diff --git a/examples/mnist/lenet_adadelta_solver.prototxt b/examples/mnist/lenet_adadelta_solver.prototxt
index b77b451d56a..776d1e06139 100644
--- a/examples/mnist/lenet_adadelta_solver.prototxt
+++ b/examples/mnist/lenet_adadelta_solver.prototxt
@@ -7,6 +7,8 @@ test_iter: 100
 # Carry out testing every 500 training iterations.
 test_interval: 500
 # The base learning rate, momentum and the weight decay of the network.
+base_lr: 1.0
+lr_policy: "fixed"
 momentum: 0.95
 weight_decay: 0.0005
 # Display every 100 iterations
diff --git a/examples/mnist/mnist_autoencoder_solver_adadelta.prototxt b/examples/mnist/mnist_autoencoder_solver_adadelta.prototxt
index 4e43468a71f..065647df31b 100644
--- a/examples/mnist/mnist_autoencoder_solver_adadelta.prototxt
+++ b/examples/mnist/mnist_autoencoder_solver_adadelta.prototxt
@@ -5,6 +5,8 @@ test_state: { stage: 'test-on-test' }
 test_iter: 100
 test_interval: 500
 test_compute_loss: true
+base_lr: 1.0
+lr_policy: "fixed"
 momentum: 0.95
 delta: 1e-8
 display: 100
diff --git a/include/caffe/solver.hpp b/include/caffe/solver.hpp
index 495cd4f159e..5fefd01e549 100644
--- a/include/caffe/solver.hpp
+++ b/include/caffe/solver.hpp
@@ -82,12 +82,12 @@ class SGDSolver : public Solver<Dtype> {
   const vector<shared_ptr<Blob<Dtype> > >& history() { return history_; }
 
  protected:
+  void PreSolve();
   Dtype GetLearningRate();
   virtual void ApplyUpdate();
   virtual void Normalize(int param_id);
   virtual void Regularize(int param_id);
   virtual void ComputeUpdateValue(int param_id, Dtype rate);
-  virtual void PreSolve();
   virtual void ClipGradients();
   virtual void SnapshotSolverState(const string& model_filename);
   virtual void SnapshotSolverStateToBinaryProto(const string& model_filename);
@@ -162,19 +162,13 @@ template <typename Dtype>
 class AdaDeltaSolver : public SGDSolver<Dtype> {
  public:
   explicit AdaDeltaSolver(const SolverParameter& param)
-      : SGDSolver<Dtype>(param) { PreSolve(); constructor_sanity_check(); }
+      : SGDSolver<Dtype>(param) { AdaDeltaPreSolve(); }
   explicit AdaDeltaSolver(const string& param_file)
-      : SGDSolver<Dtype>(param_file) { PreSolve(); constructor_sanity_check(); }
+      : SGDSolver<Dtype>(param_file) { AdaDeltaPreSolve(); }
 
  protected:
-  virtual void PreSolve();
-  virtual void ComputeUpdateValue();
-  void constructor_sanity_check() {
-    CHECK_EQ(0, this->param_.base_lr())
-        << "Learning rate cannot be used with AdaDelta.";
-    CHECK_EQ("", this->param_.lr_policy())
-        << "Learning rate policy cannot be applied to AdaDelta.";
-  }
+  void AdaDeltaPreSolve();
+  virtual void ComputeUpdateValue(int param_id, Dtype rate);
 
   DISABLE_COPY_AND_ASSIGN(AdaDeltaSolver);
 };
diff --git a/src/caffe/solver.cpp b/src/caffe/solver.cpp
index 34a290ffe3d..78902ca0ebc 100644
--- a/src/caffe/solver.cpp
+++ b/src/caffe/solver.cpp
@@ -935,10 +935,10 @@ void RMSPropSolver<Dtype>::ComputeUpdateValue(int param_id, Dtype rate) {
 }
 
 template <typename Dtype>
-void AdaDeltaSolver<Dtype>::PreSolve() {
+void AdaDeltaSolver<Dtype>::AdaDeltaPreSolve() {
   // Add the extra history entries for AdaDelta after those from
   // SGDSolver::PreSolve
-  const vector<shared_ptr<Blob<Dtype> > >& net_params = this->net_->params();
+  const vector<Blob<Dtype>*>& net_params = this->net_->learnable_params();
