[Loss] Update MSR to MSE

Update the name of mean squared error from MSR to MSE

Signed-off-by: Parichay Kapoor <pk.kapoor@samsung.com>

diff --git a/Applications/Classification/res/Classification.ini b/Applications/Classification/res/Classification.ini
index 6eaf73c..bf5558e 100644 (file)
--- a/Applications/Classification/res/Classification.ini
+++ b/Applications/Classification/res/Classification.ini
@@ -7,8 +7,8 @@ Decay_steps = 1000       # decay step for the exponential decayed learning rate
 Epoch = 30000          # Epoch
 Optimizer = adam       # Optimizer : sgd (stochastic gradien decent),
                        #             adam (Adamtive Moment Estimation)
-Cost = cross           # Cost(loss) function : msr (mean square root error)
-                        #                       cross ( for cross entropy )
+Cost = cross           # Cost(loss) function : mse (mean squared error)
+                        #                       cross (cross entropy)
 Model = "model.bin"    # model path to save / read
 minibatch = 32         # mini batch size
 beta1 = 0.9            # beta 1 for adam

diff --git a/Applications/Classification/res/Classification_func.ini b/Applications/Classification/res/Classification_func.ini
index a91c6c1..db9fb42 100644 (file)
--- a/Applications/Classification/res/Classification_func.ini
+++ b/Applications/Classification/res/Classification_func.ini
@@ -7,8 +7,8 @@ Decay_steps = 1000       # decay step for the exponential decayed learning rate
 Epoch = 30000          # Epoch
 Optimizer = adam       # Optimizer : sgd (stochastic gradien decent),
                        #             adam (Adamtive Moment Estimation)
-Cost = cross           # Cost(loss) function : msr (mean square root error)
-                        #                       cross ( for cross entropy )
+Cost = cross           # Cost(loss) function : mse (mean squared error)
+                        #                       cross (cross entropy)
 Model = "model.bin"    # model path to save / read
 minibatch = 32         # mini batch size
 beta1 = 0.9            # beta 1 for adam

diff --git a/Applications/Classification/res/Classification_new.ini b/Applications/Classification/res/Classification_new.ini
index ff074e5..0b53e87 100644 (file)
--- a/Applications/Classification/res/Classification_new.ini
+++ b/Applications/Classification/res/Classification_new.ini
@@ -8,8 +8,8 @@ Epoch = 30000           # Epoch
 Optimizer = sgd        # Optimizer : sgd (stochastic gradien decent),
                        #             adam (Adamtive Moment Estimation)
 Activation = sigmoid   # activation : sigmoid, tanh
-Cost = cross                   # Cost(loss) function : msr (mean square root error)
-                        #                       categorical ( for logistic regression )
+Cost = cross                   # Cost(loss) function : mse (mean squared error)
+                        #                       cross (cross entropy)
 Model = "model.bin"    # model path to save / read
 minibatch = 32         # mini batch size
 beta1 = 0.9            # beta 1 for adam

diff --git a/Applications/LogisticRegression/res/LogisticRegression.ini b/Applications/LogisticRegression/res/LogisticRegression.ini
index c0b8103..2f065bb 100644 (file)
--- a/Applications/LogisticRegression/res/LogisticRegression.ini
+++ b/Applications/LogisticRegression/res/LogisticRegression.ini
@@ -5,7 +5,7 @@ Learning_rate = 0.001   # Learning Rate
 Epoch = 100            # Epoch
 Optimizer = sgd                # Optimizer : sgd (stochastic gradien decent),
                        #             adam (Adamtive Moment Estimation)
-Cost = cross           # Cost(loss) function : msr (mean square root error)
+Cost = cross           # Cost(loss) function : mse (mean squared error)
                         #                       cross ( cross entropy )
 Model = "model.bin"    # model path to save / read
 minibatch = 1          # mini batch size

diff --git a/Applications/ReinforcementLearning/DeepQ/jni/DeepQ.ini b/Applications/ReinforcementLearning/DeepQ/jni/DeepQ.ini
index ec9d2c0..482f5ed 100644 (file)
--- a/Applications/ReinforcementLearning/DeepQ/jni/DeepQ.ini
+++ b/Applications/ReinforcementLearning/DeepQ/jni/DeepQ.ini
@@ -6,8 +6,8 @@ Epoch = 10000           # Epoch
 Optimizer = adam       # Optimizer : sgd (stochastic gradien decent),
                        #             adam (Adamtive Moment Estimation)
 
