val split

weecology · Aug 1, 2020 · a1f1e85 · a1f1e85
1 parent 4015d78
commit a1f1e85
Showing 1 changed file with 37 additions and 37 deletions.
diff --git a/experiments/run.py b/experiments/run.py
@@ -33,24 +33,24 @@
 
 ##Train
 #Train see config.yml for tfrecords path with weighted classes in cross entropy
-model.read_data(validation_split=False)    
+model.read_data(validation_split=True)    
 class_weight = model.calc_class_weight()
 
 ## Train subnetwork
 experiment.log_parameter("Train subnetworks", True)
 with experiment.context_manager("spatial_subnetwork"):
     print("Train spatial subnetwork")
-    model.read_data(mode="submodel",validation_split=False)
+    model.read_data(mode="submodel",validation_split=True)
     model.train(submodel="spatial", class_weight=[class_weight, class_weight, class_weight])
 
 with experiment.context_manager("spectral_subnetwork"):
     print("Train spectral subnetwork")    
-    model.read_data(mode="submodel",validation_split=False)   
+    model.read_data(mode="submodel",validation_split=True)   
     model.train(submodel="spectral", class_weight=[class_weight, class_weight, class_weight])
 
 #Train full model
 experiment.log_parameter("Class Weighted", True)
-model.read_data(validation_split=False)
+model.read_data(validation_split=True)
 model.train(class_weight=class_weight)
 
 #Get Alpha score for the weighted spectral/spatial average. Higher alpha favors spatial network.
@@ -60,44 +60,44 @@
 
 ##Evaluate
 #Evaluation scores, see config.yml for tfrecords path
-#y_pred, y_true = model.evaluate(model.val_split)
+y_pred, y_true = model.evaluate(model.val_split)
 
 #Evaluation accuracy
-#eval_acc = keras_metrics.CategoricalAccuracy()
-#eval_acc.update_state(y_true, y_pred)
-#experiment.log_metric("Evaluation Accuracy",eval_acc.result().numpy())
+eval_acc = keras_metrics.CategoricalAccuracy()
+eval_acc.update_state(y_true, y_pred)
+experiment.log_metric("Evaluation Accuracy",eval_acc.result().numpy())
 
-#macro, micro = metrics.f1_scores(y_true, y_pred)
-#experiment.log_metric("MicroF1",micro)
-#experiment.log_metric("MacroF1",macro)
+macro, micro = metrics.f1_scores(y_true, y_pred)
+experiment.log_metric("MicroF1",micro)
+experiment.log_metric("MacroF1",macro)
 
 #Confusion matrix
-#class_labels = {
-    #0: "Unclassified",
-    #1 : "Healthy grass",
-    #2 : "Stressed grass",
-    #3 : "Artificial turf",
-    #4 : "Evergreen trees",
-    #5 : "Deciduous trees",
-    #6 : "Bare earth",
-    #7 : "Water",
-    #8 : "Residential buildings",
-    #9 : "Non-residential buildings",
-    #10 : "Roads",
-    #11 : "Sidewalks",
-    #12 : "Crosswalks",
-    #13 : "Major thoroughfares",
-    #14 : "Highways",
-    #15 : "Railways",
-    #16 : "Paved parking lots",
-    #17 : "Unpaved parking lots",
-    #18 : "Cars",
-    #19 : "Trains",
-    #20 : "Stadium seat"
-#}
-
-#print("Unique labels in ytrue {}, unique labels in y_pred {}".format(np.unique(np.argmax(y_true,1)),np.unique(np.argmax(y_pred,1))))
-#experiment.log_confusion_matrix(y_true = y_true, y_predicted = y_pred, labels=list(class_labels.values()), title="Confusion Matrix")
+class_labels = {
+    0: "Unclassified",
+    1 : "Healthy grass",
+    2 : "Stressed grass",
+    3 : "Artificial turf",
+    4 : "Evergreen trees",
+    5 : "Deciduous trees",
+    6 : "Bare earth",
+    7 : "Water",
+    8 : "Residential buildings",
+    9 : "Non-residential buildings",
+    10 : "Roads",
+    11 : "Sidewalks",
+    12 : "Crosswalks",
+    13 : "Major thoroughfares",
+    14 : "Highways",
+    15 : "Railways",
+    16 : "Paved parking lots",
+    17 : "Unpaved parking lots",
+    18 : "Cars",
+    19 : "Trains",
+    20 : "Stadium seat"
+}
+
+print("Unique labels in ytrue {}, unique labels in y_pred {}".format(np.unique(np.argmax(y_true,1)),np.unique(np.argmax(y_pred,1))))
+experiment.log_confusion_matrix(y_true = y_true, y_predicted = y_pred, labels=list(class_labels.values()), title="Confusion Matrix")
 
 #Predict
 predict_tfrecords = glob.glob("/orange/ewhite/b.weinstein/Houston2018/tfrecords/predict/*.tfrecord")