   for (int i = 0; i < net_params.size(); ++i) {
         const vector<int>& shape = net_params[i]->shape();
         this->history_.push_back(
@@ -947,172 +947,134 @@ void AdaDeltaSolver<Dtype>::PreSolve() {
 }
 
 template <typename Dtype>
-void AdaDeltaSolver<Dtype>::ComputeUpdateValue() {
-  const vector<shared_ptr<Blob<Dtype> > >& net_params = this->net_->params();
-  const vector<float>& net_params_weight_decay =
-          this->net_->params_weight_decay();
+void AdaDeltaSolver<Dtype>::ComputeUpdateValue(int param_id, Dtype rate) {
+  const vector<Blob<Dtype>*>& net_params = this->net_->learnable_params();
+  const vector<float>& net_params_lr = this->net_->params_lr();
   Dtype delta = this->param_.delta();
   Dtype momentum = this->param_.momentum();
-  Dtype weight_decay = this->param_.weight_decay();
-  string regularization_type = this->param_.regularization_type();
+  Dtype local_rate = rate * net_params_lr[param_id];
   size_t update_history_offset = net_params.size();
   switch (Caffe::mode()) {
-  case Caffe::CPU:
-    for (int param_id = 0; param_id < net_params.size(); ++param_id) {
-      Dtype local_decay = weight_decay * net_params_weight_decay[param_id];
-
-      if (local_decay) {
-        if (regularization_type == "L2") {
-          // add weight decay
-          caffe_axpy(net_params[param_id]->count(),
-              local_decay,
-              net_params[param_id]->cpu_data(),
-              net_params[param_id]->mutable_cpu_diff());
-        } else if (regularization_type == "L1") {
-          caffe_cpu_sign(net_params[param_id]->count(),
-              net_params[param_id]->cpu_data(),
-              this->temp_[param_id]->mutable_cpu_data());
-          caffe_axpy(net_params[param_id]->count(),
-              local_decay,
-              this->temp_[param_id]->cpu_data(),
-              net_params[param_id]->mutable_cpu_diff());
-        } else {
-          LOG(FATAL) << "Unknown regularization type: " << regularization_type;
-        }
-      }
+  case Caffe::CPU: {
+    // compute square of gradient in update
+    caffe_powx(net_params[param_id]->count(),
+        net_params[param_id]->cpu_diff(), Dtype(2),
+        this->update_[param_id]->mutable_cpu_data());
 
-      // compute square of gradient in update
-      caffe_powx(net_params[param_id]->count(),
-          net_params[param_id]->cpu_diff(), Dtype(2),
-          this->update_[param_id]->mutable_cpu_data());
-
-      // update history of gradients
-      caffe_cpu_axpby(net_params[param_id]->count(), Dtype(1) - momentum,
-          this->update_[param_id]->cpu_data(), momentum,
-          this->history_[param_id]->mutable_cpu_data());
-
-      // add delta to history to guard against dividing by zero later
-      caffe_set(net_params[param_id]->count(), delta,
-          this->temp_[param_id]->mutable_cpu_data());
-
-      caffe_add(net_params[param_id]->count(),
-          this->temp_[param_id]->cpu_data(),
-          this->history_[update_history_offset + param_id]->cpu_data(),
-          this->update_[param_id]->mutable_cpu_data());
-
-      caffe_add(net_params[param_id]->count(),
-          this->temp_[param_id]->cpu_data(),
-          this->history_[param_id]->cpu_data(),
-          this->temp_[param_id]->mutable_cpu_data());
-
-      // divide history of updates by history of gradients
-      caffe_div(net_params[param_id]->count(),
