code: # 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 Adadelta Optimizer."""
  
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  import numpy as np
  
  from tensorflow.python.eager import context
  from tensorflow.python.framework import constant_op
  from tensorflow.python.framework import dtypes
  from tensorflow.python.framework import test_util
  from tensorflow.python.keras.optimizer_v2 import adadelta
  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
  
  _DATA_TYPES = [dtypes.half, dtypes.float32, dtypes.float64]
  # TODO(b/143684500): Eigen to support complex sqrt
  if (not test_util.IsBuiltWithNvcc() and not test.is_built_with_rocm()):
    _DATA_TYPES += [dtypes.complex64, dtypes.complex128]
  
  
  class AdadeltaOptimizerTest(test.TestCase):
  
    def doTestBasic(self, use_resource=False, use_callable_params=False):
      num_updates = 4  # number of ADADELTA steps to perform
      for dtype in _DATA_TYPES:
        for grad in [0.2, 0.1, 0.01]:
          for lr in [1.0, 0.5, 0.1]:
            var0_init = [1.0, 2.0]
            var1_init = [3.0, 4.0]
            if use_resource:
              var0 = resource_variable_ops.ResourceVariable(
                  var0_init, dtype=dtype)
              var1 = resource_variable_ops.ResourceVariable(
                  var1_init, dtype=dtype)
            else:
              var0 = variables.Variable(var0_init, dtype=dtype)
              var1 = variables.Variable(var1_init, dtype=dtype)
  
            grads = constant_op.constant([grad, grad], dtype=dtype)
  
            accum = 0.0
            accum_update = 0.0
  
            # ADADELTA gradient optimizer
            rho = 0.95
            epsilon = 1e-8
            if use_callable_params:
              adadelta_opt = adadelta.Adadelta(
                  learning_rate=lambda: lr,  # pylint: disable=cell-var-from-loop
                  rho=lambda: rho,  # pylint: disable=cell-var-from-loop
                  epsilon=epsilon)  # pylint: disable=cell-var-from-loop
            else:
              adadelta_opt = adadelta.Adadelta(
                  learning_rate=lr, rho=rho, epsilon=epsilon)
            if not context.executing_eagerly():
              adadelta_update = adadelta_opt.apply_gradients(
                  zip([grads, grads], [var0, var1]))
              self.evaluate(variables.global_variables_initializer())
  
              # Assign slots
              slot = [None] * 2
              slot_update = [None] * 2
              slot[0] = adadelta_opt.get_slot(var0, "accum_grad")
              self.assertEqual(slot[0].shape, var0.shape)
  
              slot_update[0] = adadelta_opt.get_slot(var0, "accum_var")
              self.assertEqual(slot_update[0].shape, var0.shape)
  
              slot[1] = adadelta_opt.get_slot(var1, "accum_grad")
              self.assertEqual(slot[1].shape, var1.shape)
  
              slot_update[1] = adadelta_opt.get_slot(var1, "accum_var")
              self.assertEqual(slot_update[1].shape, var1.shape)
  
            # Fetch params to validate initial values
            self.assertAllClose(var0_init, self.evaluate(var0))
            self.assertAllClose(var1_init, self.evaluate(var1))
  
            update = [None] * num_updates
            tot_update = 0
            for step in range(num_updates):
              # Run adadelta update for comparison
              if not context.executing_eagerly():
                self.evaluate(adadelta_update)
              else:
                adadelta_opt.apply_gradients(zip([grads, grads], [var0, var1]))
  
              # Perform initial update without previous accum values
              accum = accum * rho + (grad**2) * (1 - rho)
              update[step] = (
                  np.sqrt(accum_update + epsilon) *
                  (1. / np.sqrt(accum + epsilon)) * grad)
              accum_update = (
                  accum_update * rho + (update[step]**2) * (1.0 - rho))
              tot_update += update[step] * lr
  
              if not context.executing_eagerly():
                # Check that the accumulators have been updated
                # TODO(lxuechen): This is hard to test in eager mode
                for slot_idx in range(2):
                  self.assertAllCloseAccordingToType(
                      np.array([accum, accum], dtype=dtype.as_numpy_dtype(0)),
                      self.evaluate(slot[slot_idx]),
                      rtol=1e-5)
  
                  self.assertAllCloseAccordingToType(
                      np.array(
                          [accum_update, accum_update],
                          dtype=dtype.as_numpy_dtype(0)),
                      self.evaluate(slot_update[slot_idx]),
                      rtol=1e-5)
  
