text: # Copyright 2016 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 tests for AdagradDA operations."""
  
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  import numpy as np
  
  from tensorflow.python.framework import constant_op
  from tensorflow.python.framework import dtypes
  from tensorflow.python.framework import ops
  from tensorflow.python.framework import test_util
  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
  from tensorflow.python.training import adagrad_da
  
  
  class AdagradDAOptimizerTest(test.TestCase):
  
    def doTestAdagradDAwithoutRegularizationBasic1(self, use_resource=False):
      for dtype in [dtypes.float64, dtypes.float32]:
        with ops.Graph().as_default(), self.cached_session():
          global_step = variables.Variable(0, dtype=dtypes.int64)
          if use_resource:
            var0 = resource_variable_ops.ResourceVariable([0.0, 0.0], dtype=dtype)
            var1 = resource_variable_ops.ResourceVariable([0.0, 0.0], dtype=dtype)
          else:
            var0 = variables.Variable([0.0, 0.0], dtype=dtype)
            var1 = variables.Variable([0.0, 0.0], dtype=dtype)
          grads0 = constant_op.constant([0.1, 0.2], dtype=dtype)
          grads1 = constant_op.constant([0.01, 0.02], dtype=dtype)
          opt = adagrad_da.AdagradDAOptimizer(
              3.0,
              global_step,
              initial_gradient_squared_accumulator_value=0.1,
              l1_regularization_strength=0.0,
              l2_regularization_strength=0.0)
          update = opt.apply_gradients(
              zip([grads0, grads1], [var0, var1]), global_step=global_step)
          self.evaluate(variables.global_variables_initializer())
  
          v0_val, v1_val = self.evaluate([var0, var1])
          self.assertAllClose([0.0, 0.0], v0_val)
          self.assertAllClose([0.0, 0.0], v1_val)
  
          # Run a step of AdagradDA
          update.run()
  
          v0_val, v1_val = self.evaluate([var0, var1])
          # Let g be the gradient accumulator, gg be the gradient squared
          # accumulator, T be the global step, lr be the learning rate,
          # and k the initial gradient squared accumulator value.
          # w = \dfrac{sign(-g)*lr*|g - l1*T|_{+}}{l2*T*lr + \sqrt{k+gg})}
          # For -0.1*3.0*(0.1 - 0)/(0 + sqrt(0.1 + 0.1*0.1)) = -0.904534
          # similarly for others.
          self.assertAllCloseAccordingToType(
              np.array([-0.904534, -1.603567]), v0_val)
          self.assertAllCloseAccordingToType(
              np.array([-0.094821, -0.189358]), v1_val)
  
    def testAdagradDAWithoutRegularizationBasic1(self):
      self.doTestAdagradDAwithoutRegularizationBasic1()
  
    def testResourceAdagradDAWithoutRegularizationBasic1(self):
      self.doTestAdagradDAwithoutRegularizationBasic1(use_resource=True)
  
    @test_util.run_v1_only("loss needs to be callable in v2")
    def testMinimizeSparseResourceVariable(self):
      for dtype in [dtypes.float32, dtypes.float64]:
        with self.cached_session():
          var0 = resource_variable_ops.ResourceVariable([[1.0, 2.0]], dtype=dtype)
          global_step = resource_variable_ops.ResourceVariable(
              0, dtype=dtypes.int64)
          x = constant_op.constant([[4.0], [5.0]], dtype=dtype)
          pred = math_ops.matmul(embedding_ops.embedding_lookup([var0], [0]), x)
          loss = pred * pred
          sgd_op = adagrad_da.AdagradDAOptimizer(
              1.0, global_step).minimize(loss)
          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
          sgd_op.run()
          # Validate updated params
          self.assertAllCloseAccordingToType([[-1, -1]],
                                             self.evaluate(var0),
                                             rtol=0.01)
  
    def testAdagradDAwithoutRegularizationBasic2(self):
      for dtype in [dtypes.float64, dtypes.float32]:
        with ops.Graph().as_default(), self.cached_session():
          global_step = variables.Variable(0, dtype=dtypes.int64)
          var0 = variables.Variable([1.0, 2.0], dtype=dtype)
          var1 = variables.Variable([4.0, 3.0], dtype=dtype)
          grads0 = constant_op.constant([0.1, 0.2], dtype=dtype)
          grads1 = constant_op.constant([0.01, 0.02], dtype=dtype)
  
