text: from keras.models import load_model
  import cv2
  import numpy as np
  import glob
  
  model = load_model('fmodel.h5')
  
  model.compile(loss='categorical_crossentropy',
  
                optimizer='rmsprop',
  
                metrics=['accuracy'])
  
  dict={0:"capsule",1:"circle",2:"football"}
  
  for opt in ["capsule","circle","football"]:
      for name in glob.glob("./test2/"+opt+"/*"):
          img = cv2.imread(name)
          img = cv2.resize(img,(200,200))
          img = np.reshape(img,[1,200,200,3])
          classes = model.predict_classes(img)
          print(name,classes,"Shape :",dict[list(classes)[0]])
  