def my_model(features, labels, mode, params):
    '''
       model_fn指定函數，構建模型,，訓練等
       ---------------------------------
       Args:
          features: 輸入,，shape = (batch_size, 784)
          labels:   輸出,，shape = (batch_size, )
          mode:     str, 階段
          params:   dict, 超參數
    '''
    is_training = (mode == tf.estimator.ModeKeys.TRAIN)
    images = features
    images = tf.reshape(images, shape=[-1, params['image_size'], params['image_size'], 1])  # reshape (batch_size, img_size, img_size, 1)
    with tf.variable_scope("model"):
        embeddings = build_model(is_training, images, params)  # 簡歷模型
    if mode == tf.estimator.ModeKeys.PREDICT:     # 如果是預測階段,，直接返回得到embeddings
        predictions = {'embeddings': embeddings}
        return tf.estimator.EstimatorSpec(mode=mode, predictions=predictions)
    '''調用對應的triplet loss'''
    labels = tf.cast(labels, tf.int64)
    if params['triplet_strategy'] == 'batch_all':
        loss, fraction = batch_all_triplet_loss(labels, embeddings, margin=params['margin'], squared=params['squared'])
    elif params['triplet_strategy'] == 'batch_hard':
        loss = batch_hard_triplet_loss(labels, embeddings, margin=params['margin'], squared=params['squared'])
    else:
        raise ValueError("triplet_strategy 配置不正確: {}".format(params['triplet_strategy']))
    embedding_mean_norm = tf.reduce_mean(tf.norm(embeddings, axis=1))     # 這里計算了embeddings的二范數的均值 
    tf.summary.scalar("embedding_mean_norm", embedding_mean_norm)
    with tf.variable_scope("metrics"):
        eval_metric_ops = {'embedding_mean_norm': tf.metrics.mean(embedding_mean_norm)}
        if params['triplet_strategy'] == 'batch_all':
            eval_metric_ops['fraction_positive_triplets'] = tf.metrics.mean(fraction)
    if mode == tf.estimator.ModeKeys.EVAL:
        return tf.estimator.EstimatorSpec(mode, loss=loss, eval_metric_ops=eval_metric_ops)
    tf.summary.scalar('loss', loss)
    if params['triplet_strategy'] == "batch_all":
        tf.summary.scalar('fraction_positive_triplets', fraction)
    tf.summary.image('train_image', images, max_outputs=1)   # 1代表1個channel
    optimizer = tf.train.AdamOptimizer(learning_rate=params['learning_rate'])
    global_step = tf.train.get_global_step()
    if params['use_batch_norm']:
        '''如果使用BN,，需要估計batch上的均值和方差,，tf.get_collection(tf.GraphKeys.UPDATE_OPS)就可以得到
        tf.control_dependencies計算完之后再進行里面的操作
        '''
        with tf.control_dependencies(tf.get_collection(tf.GraphKeys.UPDATE_OPS)):
            train_op = optimizer.minimize(loss, global_step=global_step)
    else:
        train_op = optimizer.minimize(loss, global_step=global_step)
    return tf.estimator.EstimatorSpec(mode, loss=loss, train_op=train_op)