Adadelta

examples/mnist/lenet_adadelta_solver.prototxt

@@ -0,0 +1,22 @@
+# The train/test net protocol buffer definition
+net: "examples/mnist/lenet_train_test.prototxt"
+# test_iter specifies how many forward passes the test should carry out.
+# In the case of MNIST, we have test batch size 100 and 100 test iterations,
+# covering the full 10,000 testing images.
+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.
+momentum: 0.95
+weight_decay: 0.0005
+# Display every 100 iterations
+display: 100
+# The maximum number of iterations
+max_iter: 10000
+# snapshot intermediate results
+snapshot: 5000
+snapshot_prefix: "examples/mnist/lenet_adadelta"
+# solver mode: CPU or GPU
+solver_mode: GPU
+solver_type: ADADELTA
+delta: 1e-6

examples/mnist/mnist_autoencoder_solver_adadelta.prototxt

@@ -0,0 +1,17 @@
+net: "examples/mnist/mnist_autoencoder.prototxt"
+test_state: { stage: 'test-on-train' }
+test_iter: 500
+test_state: { stage: 'test-on-test' }
+test_iter: 100
+test_interval: 500
+test_compute_loss: true
+momentum: 0.95
+display: 100
+max_iter: 65000
+weight_decay: 0.0005
+snapshot: 10000
+snapshot_prefix: "examples/mnist/mnist_autoencoder_adadelta_train"
+# solver mode: CPU or GPU
+solver_mode: GPU
+solver_type: ADADELTA
+delta: 1e-8

examples/mnist/train_mnist_autoencoder_adadelta.sh

@@ -0,0 +1,4 @@
+#!/bin/bash
+
+./build/tools/caffe train \
+  --solver=examples/mnist/mnist_autoencoder_solver_adadelta.prototxt

include/caffe/solver.hpp

@@ -124,6 +124,27 @@ class AdaGradSolver : public SGDSolver<Dtype> {
   DISABLE_COPY_AND_ASSIGN(AdaGradSolver);
 };
 
+template <typename Dtype>
+class AdaDeltaSolver : public SGDSolver<Dtype> {
+ public:
+  explicit AdaDeltaSolver(const SolverParameter& param)
+      : SGDSolver<Dtype>(param) { constructor_sanity_check(); }
+  explicit AdaDeltaSolver(const string& param_file)
+      : SGDSolver<Dtype>(param_file) { constructor_sanity_check(); }
+
+ 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.";
+  }
+
+  DISABLE_COPY_AND_ASSIGN(AdaDeltaSolver);
+};
+
 template <typename Dtype>
 Solver<Dtype>* GetSolver(const SolverParameter& param) {
   SolverParameter_SolverType type = param.solver_type();
@@ -135,6 +156,8 @@ Solver<Dtype>* GetSolver(const SolverParameter& param) {
       return new NesterovSolver<Dtype>(param);
   case SolverParameter_SolverType_ADAGRAD:
       return new AdaGradSolver<Dtype>(param);
+  case SolverParameter_SolverType_ADADELTA:
+      return new AdaDeltaSolver<Dtype>(param);
   default:
       LOG(FATAL) << "Unknown SolverType: " << type;
   }

src/caffe/proto/caffe.proto

@@ -146,6 +146,7 @@ message SolverParameter {
     SGD = 0;
     NESTEROV = 1;
     ADAGRAD = 2;
+    ADADELTA = 3;
   }
   optional SolverType solver_type = 30 [default = SGD];
   // numerical stability for AdaGrad

src/caffe/solver.cpp

@@ -753,9 +753,208 @@ void AdaGradSolver<Dtype>::ComputeUpdateValue() {
   }
 }
 
+template <typename Dtype>
+void AdaDeltaSolver<Dtype>::PreSolve() {
+  // Initialize the history
+  vector<shared_ptr<Blob<Dtype> > >& net_params = this->net_->params();
+  this->history_.clear();
+  this->update_.clear();
+  this->temp_.clear();
+  for (int i = 0; i < net_params.size(); ++i) {
+    const Blob<Dtype>* net_param = net_params[i].get();
+    this->history_.push_back(shared_ptr<Blob<Dtype> >(new Blob<Dtype>(
+        net_param->num(), net_param->channels(), net_param->height(),
+        net_param->width())));
+    this->update_.push_back(shared_ptr<Blob<Dtype> >(new Blob<Dtype>(
+        net_param->num(), net_param->channels(), net_param->height(),
+        net_param->width())));
+    this->temp_.push_back(shared_ptr<Blob<Dtype> >(new Blob<Dtype>(
+        net_param->num(), net_param->channels(), net_param->height(),
+        net_param->width())));
+  }
+  for (int i = 0; i < net_params.size(); ++i) {
+    const Blob<Dtype>* net_param = net_params[i].get();
+    this->history_.push_back(shared_ptr<Blob<Dtype> >(new Blob<Dtype>(
+        net_param->num(), net_param->channels(), net_param->height(),
+        net_param->width())));
+  }
+}
+
+template <typename Dtype>
+void AdaDeltaSolver<Dtype>::ComputeUpdateValue() {
+  vector<shared_ptr<Blob<Dtype> > >& net_params = this->net_->params();
+  vector<float>& net_params_weight_decay = this->net_->params_weight_decay();
+  Dtype delta = this->param_.delta();
+  Dtype momentum = this->param_.momentum();
+  Dtype weight_decay = this->param_.weight_decay();
+  string regularization_type = this->param_.regularization_type();
+  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;
+        }
+      }
+
+      // 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());
+    }
+    break;
+  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());
+
+      // 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());
+    }
+#else
+    NO_GPU;
+#endif
+    break;
+  default:
+    LOG(FATAL) << "Unknown caffe mode: " << Caffe::mode();
+  }
+}
+
 INSTANTIATE_CLASS(Solver);
 INSTANTIATE_CLASS(SGDSolver);
 INSTANTIATE_CLASS(NesterovSolver);
 INSTANTIATE_CLASS(AdaGradSolver);
+INSTANTIATE_CLASS(AdaDeltaSolver);
 
 }  // namespace caffe