new model

pred.py.save

@@ -0,0 +1 @@
+q

pred_test.py

@@ -9,10 +9,10 @@ import numpy as np
 import tensorflow as tf
 
 
-model = tf.keras.models.load_model('model_pred/sign_char_detection_model')
+model = tf.keras.models.load_model('model_pred/VGG16_sign_char_detection_model')
 
 # Get the list of all files and directories
-path = "test_data"
+path = "test_data_own"
 dir_list = os.listdir(path)
  
 print(dir_list)
@@ -23,15 +23,18 @@ tf.keras.utils.load_img
 
 class_names = ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J', 'K', 'L', 'M', 'N', 'O', 'P', 'Q', 'R', 'S', 'T', 'U', 'V', 'W', 'X', 'Y', 'Z', 'del', 'nothing', 'space']
 
-img_height=256
-img_width=256
+img_height=224
+img_width=224
 actual=[]
 pred=[]
+
+
+img_size = [img_height, img_width]
 for i in dir_list:
     for j in os.listdir(path+'/'+i):
         file_path = path+'/'+i + '/' + j
         actual.append(i)
-        test_image = tf.keras.utils.load_img(file_path, target_size = (200, 200))
+        test_image = tf.keras.utils.load_img(file_path, target_size = img_size)
         test_image = tf.keras.utils.img_to_array(test_image)
         test_image = np.expand_dims(test_image, axis = 0)
         result = model.predict(test_image)