-Cost = msr             # Cost(loss) function : msr (mean square root error)
-                        #                       categorical ( for logistic regression )
+Cost = mse             # Cost(loss) function : mse (mean squared error)
+                        #                       cross (cross entropy)
 Model = "model.bin"    # model path to save / read
 minibatch = 32         # mini batch size
 beta1 = 0.9            # beta 1 for adam

diff --git a/Applications/Tizen_CAPI/Tizen_CAPI_config.ini b/Applications/Tizen_CAPI/Tizen_CAPI_config.ini
index ba96f76..9318a70 100644 (file)
--- a/Applications/Tizen_CAPI/Tizen_CAPI_config.ini
+++ b/Applications/Tizen_CAPI/Tizen_CAPI_config.ini
@@ -7,7 +7,7 @@ Decay_steps = 1000       # decay step for the exponential decayed learning rate
 Epoch = 10             # Epoch
 Optimizer = adam       # Optimizer : sgd (stochastic gradien decent),
                        #             adam (Adamtive Moment Estimation)
-Cost = cross           # Cost(loss) function : msr (mean square root error)
+Cost = cross           # Cost(loss) function : mse (mean squared error)
                         #                       cross ( for cross entropy )
 Model = "model.bin"    # model path to save / read
 minibatch = 32         # mini batch size

diff --git a/Applications/Training/res/Training.ini b/Applications/Training/res/Training.ini
index 886c135..8b02b78 100644 (file)
--- a/Applications/Training/res/Training.ini
+++ b/Applications/Training/res/Training.ini
@@ -5,8 +5,8 @@ Learning_rate = 0.01    # Learning Rate
 Epoch = 100            # Epoch
 Optimizer = sgd                # Optimizer : sgd (stochastic gradien decent),
                        #             adam (Adamtive Moment Estimation)
-Cost = cross                   # Cost(loss) function : msr (mean square root error)
-                        #                       cross ( for Cross Entropy )
+Cost = cross                   # Cost(loss) function : mse (mean squared error)
+                        #                       cross (cross entropy)
 Model = "model.bin"    # model path to save / read
 minibatch = 1          # mini batch size
 # beta1 = 0.9          # beta 1 for adam

diff --git a/Applications/mnist/res/mnist.ini b/Applications/mnist/res/mnist.ini
index 71a98c7..c161ed9 100644 (file)
--- a/Applications/mnist/res/mnist.ini
+++ b/Applications/mnist/res/mnist.ini
@@ -5,7 +5,7 @@ Learning_rate = 1e-4    # Learning Rate
 Epoch = 1500           # Epoch
 Optimizer = adam       # Optimizer : sgd (stochastic gradien decent),
                        #             adam (Adamtive Moment Estimation)
-Cost = cross           # Cost(loss) function : msr (mean square root error)
+Cost = cross           # Cost(loss) function : mse (mean squared error)
                         #                       cross ( for cross entropy )
 Model = "model.bin"    # model path to save / read
 minibatch = 32         # mini batch size

diff --git a/README.md b/README.md
index 48127ce..7032d1d 100644 (file)
--- a/README.md
+++ b/README.md
@@ -44,7 +44,7 @@ The most important role of this component is to activate forward / backward prop
 - **_Optimizer:_** Optimizer for the Network Model - sgd, adam
 - **_Activation:_** Activation Function - sigmoid , tanh
 - **_Cost:_** Cost Function -
-      msr(mean square root error), categorical (for logistic regression), cross (cross entropy)
+      mse(mean squared error), cross (cross entropy)
 - **_Model:_** Name of Model. Weight Data is saved in the name of this.
 - **_minibach:_** mini batch size
 - **_beta1,beta2,epsilon:_** hyper parameters for the adam optimizer
@@ -86,7 +86,7 @@ The following dependencies are needed to compile / build / run.
 
 You can use [docker image](https://hub.docker.com/r/lunapocket/nntrainer-build-env) to easily set up and try building.
 