-          this->update_[param_id]->cpu_data(),
-          this->temp_[param_id]->cpu_data(),
-          this->update_[param_id]->mutable_cpu_data());
-
-      // jointly compute the RMS of both for update and gradient history
-      caffe_powx(net_params[param_id]->count(),
-          this->update_[param_id]->cpu_data(), Dtype(0.5),
-          this->update_[param_id]->mutable_cpu_data());
-
-      // compute the update
-      caffe_mul(net_params[param_id]->count(),
-          net_params[param_id]->cpu_diff(),
-          this->update_[param_id]->cpu_data(),
-          net_params[param_id]->mutable_cpu_diff());
-
-      // compute square of update
-      caffe_powx(net_params[param_id]->count(),
-          net_params[param_id]->cpu_diff(), Dtype(2),
-          this->update_[param_id]->mutable_cpu_data());
-
-      // update history of updates
-      caffe_cpu_axpby(net_params[param_id]->count(), Dtype(1) - momentum,
-          this->update_[param_id]->cpu_data(), momentum,
-          this->history_[update_history_offset + param_id]->mutable_cpu_data());
-    }
+    // update history of gradients
+    caffe_cpu_axpby(net_params[param_id]->count(), Dtype(1) - momentum,
+        this->update_[param_id]->cpu_data(), momentum,
+        this->history_[param_id]->mutable_cpu_data());
+
+    // add delta to history to guard against dividing by zero later
+    caffe_set(net_params[param_id]->count(), delta,
+        this->temp_[param_id]->mutable_cpu_data());
+
+    caffe_add(net_params[param_id]->count(),
+        this->temp_[param_id]->cpu_data(),
+        this->history_[update_history_offset + param_id]->cpu_data(),
+        this->update_[param_id]->mutable_cpu_data());
+
+    caffe_add(net_params[param_id]->count(),
+        this->temp_[param_id]->cpu_data(),
+        this->history_[param_id]->cpu_data(),
+        this->temp_[param_id]->mutable_cpu_data());
+
+    // divide history of updates by history of gradients
+    caffe_div(net_params[param_id]->count(),
+        this->update_[param_id]->cpu_data(),
+        this->temp_[param_id]->cpu_data(),
+        this->update_[param_id]->mutable_cpu_data());
+
+    // jointly compute the RMS of both for update and gradient history
+    caffe_powx(net_params[param_id]->count(),
+        this->update_[param_id]->cpu_data(), Dtype(0.5),
+        this->update_[param_id]->mutable_cpu_data());
+
+    // compute the update
+    caffe_mul(net_params[param_id]->count(),
+        net_params[param_id]->cpu_diff(),
+        this->update_[param_id]->cpu_data(),
+        net_params[param_id]->mutable_cpu_diff());
+
+    // compute square of update
+    caffe_powx(net_params[param_id]->count(),
+        net_params[param_id]->cpu_diff(), Dtype(2),
+        this->update_[param_id]->mutable_cpu_data());
+
+    // update history of updates
+    caffe_cpu_axpby(net_params[param_id]->count(), Dtype(1) - momentum,
+        this->update_[param_id]->cpu_data(), momentum,
+        this->history_[update_history_offset + param_id]->mutable_cpu_data());
+
+    // apply learning rate
+    caffe_cpu_scale(net_params[param_id]->count(), local_rate,
+        net_params[param_id]->cpu_diff(),
+        net_params[param_id]->mutable_cpu_diff());
     break;
-  case Caffe::GPU:
+  }
+  case Caffe::GPU: {
 #ifndef CPU_ONLY
-    for (int param_id = 0; param_id < net_params.size(); ++param_id) {
