fix covariance normalization; add run_model script for multi times training

run_model.sh

@@ -0,0 +1,29 @@
+#!/usr/bin/env bash
+
+
+N=$1
+OUTPUT=$2
+DEPTH=$3
+TYPE=$4
+RESDIR=$5
+mkdir -p /tmp/rk/${RESDIR}
+DATADIR=$6
+
+EPOCHS=100
+
+for i in {1..$N}
+do
+    python main.py --mode train \
+                --train ${DATADIR}/currentData.csv \
+                --model ${RESDIR}/${OUTPUT}_${TYPE}_$i \
+                --epochs $EPOCHS \
+                --embd 128 \
+                --filter_embd 256 --kernel_embd 8 --dense_embd 128 \
+                --domain_embd 32 \
+                --filter_main 32  --kernel_main 8 --dense_main 1024 \
+                --batch 256 \
+                --balanced_weights \
+                --model_output ${OUTPUT} \
+                --type ${TYPE} \
+                --depth ${DEPTH}
+done

visualize.py

@@ -104,21 +104,21 @@ def plot_confusion_matrix(y_true, y_pred, path,
     """
     plt.clf()
     cm = confusion_matrix(y_true, y_pred)
-    plt.imshow(cm, interpolation='nearest', cmap=cmap)
-    plt.title(title)
-    plt.colorbar()
-    tick_marks = np.arange(len(classes))
-    plt.xticks(tick_marks, classes, rotation=45)
-    plt.yticks(tick_marks, classes)
 
     if normalize:
         cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]
         print("Normalized confusion matrix")
     else:
         print('Confusion matrix, without normalization')
-
     print(cm)
 
+    plt.imshow(cm, interpolation='nearest', cmap=cmap)
+    plt.title(title)
+    plt.colorbar()
+    tick_marks = np.arange(len(classes))
+    plt.xticks(tick_marks, classes, rotation=45)
+    plt.yticks(tick_marks, classes)
+
     thresh = cm.max() / 2.
     for i, j in ((i, j) for i in range(cm.shape[0]) for j in range(cm.shape[1])):
         plt.text(j, i, cm[i, j],