                # Check that the parameters have been updated
                self.assertAllCloseAccordingToType(
                    np.array(
                        [var0_init[0] - tot_update, var0_init[1] - tot_update],
                        dtype=dtype.as_numpy_dtype(0)),
                    self.evaluate(var0),
                    rtol=1e-5)
  
                self.assertAllCloseAccordingToType(
                    np.array(
                        [var1_init[0] - tot_update, var1_init[1] - tot_update],
                        dtype=dtype.as_numpy_dtype(0)),
                    self.evaluate(var1),
                    rtol=1e-5)
  
    @test_util.run_in_graph_and_eager_modes(reset_test=True)
    def testResourceBasic(self):
      self.doTestBasic(use_resource=True)
  
    def testBasicCallableParams(self):
      with context.eager_mode():
        self.doTestBasic(use_resource=True, use_callable_params=True)
  
    @test_util.run_deprecated_v1
    def testMinimizeSparseResourceVariable(self):
      for dtype in _DATA_TYPES:
        var0 = resource_variable_ops.ResourceVariable([[1.0, 2.0]], dtype=dtype)
        x = constant_op.constant([[4.0], [5.0]], dtype=dtype)
  
        def loss():
          pred = math_ops.matmul(embedding_ops.embedding_lookup([var0], [0]), x)  # pylint: disable=cell-var-from-loop
          return pred * pred
  
        sgd_op = adadelta.Adadelta(1.0, 1.0, 1.0).minimize(loss, var_list=[var0])
        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
        self.evaluate(sgd_op)
        # Validate updated params
        self.assertAllCloseAccordingToType([[-111, -138]], self.evaluate(var0))
  
    def testConstructAdadeltaWithLR(self):
      opt = adadelta.Adadelta(lr=1.0, rho=0.9, epsilon=1.)
      opt_2 = adadelta.Adadelta(learning_rate=0.1, rho=0.9, epsilon=1., lr=1.0)
      opt_3 = adadelta.Adadelta(learning_rate=0.1, rho=0.9, epsilon=1.)
      self.assertIsInstance(opt.lr, variables.Variable)
      self.assertIsInstance(opt_2.lr, variables.Variable)
      self.assertIsInstance(opt_3.lr, variables.Variable)
  
      self.evaluate(variables.global_variables_initializer())
      self.assertAllClose(self.evaluate(opt.lr), (1.0))
      self.assertAllClose(self.evaluate(opt_2.lr), (1.0))
      self.assertAllClose(self.evaluate(opt_3.lr), (0.1))
  
    def testConstructAdadeltaWithEpsilonValues(self):
      opt = adadelta.Adadelta(epsilon=None)
      self.assertEqual(opt.epsilon, 1e-7)
  
      opt = adadelta.Adadelta(epsilon=1e-8)
      self.assertEqual(opt.epsilon, 1e-8)
  
  
  if __name__ == "__main__":
    test.main()
  
apis: tensorflow.python.platform.test.is_built_with_rocm tensorflow.python.eager.context.eager_mode tensorflow.python.framework.test_util.run_in_graph_and_eager_modes tensorflow.python.keras.optimizer_v2.adadelta.Adadelta tensorflow.python.ops.embedding_ops.embedding_lookup tensorflow.python.ops.variables.global_variables_initializer tensorflow.python.framework.test_util.IsBuiltWithNvcc tensorflow.python.platform.test.main tensorflow.python.ops.resource_variable_ops.ResourceVariable numpy.sqrt tensorflow.python.eager.context.executing_eagerly tensorflow.python.ops.variables.Variable tensorflow.python.framework.constant_op.constant