def test_cnn(x_data):
    output = create_cnn(4)
    saver = tf.train.Saver()
    with tf.Session() as sess:
        #載入訓練好的模型
        saver.restore(sess,"./model/cnn.model-2100")
        preject = tf.argmax(output,1)
        x_in = np.array(x_data)
        #keep_prob設定為1
        label = sess.run(preject,feed_dict={X:[x_in],keep_prob:1})
    return label
 

if __name__ == '__main__':
    isTrain = 2    
    if 1 == isTrain:        
        X = tf.placeholder(tf.float32,[None,200,150,3])
        Y = tf.placeholder(tf.float32,[None,4])
        
        keep_prob = tf.placeholder(tf.float32)
        train_cnn(xdata,ydata)
    if 2 == isTrain:
        #將測試資料放在相應的檔案中    
        path_list = ['./0','./1','./2','./3']
        for p in path_list:
            file_info = os.listdir(p)
            for file_name in file_info:
                x_data = read_test_data(p+'/'+file_name)
                if type(x_data) == type(None):
                    print('==>',p)
                    continue
                #沒有這句，會出現問題
                tf.reset_default_graph()  
                X = tf.placeholder(tf.float32,[None,200,150,3])
                keep_prob = tf.placeholder(tf.float32)
                l = test_cnn(x_data)
                label = ['girl','beauty girl','boy','handsome boy']               
                print(p,':',file_name,'====>',label[l[0]])