Added suporte for cylindrical model from config file

fialhocoelho · Feb 17, 2020 · 94dbe35 · 94dbe35
1 parent fbd36e2
commit 94dbe35
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Showing 6 changed files with 20 additions and 18 deletions.
diff --git a/config-cnn.json b/config-cnn.json
@@ -6,7 +6,8 @@
     "data": {
         "filename": "./dataset/phi025-025_eta025-025_filtered.csv",
         "train_split": 0.70,
-        "normalise": true
+        "normalise": true,
+        "cilyndrical": true        
     },
     "training": {
         "epochs": 20,

diff --git a/config-lstm-paralel.json b/config-lstm-paralel.json
@@ -6,7 +6,8 @@
     "data": {
         "filename": "./dataset/phi025-025_eta025-025_filtered.csv",
         "train_split": 0.70,
-        "normalise": true
+        "normalise": true,
+        "cilyndrical": false
     },
     "training": {
         "epochs": 20,

diff --git a/config-lstm.json b/config-lstm.json
@@ -6,7 +6,8 @@
     "data": {
         "filename": "./dataset/phi025-025_eta025-025_filtered.csv",
         "train_split": 0.70,
-        "normalise": true
+        "normalise": true,
+        "cilyndrical": false
     },
     "training": {
         "epochs": 20,

diff --git a/config.json b/config.json
@@ -6,19 +6,20 @@
     "data": {
         "filename": "./dataset/phi025-025_eta025-025_filtered.csv",
         "train_split": 0.70,
-        "normalise": true
+        "normalise": true,
+        "cilyndrical": true
     },
     "training": {
         "epochs": 20,
         "batch_size": 32,
         "save_model": true,
-        "load_model": true,
+        "load_model": false,
         "use_gpu": true        
     },
     "model": {
         "name": "lstm",
         "loss": "mse",
-        "optimizer": "RMSprop",
+        "optimizer": "rmsprop",
         "layers": [
             {
                 "type": "lstm",

diff --git a/main-paralel.py b/main-paralel.py
@@ -90,16 +90,17 @@ def main():
     num_features = configs['model']['layers'][0]['input_features']  # the number of features of each hits
 
     split = configs['data']['train_split']  # the number of features of each hits
-
+    cilyndrical = configs['data']['cilyndrical']  # set to polar or cartesian coordenates
+    normalise = configs['data']['normalise'] 
 
     # config gpu
     #gpu()
 
     # prepare data set
     data = Dataset(data_dir, KindNormalization.Zscore)
 
-    X, X_, y = data.prepare_training_data(FeatureType.Divided, normalise=True,
-                                                  cilyndrical=True)
+    X, X_, y = data.prepare_training_data(FeatureType.Divided, normalise=normalise,
+                                                  cilyndrical=cilyndrical)
 
     # reshape data     
     X = data.reshape3d(X, time_steps, num_features)

diff --git a/main.py b/main.py
@@ -95,19 +95,20 @@ def main():
     num_features = configs['model']['layers'][0]['input_features']  # the number of features of each hits
 
     split = configs['data']['train_split']  # the number of features of each hits
-
+    cilyndrical = configs['data']['cilyndrical']  # set to polar or cartesian coordenates
+    normalise = configs['data']['normalise'] 
 
     # config gpu
     #gpu()
 
     # prepare data set
     data = Dataset(data_dir, KindNormalization.Zscore)
 
-    X, y = data.prepare_training_data(FeatureType.Positions, normalise=True,
-                                                  cilyndrical=True)
+    X, y = data.prepare_training_data(FeatureType.Positions, normalise=normalise,
+                                                  cilyndrical=cilyndrical)
 
     # reshape data     
-    #X = data.reshape3d(X, time_steps, num_features)
+    X = data.reshape3d(X, time_steps, num_features)
 
     X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=1-split, random_state=42)
 
@@ -129,8 +130,6 @@ def main():
 
         model.build_model()
 
-        #x_train = [X_train, X_train_]
-
         # in-memory training
         history = model.train(
             x=X_train,
@@ -145,9 +144,7 @@ def main():
         if not model.load_model():
             print ('[Error] please change the config file : load_model')
             return
-
-    #x_test = [X_test, X_test_]
-
+
     predicted = model.predict_one_hit(X_test)
 
     y_predicted = np.reshape(predicted, (predicted.shape[0]*predicted.shape[1], 1))