-      Dtype local_decay = weight_decay * net_params_weight_decay[param_id];
-
-      if (local_decay) {
-        if (regularization_type == "L2") {
-          // add weight decay
-          caffe_gpu_axpy(net_params[param_id]->count(),
-              local_decay,
-              net_params[param_id]->gpu_data(),
-              net_params[param_id]->mutable_gpu_diff());
-        } else if (regularization_type == "L1") {
-          caffe_gpu_sign(net_params[param_id]->count(),
-              net_params[param_id]->gpu_data(),
-              this->temp_[param_id]->mutable_gpu_data());
-          caffe_gpu_axpy(net_params[param_id]->count(),
-              local_decay,
-              this->temp_[param_id]->gpu_data(),
-              net_params[param_id]->mutable_gpu_diff());
-        } else {
-          LOG(FATAL) << "Unknown regularization type: " << regularization_type;
-        }
-      }
+    // compute square of gradient in update
+    caffe_gpu_powx(net_params[param_id]->count(),
+        net_params[param_id]->gpu_diff(), Dtype(2),
+        this->update_[param_id]->mutable_gpu_data());
 
-      // compute square of gradient in update
-      caffe_gpu_powx(net_params[param_id]->count(),
-          net_params[param_id]->gpu_diff(), Dtype(2),
-          this->update_[param_id]->mutable_gpu_data());
-
-      // update history of gradients
-      caffe_gpu_axpby(net_params[param_id]->count(), Dtype(1) - momentum,
-          this->update_[param_id]->gpu_data(), momentum,
-          this->history_[param_id]->mutable_gpu_data());
-
-      // add delta to history to guard against dividing by zero later
-      caffe_gpu_set(net_params[param_id]->count(), delta,
-          this->temp_[param_id]->mutable_gpu_data());
-
-      caffe_gpu_add(net_params[param_id]->count(),
-          this->temp_[param_id]->gpu_data(),
-          this->history_[update_history_offset + param_id]->gpu_data(),
-          this->update_[param_id]->mutable_gpu_data());
-
-      caffe_gpu_add(net_params[param_id]->count(),
-          this->temp_[param_id]->gpu_data(),
-          this->history_[param_id]->gpu_data(),
-          this->temp_[param_id]->mutable_gpu_data());
-
-      // divide history of updates by history of gradients
-      caffe_gpu_div(net_params[param_id]->count(),
-          this->update_[param_id]->gpu_data(),
-          this->temp_[param_id]->gpu_data(),
-          this->update_[param_id]->mutable_gpu_data());
-
-      // jointly compute the RMS of both for update and gradient history
-      caffe_gpu_powx(net_params[param_id]->count(),
-          this->update_[param_id]->gpu_data(), Dtype(0.5),
-          this->update_[param_id]->mutable_gpu_data());
-
-      // compute the update and copy to net_diff
-      caffe_gpu_mul(net_params[param_id]->count(),
-          net_params[param_id]->gpu_diff(),
-          this->update_[param_id]->gpu_data(),
-          net_params[param_id]->mutable_gpu_diff());
-
-      // compute square of update
-      caffe_gpu_powx(net_params[param_id]->count(),
-          net_params[param_id]->gpu_diff(), Dtype(2),
-          this->update_[param_id]->mutable_gpu_data());
-
-      // update history of updates
-      caffe_gpu_axpby(net_params[param_id]->count(), Dtype(1) - momentum,
-          this->update_[param_id]->gpu_data(), momentum,
-          this->history_[update_history_offset + param_id]->mutable_gpu_data());
-    }
