: # Copyright 2018 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 AdaMax optimizer."""
  
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  import numpy as np
  
  from tensorflow.compiler.tests import xla_test
  from tensorflow.contrib.opt.python.training import adamax
  from tensorflow.python.framework import constant_op
  from tensorflow.python.framework import ops
  from tensorflow.python.ops import resource_variable_ops
  from tensorflow.python.ops import variable_scope
  from tensorflow.python.ops import variables
  from tensorflow.python.platform import test
  
  
  def adamax_update_numpy(param,
                          g_t,
                          t,
                          m,
                          v,
                          alpha=0.001,
                          beta1=0.9,
                          beta2=0.999,
                          epsilon=1e-8):
    m_t = beta1 * m + (1 - beta1) * g_t
    v_t = np.maximum(beta2 * v, np.abs(g_t))
    param_t = param - (alpha / (1 - beta1**t)) * (m_t / (v_t + epsilon))
    return param_t, m_t, v_t
  
  
  class AdaMaxOptimizerTest(xla_test.XLATestCase):
  
    def testBasic(self):
      for i, dtype in enumerate(self.float_types):
        with self.cached_session(), self.test_scope():
          variable_scope.get_variable_scope().set_use_resource(True)
          # Initialize variables for numpy implementation.
          m0, v0, m1, v1 = 0.0, 0.0, 0.0, 0.0
          var0_np = np.array([1.0, 2.0], dtype=dtype)
          grads0_np = np.array([0.1, 0.1], dtype=dtype)
          var1_np = np.array([3.0, 4.0], dtype=dtype)
          grads1_np = np.array([0.01, 0.01], dtype=dtype)
  
          var0 = resource_variable_ops.ResourceVariable(
              var0_np, name="var0_%d" % i)
          var1 = resource_variable_ops.ResourceVariable(
              var1_np, name="var1_%d" % i)
          grads0 = constant_op.constant(grads0_np)
          grads1 = constant_op.constant(grads1_np)
  
          opt = adamax.AdaMaxOptimizer()
          update = opt.apply_gradients(zip([grads0, grads1], [var0, var1]))
          opt_variables = opt.variables()
          beta1_power = opt._get_beta_accumulators()
          self.assertTrue(beta1_power is not None)
          self.assertIn(beta1_power, opt_variables)
  
          with ops.Graph().as_default():
            # Shouldn't return non-slot variables from other graphs.
            self.assertEqual(0, len(opt.variables()))
  
          variables.global_variables_initializer().run()
          # Fetch params to validate initial values
          self.assertAllClose([1.0, 2.0], self.evaluate(var0))
          self.assertAllClose([3.0, 4.0], self.evaluate(var1))
  
          beta1_power = opt._get_beta_accumulators()
  
          # Run 3 steps of AdaMax
          for t in range(1, 4):
            update.run()
  
            self.assertAllCloseAccordingToType(0.9**(t + 1),
                                               self.evaluate(beta1_power))
  
            var0_np, m0, v0 = adamax_update_numpy(var0_np, grads0_np, t, m0, v0)
            var1_np, m1, v1 = adamax_update_numpy(var1_np, grads1_np, t, m1, v1)
  
            # Validate updated params
            self.assertAllCloseAccordingToType(
                var0_np, self.evaluate(var0), rtol=1e-2)
            self.assertAllCloseAccordingToType(
                var1_np, self.evaluate(var1), rtol=1e-2)
            self.assertEqual("var0_%d/AdaMax:0" % (i,),
                             opt.get_slot(var=var0, name="m").name)
  
    def testTensorLearningRate(self):
      for dtype in self.float_types:
        with self.cached_session(), self.test_scope():
          variable_scope.get_variable_scope().set_use_resource(True)
          # Initialize variables for numpy implementation.
          m0, v0, m1, v1 = 0.0, 0.0, 0.0, 0.0
          var0_np = np.array([1.0, 2.0], dtype=dtype)
          grads0_np = np.array([0.1, 0.1], dtype=dtype)
          var1_np = np.array([3.0, 4.0], dtype=dtype)
          grads1_np = np.array([0.01, 0.01], dtype=dtype)
  
          var0 = resource_variable_ops.ResourceVariable(var0_np)
          var1 = resource_variable_ops.ResourceVariable(var1_np)
          grads0 = constant_op.constant(grads0_np)
          grads1 = constant_op.constant(grads1_np)
          opt = adamax.AdaMaxOptimizer(constant_op.constant(0.001))
          update = opt.apply_gradients(zip([grads0, grads1], [var0, var1]))
          variables.global_variables_initializer().run()
  
          # Fetch params to validate initial values
          self.assertAllClose([1.0, 2.0], self.evaluate(var0))
          self.assertAllClose([3.0, 4.0], self.evaluate(var1))
  
          beta1_power = opt._get_beta_accumulators()
  
          # Run 3 steps of AdaMax
          for t in range(1, 4):
            self.assertAllCloseAccordingToType(0.9**t, self.evaluate(beta1_power))
            update.run()
  
            var0_np, m0, v0 = adamax_update_numpy(var0_np, grads0_np, t, m0, v0)
            var1_np, m1, v1 = adamax_update_numpy(var1_np, grads1_np, t, m1, v1)
  
            # Validate updated params
            self.assertAllCloseAccordingToType(var0_np, self.evaluate(var0))
            self.assertAllCloseAccordingToType(var1_np, self.evaluate(var1))
  
  
  if __name__ == "__main__":
    test.main()
  