validation_steps=len(test_set))
model.save(self.model_save_path)
def test(self):
model = load_model(self.model_save_path)
image = load_img(self.test_image_path, target_size=(224, 224))
image1 = img_to_array(image)
image1 = image1.reshape(
(1, image1.shape[0], image1.shape[1], image1.shape[2]))
image1 = preprocess_input(image1)
yhat = model.predict(image1)
pred = np.argmax(yhat)
#change according to your dataset
if pred == 0:
prediction = 'satellite_image'
print(prediction)
plt.imshow(image)
else:
prediction = 'non_satellite_image'
print(prediction)
plt.imshow(image)
if __name__ == "__main__":
train_path = input('Enter the Input folder path:')
model_save_path = input('Enter the path to save model in terms of .h5')
test_image_path = input('Enter the image path to test:')
a = Xception(train_path, model_save_path, test_image_path)
b = a.train()
c = a.test()