+    // update history of gradients
+    caffe_gpu_axpby(net_params[param_id]->count(), Dtype(1) - momentum,
+        this->update_[param_id]->gpu_data(), momentum,
+        this->history_[param_id]->mutable_gpu_data());
+
+    // add delta to history to guard against dividing by zero later
+    caffe_gpu_set(net_params[param_id]->count(), delta,
+        this->temp_[param_id]->mutable_gpu_data());
+
+    caffe_gpu_add(net_params[param_id]->count(),
+        this->temp_[param_id]->gpu_data(),
+        this->history_[update_history_offset + param_id]->gpu_data(),
+        this->update_[param_id]->mutable_gpu_data());
+
+    caffe_gpu_add(net_params[param_id]->count(),
+        this->temp_[param_id]->gpu_data(),
+        this->history_[param_id]->gpu_data(),
+        this->temp_[param_id]->mutable_gpu_data());
+
+    // divide history of updates by history of gradients
+    caffe_gpu_div(net_params[param_id]->count(),
+        this->update_[param_id]->gpu_data(),
+        this->temp_[param_id]->gpu_data(),
+        this->update_[param_id]->mutable_gpu_data());
+
+    // jointly compute the RMS of both for update and gradient history
+    caffe_gpu_powx(net_params[param_id]->count(),
+        this->update_[param_id]->gpu_data(), Dtype(0.5),
+        this->update_[param_id]->mutable_gpu_data());
+
+    // compute the update and copy to net_diff
+    caffe_gpu_mul(net_params[param_id]->count(),
+        net_params[param_id]->gpu_diff(),
+        this->update_[param_id]->gpu_data(),
+        net_params[param_id]->mutable_gpu_diff());
+
+    // compute square of update
+    caffe_gpu_powx(net_params[param_id]->count(),
+        net_params[param_id]->gpu_diff(), Dtype(2),
+        this->update_[param_id]->mutable_gpu_data());
+
+    // update history of updates
+    caffe_gpu_axpby(net_params[param_id]->count(), Dtype(1) - momentum,
+        this->update_[param_id]->gpu_data(), momentum,
+        this->history_[update_history_offset + param_id]->mutable_gpu_data());
+
+    // apply learning rate
+    caffe_gpu_scale(net_params[param_id]->count(), local_rate,
+        net_params[param_id]->gpu_diff(),
+        net_params[param_id]->mutable_gpu_diff());
 #else
     NO_GPU;
 #endif
     break;
+  }
   default:
     LOG(FATAL) << "Unknown caffe mode: " << Caffe::mode();
   }
diff --git a/src/caffe/test/test_gradient_based_solver.cpp b/src/caffe/test/test_gradient_based_solver.cpp
index 277aa3a5c8e..1d84b1a72ed 100644
--- a/src/caffe/test/test_gradient_based_solver.cpp
+++ b/src/caffe/test/test_gradient_based_solver.cpp
@@ -165,10 +165,6 @@ class GradientBasedSolverTest : public MultiDeviceTest<TypeParam> {
        "    bottom: 'targets' "
        "  } "
        "} ";
-    if (learning_rate != 0) {
-      proto << "base_lr: " << learning_rate << " ";
-      proto << "lr_policy: 'fixed' ";
-    }
     if (weight_decay != 0) {
       proto << "weight_decay: " << weight_decay << " ";
     }
@@ -897,6 +893,138 @@ TYPED_TEST(NesterovSolverTest, TestSnapshotShare) {
   }
 }
 
+template <typename TypeParam>
+class AdaDeltaSolverTest : public GradientBasedSolverTest<TypeParam> {
+  typedef typename TypeParam::Dtype Dtype;
+
+ protected:
+  virtual void InitSolver(const SolverParameter& param) {
+    this->solver_.reset(new AdaDeltaSolver<Dtype>(param));
+  }
+
+  virtual SolverParameter_SolverType solver_type() {
+    return SolverParameter_SolverType_ADADELTA;
