python之如何使用tensorflow保存DNN模型

我有训练 DNN 网络的代码。我不想每次都训练这个网络，因为它使用了太多时间。如何保存模型？

def train_model(filename, validation_ratio=0.): 
    # define model to be trained 
    columns = [tf.contrib.layers.real_valued_column(str(col), 
                                                    dtype=tf.int8) 
               for col in FEATURE_COLS] 
    classifier = tf.contrib.learn.DNNClassifier( 
        feature_columns=columns, 
        hidden_units=[100, 100], 
        n_classes=N_LABELS, 
        dropout=0.3) 
 
    # load and split data 
    print( 'Loading training data.') 
    data = load_batch(filename) 
    overall_size = data.shape[0] 
    learn_size = int(overall_size * (1 - validation_ratio)) 
    learn, validation = np.array_split(data, [learn_size]) 
    print( 'Finished loading data. Samples count = {}'.format(overall_size)) 
 
    # learning 
    print( 'Training using batch of size {}'.format(learn_size)) 
    classifier.fit(input_fn=lambda: pipeline(learn), 
                   steps=learn_size) 
 
    if validation_ratio > 0: 
        validate_model(classifier, learn, validation) 
 
    return classifier 

运行此函数后，我得到一个我要保存的 DNNClassifier。