          opt = adagrad_da.AdagradDAOptimizer(
              3.0,
              global_step,
              initial_gradient_squared_accumulator_value=0.1,
              l1_regularization_strength=0.0,
              l2_regularization_strength=0.0)
          update = opt.apply_gradients(
              zip([grads0, grads1], [var0, var1]), global_step=global_step)
          self.evaluate(variables.global_variables_initializer())
  
          v0_val, v1_val = self.evaluate([var0, var1])
          self.assertAllCloseAccordingToType([1.0, 2.0], v0_val)
          self.assertAllCloseAccordingToType([4.0, 3.0], v1_val)
  
          # Run a step of AdagradDA
          update.run()
  
          v0_val, v1_val = self.evaluate([var0, var1])
          self.assertAllCloseAccordingToType(
              np.array([-0.904534, -1.603567]), v0_val)
          self.assertAllCloseAccordingToType(
              np.array([-0.094821, -0.189358]), v1_val)
  
    def testAdagradDAWithL1(self):
      for dtype in [dtypes.float64, dtypes.float32]:
        with ops.Graph().as_default(), self.cached_session():
          global_step = variables.Variable(0, dtype=dtypes.int64)
          var0 = variables.Variable([1.0, 2.0], dtype=dtype)
          var1 = variables.Variable([4.0, 3.0], dtype=dtype)
          grads0 = constant_op.constant([0.1, 0.2], dtype=dtype)
          grads1 = constant_op.constant([0.01, 0.02], dtype=dtype)
  
          opt = adagrad_da.AdagradDAOptimizer(
              3.0,
              global_step,
              initial_gradient_squared_accumulator_value=0.1,
              l1_regularization_strength=0.001,
              l2_regularization_strength=0.0)
          update = opt.apply_gradients(
              zip([grads0, grads1], [var0, var1]), global_step=global_step)
          self.evaluate(variables.global_variables_initializer())
  
          v0_val, v1_val = self.evaluate([var0, var1])
          self.assertAllCloseAccordingToType([1.0, 2.0], v0_val)
          self.assertAllCloseAccordingToType([4.0, 3.0], v1_val)
  
          # Run a step of AdagradDA
          update.run()
  
          v0_val, v1_val = self.evaluate([var0, var1])
          self.assertAllCloseAccordingToType(
              np.array([-0.895489, -1.59555]), v0_val)
          self.assertAllCloseAccordingToType(
              np.array([-0.085339, -0.17989]), v1_val)
  
    def testAdagradDAWithL1_L2(self):
      for dtype in [dtypes.float64, dtypes.float32]:
        with ops.Graph().as_default(), self.cached_session():
          global_step = variables.Variable(0, dtype=dtypes.int64)
          var0 = variables.Variable([1.0, 2.0], dtype=dtype)
          var1 = variables.Variable([4.0, 3.0], dtype=dtype)
          grads0 = constant_op.constant([0.1, 0.2], dtype=dtype)
          grads1 = constant_op.constant([0.01, 0.02], dtype=dtype)
  
          opt = adagrad_da.AdagradDAOptimizer(
              3.0,
              global_step,
              initial_gradient_squared_accumulator_value=0.1,
              l1_regularization_strength=0.001,
              l2_regularization_strength=2.0)
          update = opt.apply_gradients(
              zip([grads0, grads1], [var0, var1]), global_step=global_step)
          self.evaluate(variables.global_variables_initializer())
  
          v0_val, v1_val = self.evaluate([var0, var1])
          self.assertAllCloseAccordingToType([1.0, 2.0], v0_val)
          self.assertAllCloseAccordingToType([4.0, 3.0], v1_val)
  
          # Run a step of AdagradDA
          update.run()
  
          v0_val, v1_val = self.evaluate([var0, var1])
          self.assertAllCloseAccordingToType(
              np.array([-0.046907, -0.093659]), v0_val)
          self.assertAllCloseAccordingToType(
              np.array([-0.004275, -0.009023]), v1_val)
  
  
  if __name__ == "__main__":
    test.main()
  