+  }
+};
+
+TYPED_TEST_CASE(AdaDeltaSolverTest, TestDtypesAndDevices);
+
+TYPED_TEST(AdaDeltaSolverTest, TestAdaDeltaLeastSquaresUpdate) {
+  typedef typename TypeParam::Dtype Dtype;
+  const Dtype kLearningRate = 1.0;
+  this->TestLeastSquaresUpdate(kLearningRate);
+}
+
+TYPED_TEST(AdaDeltaSolverTest, TestAdaDeltaLeastSquaresUpdateWithWeightDecay) {
+  typedef typename TypeParam::Dtype Dtype;
+  const Dtype kLearningRate = 1.0;
+  const Dtype kWeightDecay = 0.5;
+  const Dtype kMomentum = 0.95;
+  this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum);
+}
+
+TYPED_TEST(AdaDeltaSolverTest, TestAdaDeltaLeastSquaresUpdateWithHalfMomentum) {
+  typedef typename TypeParam::Dtype Dtype;
+  const Dtype kLearningRate = 1.0;
+  const Dtype kWeightDecay = 0.0;
+  const Dtype kMomentum = 0.5;
+  const int kNumIters = 1;
+  for (int i = 0; i <= kNumIters; ++i) {
+      this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum);
+  }
+}
+
+TYPED_TEST(AdaDeltaSolverTest, TestAdaDeltaLeastSquaresUpdateWithMomentum) {
+  typedef typename TypeParam::Dtype Dtype;
+  const Dtype kLearningRate = 1.0;
+  const Dtype kWeightDecay = 0.0;
+  const Dtype kMomentum = 0.95;
+  const int kNumIters = 1;
+  for (int i = 0; i <= kNumIters; ++i) {
+      this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum);
+  }
+}
+
+TYPED_TEST(AdaDeltaSolverTest, TestLeastSquaresUpdateWithMomentumMultiIter) {
+  typedef typename TypeParam::Dtype Dtype;
+  const Dtype kLearningRate = 1.0;
+  const Dtype kWeightDecay = 0.0;
+  const Dtype kMomentum = 0.95;
+  const int kNumIters = 4;
+  for (int i = 0; i <= kNumIters; ++i) {
+      this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum, i);
+  }
+}
+
+TYPED_TEST(AdaDeltaSolverTest, TestAdaDeltaLeastSquaresUpdateWithEverything) {
+  typedef typename TypeParam::Dtype Dtype;
+  const Dtype kLearningRate = 1.0;
+  const Dtype kWeightDecay = 0.1;
+  const Dtype kMomentum = 0.95;
+  const int kNumIters = 4;
+  for (int i = 0; i <= kNumIters; ++i) {
+      this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum, i);
+  }
+}
+
+TYPED_TEST(AdaDeltaSolverTest, TestAdaDeltaLeastSquaresUpdateWithEverythingShare) {
+  typedef typename TypeParam::Dtype Dtype;
+  const Dtype kLearningRate = 1.0;
+  const Dtype kWeightDecay = 0.1;
+  const Dtype kMomentum = 0.95;
+  const int kNumIters = 4;
+  this->share_ = true;
+  for (int i = 0; i <= kNumIters; ++i) {
+      this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum, i);
+  }
+}
+
+TYPED_TEST(AdaDeltaSolverTest, TestLeastSquaresUpdateWithEverythingAccum) {
+  typedef typename TypeParam::Dtype Dtype;
+  const Dtype kLearningRate = 1.0;
+  const Dtype kWeightDecay = 0.1;
+  const Dtype kMomentum = 0.95;
+  const int kNumIters = 4;
+  const int kIterSize = 2;
+  this->CheckAccumulation(kLearningRate, kWeightDecay, kMomentum, kNumIters,
+      kIterSize);
+}
+
+TYPED_TEST(AdaDeltaSolverTest, TestLeastSquaresUpdateWithEverythingAccumShare) {
+  typedef typename TypeParam::Dtype Dtype;
+  const Dtype kLearningRate = 1.0;
+  const Dtype kWeightDecay = 0.1;
+  const Dtype kMomentum = 0.95;
+  const int kNumIters = 4;
+  const int kIterSize = 2;
+  this->share_ = true;
+  this->CheckAccumulation(kLearningRate, kWeightDecay, kMomentum, kNumIters,
+      kIterSize);
+}
+
+TYPED_TEST(AdaDeltaSolverTest, TestSnapshot) {
