S676951 2017-12-05 13:00 采纳率: 100%
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如何解决Tensorflow数据集太大的问题?

将稀疏矩阵压缩时用的tensorflow.sparse_to_tense函数,报错Cannot create a tensor proto whose content is larger than 2GB,这个问题要怎么解决啊?老师给我们的数据集最大的一个有10GB,才接触TensorFlow,不知道怎么处理,麻烦大神们帮帮忙!

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  • COCO_AS 2017-12-05 13:42
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    这里有解决办法
    https://stackoverflow.com/questions/38087342/use-large-dataset-in-tensorflow

    搬过来供你参考

    Do not load data to constant, it will be part of your computational graph.

    You should rather:

    Create an op which is loading your data in stream fashion
    Load data in python part, and use feed_dict to pass the batch into the graph
    

    For TensorFlow 1.x and Python 3, there is my simple solution:

     X_init = tf.placeholder(tf.float32, shape=(m_input, n_input))
    X = tf.Variable(X_init)
    sess.run(tf.global_variables_initializer(), feed_dict={X_init: data_for_X})
    
    

    In practice, you will mostly specify Graph and Session for continuous computation, this following code will help you:

     my_graph = tf.Graph()
    sess = tf.Session(graph=my_graph)
    with my_graph.as_default():
        X_init = tf.placeholder(tf.float32, shape=(m_input, n_input))
        X = tf.Variable(X_init)
        sess.run(tf.global_variables_initializer(), feed_dict={X_init: data_for_X})
        .... # build your graph with X here
    .... # Do some other things here
    with my_graph.as_default():
        output_y = sess.run(your_graph_output, feed_dict={other_placeholder: other_data})
    
    本回答被题主选为最佳回答 , 对您是否有帮助呢?
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