-To run the docker 
+To run the docker
 
 ```bash
 $ docker pull lunapocket/nntrainer-build-env:ubuntu-18.04

diff --git a/nntrainer/include/layer.h b/nntrainer/include/layer.h
index 18b84f3..50186b7 100644 (file)
--- a/nntrainer/include/layer.h
+++ b/nntrainer/include/layer.h
@@ -36,7 +36,7 @@ namespace nntrainer {
 
 /**
  * @brief     Enumeration of cost(loss) function type
- *            0. MSR ( Mean Squared Roots )
+ *            0. MSE ( Mean Squared Error )
  *            1. ENTROPY ( Cross Entropy )
  *            2. ENTROPY_SIGMOID (Cross Entropy amalgamated with sigmoid for
  * stability)
@@ -45,7 +45,7 @@ namespace nntrainer {
  *            4. Unknown
  */
 typedef enum {
-  COST_MSR,
+  COST_MSE,
   COST_ENTROPY,
   COST_ENTROPY_SIGMOID,
   COST_ENTROPY_SOFTMAX,

diff --git a/nntrainer/src/loss_layer.cpp b/nntrainer/src/loss_layer.cpp
index ec752c5..89e1e0a 100644 (file)
--- a/nntrainer/src/loss_layer.cpp
+++ b/nntrainer/src/loss_layer.cpp
@@ -51,7 +51,7 @@ Tensor LossLayer::forwarding(Tensor output, Tensor label, int &status) {
   Tensor l;
 
   switch (cost) {
-  case COST_MSR: {
+  case COST_MSE: {
     // y2 <- y2 - y;
     y2.subtract_i(y);
 
@@ -123,7 +123,7 @@ Tensor LossLayer::backwarding(Tensor derivative, int iteration) {
   Tensor y = input;
 
   switch (cost) {
-  case COST_MSR:
+  case COST_MSE:
     ret_derivative = y.subtract(y2).multiply(2).divide(y.getDim().getDataLen());
     break;
   case COST_ENTROPY_SIGMOID:

diff --git a/nntrainer/src/parse_util.cpp b/nntrainer/src/parse_util.cpp
index b4c1a53..cf0c486 100644 (file)
--- a/nntrainer/src/parse_util.cpp
+++ b/nntrainer/src/parse_util.cpp
@@ -73,10 +73,10 @@ unsigned int parseType(std::string ll, InputType t) {
 
   /**
    * @brief     Cost Function String from configure file
-   *            "msr"  : Mean Squared Roots
+   *            "mse"  : Mean Squared Error
    *            "caterogical" : Categorical Cross Entropy
    */
-  std::array<std::string, 2> cost_string = {"msr", "cross"};
+  std::array<std::string, 2> cost_string = {"mse", "cross"};
 
   /**
    * @brief     Network Type String from configure file

diff --git a/test/tizen_capi/test_conf.ini b/test/tizen_capi/test_conf.ini
index 29cd287..f542b27 100644 (file)
--- a/test/tizen_capi/test_conf.ini
+++ b/test/tizen_capi/test_conf.ini
@@ -5,8 +5,8 @@ Learning_rate = 0.01    # Learning Rate
 Epoch = 100            # Epoch
 Optimizer = sgd                # Optimizer : sgd (stochastic gradien decent),
                        #             adam (Adamtive Moment Estimation)
-Cost = cross                   # Cost(loss) function : msr (mean square root error)
-                        #                       cross ( for Cross Entropy )
+Cost = cross                   # Cost(loss) function : mse (mean squared error)
+                        #                       cross (Cross Entropy)
 Model = "model.bin"    # model path to save / read
 Minibatch = 1          # mini batch size
 # beta1 = 0.9          # beta 1 for adam

diff --git a/test/unittest/unittest_nntrainer_layers.cpp b/test/unittest/unittest_nntrainer_layers.cpp
index 468a20c..270ba31 100644 (file)
--- a/test/unittest/unittest_nntrainer_layers.cpp
+++ b/test/unittest/unittest_nntrainer_layers.cpp
@@ -479,7 +479,7 @@ TEST_F(nntrainer_FullyConnectedLayer_TFmatch, forwarding_backwarding_01_p) {
 TEST_F(nntrainer_FullyConnectedLayer_TFmatch, forwarding_backwarding_02_p) {
 
   addActivation(nntrainer::ACT_SIGMOID);
-  addLoss(nntrainer::COST_MSR);
+  addLoss(nntrainer::COST_MSE);
   setOptimizer(nntrainer::OptType::sgd, "learning_rate=1.0");
 
   /** Verify forwarding value */
@@ -500,7 +500,7 @@ TEST_F(nntrainer_FullyConnectedLayer_TFmatch, forwarding_backwarding_02_p) {
 TEST_F(nntrainer_FullyConnectedLayer_TFmatch, forwarding_backwarding_03_p) {
 
   addActivation(nntrainer::ACT_SOFTMAX);
-  addLoss(nntrainer::COST_MSR);
+  addLoss(nntrainer::COST_MSE);
   setOptimizer(nntrainer::OptType::sgd, "learning_rate=1.0");
 
   /** Verify forwarding value */
@@ -520,7 +520,7 @@ TEST_F(nntrainer_FullyConnectedLayer_TFmatch, forwarding_backwarding_03_p) {
  */
 TEST_F(nntrainer_FullyConnectedLayer_TFmatch, forwarding_backwarding_04_p) {
 
-  addLoss(nntrainer::COST_MSR);
+  addLoss(nntrainer::COST_MSE);
   setOptimizer(nntrainer::OptType::sgd, "learning_rate=1.0");
 
   /** Verify forwarding value */
@@ -541,7 +541,7 @@ TEST_F(nntrainer_FullyConnectedLayer_TFmatch, forwarding_backwarding_04_p) {
 TEST_F(nntrainer_FullyConnectedLayer_TFmatch, forwarding_backwarding_05_p) {
 
   addActivation(nntrainer::ACT_SIGMOID);
-  addLoss(nntrainer::COST_MSR);
+  addLoss(nntrainer::COST_MSE);
   setOptimizer(nntrainer::OptType::sgd, "learning_rate=1.0");
 
   /** Verify forwarding value */
@@ -562,7 +562,7 @@ TEST_F(nntrainer_FullyConnectedLayer_TFmatch, forwarding_backwarding_05_p) {
 TEST_F(nntrainer_FullyConnectedLayer_TFmatch, forwarding_backwarding_06_p) {
 
   addActivation(nntrainer::ACT_SOFTMAX);
-  addLoss(nntrainer::COST_MSR);
+  addLoss(nntrainer::COST_MSE);
   setOptimizer(nntrainer::OptType::sgd, "learning_rate=1.0");
 
   /** Verify forwarding value */