what will happen if an image for testing my cnn model which is trained to detect potholes and garbage receive an non pothole or non garbage image? - tensorflow

Question

I am using keras from tensorflow backend to detect potholes and garbage in my trained model. The output generated for detecting potholes or garbage works fine but when i give a random image of a car or bike or cat or human or building it identifies everything as a garbage class.what should i do now? what should be the optimal output for such cases. I am providig my code here.
#train
import numpy as np
np.random.seed(123) # for reproducibility
from keras.models import Sequential
from keras.layers import Dense, Dropout, Activation, Flatten
from keras.layers import Convolution2D, MaxPooling2D
from keras.utils import np_utils
from dataset_pothole import pothole
from keras.models import model_from_json
# 4. Load pre-shuffled MNIST data into train and test sets
(X_train, y_train), (X_test, y_test) = pothole.load_data()
# 5. Preprocess input data
X_train = X_train.reshape(X_train.shape[0], 50, 50, 3)
X_test = X_test.reshape(X_test.shape[0], 50, 50, 3)
X_train = X_train.astype('float32')
X_test = X_test.astype('float32')
X_train /= 255
X_test /= 255
# 6. Preprocess class labels
Y_train = np_utils.to_categorical(y_train, 2)
Y_test = np_utils.to_categorical(y_test, 2)
# 7. Define model architecture
model = Sequential()
model.add(Convolution2D(32, 3, 3, activation='relu', input_shape=(50, 50, 3)))
model.add(Convolution2D(32, 3, 3, activation='relu'))
model.add(MaxPooling2D(pool_size=(2,2)))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(128, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(3, activation='softmax'))
# 8. Compile model
model.compile(loss='categorical_crossentropy',
optimizer='adam',
metrics=['accuracy'])
# 9. Fit model on training data
model.fit(X_train, Y_train,
batch_size=32, nb_epoch=20, verbose=1)
# 10. Evaluate model on test data
score = model.evaluate(X_test, Y_test, verbose=0)
model_json = model.to_json()
with open("model.json", "w") as json_file:
json_file.write(model_json)
# serialize weights to HDF5
model.save_weights("model.h5")
print("Saved model to disk")
print('Test loss: ', score[0])
print('Test accuracy: ', score[1])
#evaluation
import numpy as np
np.random.seed(123) # for reproducibility
import keras
from keras.utils import np_utils
from keras.models import model_from_json
import os
from PIL import Image
from numpy import *
from keras.models import Sequential
from keras.layers import Dense, Dropout, Activation, Flatten
from keras.layers import Convolution2D, MaxPooling2D
img = input("Please enter the test filename:")
test = array(array(Image.open(img)).flatten())
print(test.shape)
X_test = test.reshape((1, 50, 50, 3))
print(X_test.shape)
json_file = open('model.json', 'r')
loaded_model_json = json_file.read()
json_file.close()
loaded_model = model_from_json(loaded_model_json)
# load weights into new model
loaded_model.load_weights("model.h5")
#loaded_model.compile(loss='categorical_crossentropy',
# optimizer='adam',
# metrics=['accuracy'])
prediction = loaded_model.predict_classes(X_test)
print(prediction)
print(loaded_model.predict(X_test))
if prediction==[1]:
print("Pothole")
elif prediction==[0]:
print("Garbage")
else:
print("Invalid Image!")