+  typedef typename TypeParam::Dtype Dtype;
+  const Dtype kLearningRate = 1.0;
+  const Dtype kWeightDecay = 0.1;
+  const Dtype kMomentum = 0.95;
+  const int kNumIters = 4;
+  for (int i = 1; i <= kNumIters; ++i) {
+    this->TestSnapshot(kLearningRate, kWeightDecay, kMomentum, i);
+  }
+}
+
+TYPED_TEST(AdaDeltaSolverTest, TestSnapshotShare) {
+  typedef typename TypeParam::Dtype Dtype;
+  const Dtype kLearningRate = 1.0;
+  const Dtype kWeightDecay = 0.1;
+  const Dtype kMomentum = 0.95;
+  const int kNumIters = 4;
+  this->share_ = true;
+  for (int i = 1; i <= kNumIters; ++i) {
+    this->TestSnapshot(kLearningRate, kWeightDecay, kMomentum, i);
+  }
+}
+
 template <typename TypeParam>
 class RMSPropSolverTest : public GradientBasedSolverTest<TypeParam> {
   typedef typename TypeParam::Dtype Dtype;
@@ -1003,78 +1131,4 @@ TYPED_TEST(RMSPropSolverTest, TestSnapshotShare) {
   }
 }
 
-template <typename TypeParam>
-class AdaDeltaSolverTest : public GradientBasedSolverTest<TypeParam> {
-  typedef typename TypeParam::Dtype Dtype;
-
- protected:
-  virtual void InitSolver(const SolverParameter& param) {
-    this->solver_.reset(new AdaDeltaSolver<Dtype>(param));
-  }
-
-  virtual SolverParameter_SolverType solver_type() {
-    return SolverParameter_SolverType_ADADELTA;
-  }
-};
-
-TYPED_TEST_CASE(AdaDeltaSolverTest, TestDtypesAndDevices);
-
-TYPED_TEST(AdaDeltaSolverTest, TestAdaDeltaLeastSquaresUpdate) {
-  typedef typename TypeParam::Dtype Dtype;
-  const Dtype kLearningRate = 0.0;
-  this->TestLeastSquaresUpdate(kLearningRate);
-}
-
-TYPED_TEST(AdaDeltaSolverTest, TestAdaDeltaLeastSquaresUpdateWithWeightDecay) {
-  typedef typename TypeParam::Dtype Dtype;
-  const Dtype kLearningRate = 0.0;
-  const Dtype kWeightDecay = 0.5;
-  const Dtype kMomentum = 0.95;
-  this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum);
-}
-
-TYPED_TEST(AdaDeltaSolverTest, TestAdaDeltaLeastSquaresUpdateWithHalfMomentum) {
-  typedef typename TypeParam::Dtype Dtype;
-  const Dtype kLearningRate = 0.0;
-  const Dtype kWeightDecay = 0.0;
-  const Dtype kMomentum = 0.5;
-  const int kNumIters = 1;
-  for (int i = 0; i <= kNumIters; ++i) {
-      this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum);
-  }
-}
-
-TYPED_TEST(AdaDeltaSolverTest, TestAdaDeltaLeastSquaresUpdateWithMomentum) {
-  typedef typename TypeParam::Dtype Dtype;
-  const Dtype kLearningRate = 0.0;
-  const Dtype kWeightDecay = 0.0;
-  const Dtype kMomentum = 0.95;
-  const int kNumIters = 1;
-  for (int i = 0; i <= kNumIters; ++i) {
-      this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum);
-  }
-}
-
-TYPED_TEST(AdaDeltaSolverTest, TestLeastSquaresUpdateWithMomentumMultiIter) {
-  typedef typename TypeParam::Dtype Dtype;
-  const Dtype kLearningRate = 0.0;
-  const Dtype kWeightDecay = 0.0;
-  const Dtype kMomentum = 0.95;
-  const int kNumIters = 4;
-  for (int i = 0; i <= kNumIters; ++i) {
-      this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum, i);
-  }
-}
-
-TYPED_TEST(AdaDeltaSolverTest, TestAdaDeltaLeastSquaresUpdateWithEverything) {
-  typedef typename TypeParam::Dtype Dtype;
-  const Dtype kLearningRate = 0.0;
-  const Dtype kWeightDecay = 0.1;
-  const Dtype kMomentum = 0.95;
-  const int kNumIters = 4;
-  for (int i = 0; i <= kNumIters; ++i) {
-      this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum, i);
-  }
-}
-
 }  // namespace caffe