test_model.py

from keras.models import load_model

classifier = load_model('Trained_model.h5')
classifier.evaluate()

#Prediction of single image
import numpy as np
from keras.preprocessing import image
img_name = input('Enter Image Name: ')
image_path = './predicting_data/{}'.format(img_name)
print('')

test_image = image.load_img(image_path, target_size=(200, 200))
test_image = image.img_to_array(test_image)
test_image = np.expand_dims(test_image, axis = 0)
result = classifier.predict(test_image)
#training_set.class_indices
print('Predicted Sign is:')
print('')
if result[0][0] == 1:
       return 'A'
elif result[0][1] == 1:
       return 'B'
elif result[0][2] == 1:
       return 'C'
elif result[0][3] == 1:
       return 'D'
elif result[0][4] == 1:
       return 'E'
elif result[0][5] == 1:
       return 'F'
elif result[0][6] == 1:
       return 'G'
elif result[0][7] == 1:
       return 'H'
elif result[0][8] == 1:
       return 'I'
elif result[0][9] == 1:
       return 'J'
elif result[0][10] == 1:
       return 'K'
elif result[0][11] == 1:
       return 'L'
elif result[0][12] == 1:
       return 'M'
elif result[0][13] == 1:
       return 'N'
elif result[0][14] == 1:
       return 'O'
elif result[0][15] == 1:
       return 'P'
elif result[0][16] == 1:
       return 'Q'
elif result[0][17] == 1:
       return 'R'
elif result[0][18] == 1:
       return 'S'
elif result[0][19] == 1:
       return 'T'
elif result[0][20] == 1:
       return 'U'
elif result[0][21] == 1:
       return 'V'
elif result[0][22] == 1:
       return 'W'
elif result[0][23] == 1:
       return 'X'
elif result[0][24] == 1:
       return 'Y'
elif result[0][25] == 1:
       return 'Z'