Tensorflow踩过的坑——Tf.data加载数据篇简要

59 阅读 0 评论 39 点赞

我是靠谱客的博主俭朴盼望，最近开发中收集的这篇文章主要介绍Tensorflow踩过的坑——Tf.data加载数据篇简要，觉得挺不错的，现在分享给大家，希望可以做个参考。

概述

Tensorflow踩过的坑——Tf.data数据加载篇

简要
- 问题一：
- - 问题描述：
  - 问题解决：
- 问题二：
- - 问题描述：
  - 问题解决：

简要

本系列主要记录在用Tensorflow框架训练深度学习模型时遇到过的一些问题。

问题一：

问题描述：

以Tf.data的方式进行多GPU训练，训练中采用tf.summary.image发现：所有的GPU中每个Batchsize的图片一样？
部分代码如下：

def train_model_multi_gpu(sess):
    inf_dir = os.path.join("./experiments/logs/", cfg.MODEL_TYPE)
    if not os.path.exists(inf_dir):
        os.makedirs(inf_dir)
        
    with tf.device('/cpu:0'):
        global_step = tf.get_variable('global_step', [],
                                      initializer=tf.constant_initializer(0),
                                      trainable=False)
        learning_rate = configure_learning_rate(global_step)
        optimizer = configure_optimizer(learning_rate)

        """ bucket by sequence length"""
        images_batch_bucket, labels_batch_bucket, im_width_batch_bucket = data_input_bucket()

        tower_grads = []
        with tf.variable_scope(tf.get_variable_scope()):
            for i in range(cfg.TRAIN.NUM_GPUS):
                with tf.device('/gpu:%d' % i):
                    """ create a dataset and iterator per GPU"""
                    with tf.name_scope('%s_%d' % ("TOWER", i)) as scope:
                    
                        """ loss by bucket """
                        im_batch = images_batch_bucket
                        label_batch = labels_batch_bucket
                        width_batch = im_width_batch_bucket
                        loss = tower_loss_bucket(scope, im_batch, label_batch, width_batch)
                        .......

问题解决：

参考链接：multi-gpu-towers-training-methods
上述代码块中images_batch_bucket, labels_batch_bucket, im_width_batch_bucket = data_input_bucket()采用tf的迭代器获取数据，在每个gpu外构建之外已获取，即tf.data方式中应该调用“Iterator.get_next() once per GPU to get multiple different batches”，修改后代码如下：

def train_model_multi_gpu(sess):

    inf_dir = os.path.join(cfg.MODEL_DIR, cfg.MODEL_TYPE)

    if not os.path.exists(inf_dir):
        os.makedirs(inf_dir)

    with tf.device('/cpu:0'):

        global_step = tf.get_variable('global_step', [],
                                      initializer=tf.constant_initializer(0),
                                      trainable=False)
        learning_rate = configure_learning_rate(global_step)
        optimizer = configure_optimizer(learning_rate)

        tower_grads = []
        with tf.variable_scope(tf.get_variable_scope()):
            for i in range(cfg.TRAIN.NUM_GPUS):

                with tf.device('/gpu:%d' % i):
                    
                    """ create a dataset and iterator per GPU"""
                    with tf.name_scope('%s_%d' % ("TOWER", i)) as scope:

                        """ bucket by sequence length"""
                        images_batch_bucket, labels_batch_bucket, im_width_batch_bucket = data_input_bucket()
                        loss = tower_loss_bucket(scope, images_batch_bucket, labels_batch_bucket, im_width_batch_bucket)

问题二：

问题描述：

模型可正常训练但是发现生成的checkpoint文件未保存所有的变量，即tf.train.saver.restore失败，未发现相关变量？

问题解决：

参考：grap & saver: no variables to save
save = tf.train.saver()写的位置不对，应放在所有graph构建完毕之后，以便“Gets all variables in graph”.

with graph.as_default():
    # [Variable and model creation goes here.]

    saver = tf.train.Saver()  # Gets all variables in `graph`.

with tf.Session(graph=graph) as sess:
    saver.restore(sess)
    # Do some work with the model....