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!

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})