--- a
+++ b/Model_Testing.py
@@ -0,0 +1,90 @@
+import pandas as pd 
+import tensorflow as tf 
+import matplotlib as plt
+import glob
+import numpy as np 
+from tensorflow import keras
+from sklearn.utils import shuffle
+import sklearn.model_selection
+from sklearn.metrics import confusion_matrix, accuracy_score
+STEP_SIZE = 20
+SENSOR_NUM = 6
+
+df = pd.concat([pd.read_csv(f) for f in glob.glob('./test/*.csv')], ignore_index = True)
+print(df)
+
+Label = { 'STD':0, 'WAL':1, 'JOG':2 , 'JUM':3, 'FALL':4 , 'LYI':5,'RA':6} 
+class_names = { 0:'STD', 1:'WAL', 2:'JOG' , 3:'JUM', 4:'FALL', 5:'LYI',6:'RA'}
+
+dataSet = df[["acc_x", "acc_y", "acc_z", "gyro_x", "gyro_y", "gyro_z", "label"]]
+dataSet.label = [Label[item] for item in dataSet.label]
+
+
+print(dataSet)
+
+ 
+x = np.array(dataSet.drop(["label"],1))
+y = np.array(dataSet["label"])
+
+modDataset = []
+modTruth =[]
+
+for i in range(len(x)-STEP_SIZE):
+    temp = []
+    for j in range(i, i+STEP_SIZE):
+        temp.append(x[j])
+    modDataset.append(temp)
+
+for i in range(len(y)-STEP_SIZE):
+    temp = []
+    for j in range(i, i+STEP_SIZE):
+        temp.append(y[j])
+    
+    most_common_item = max(temp, key = temp.count)
+
+    modTruth.append(most_common_item)
+
+
+modDataset = np.array(modDataset).reshape(-1, STEP_SIZE, SENSOR_NUM)
+
+
+print(modDataset)
+print(modDataset.shape)
+model = tf.keras.models.load_model('./model/model_4')
+model.summary()
+
+pred = model.predict(modDataset)
+results = np.argmax(pred, axis=1)
+
+count = 0
+correct = 0
+for i in range(len(modDataset)) :
+    if class_names[y[i]] == class_names[results[i]]:
+        print("prediction: ", class_names[results[i]], "    actual: ", class_names[modTruth[i]], "prediction: Correct!!!" )
+        count += 1
+        correct += 1
+    else:
+        print("prediction: ", class_names[results[i]], "    actual: ", class_names[modTruth[i]], "prediction: Wrong :( " )
+        count += 1
+
+import seaborn as sn
+import pandas as pd
+import matplotlib.pyplot as plt
+from sklearn.metrics import confusion_matrix, accuracy_score
+print("success rate: ", correct/count*100)
+cm = confusion_matrix(modTruth, results)
+print(cm)
+
+df_cm = pd.DataFrame(cm, index=['STD', 'WAL', 'JOG', 'JUM', 'FALL' , 'LYI','RA'], 
+columns=['STD', 'WAL', 'JOG', 'JUM', 'FALL' , 'LYI','RA']) 
+# plt.figure(figsize=(10,7))
+sn.set(font_scale=1) # for label size
+sn.heatmap(df_cm, annot=True, annot_kws={"size": 10}) # font size
+
+from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score
+from sklearn.metrics import classification_report
+
+print(classification_report(modTruth, results, target_names=['STD', 'WAL', 'JOG', 'JUM', 'FALL' , 'LYI','RA']))
+
+
+plt.show()
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