在TensorFlow的tf记录中存储/读取列表列表的最佳方法是什么？ 我试图将数据序列化为一维列表，然后在读取时将其重新整形为原始大小。但是，编码过程将永远存在。
写信给tf_record：
有问题的变量：word_data（shape = [nb_channels，1500]）
electrodes_coordinates=word_data['electrodes_coordinates']
electrodes_loc3=word_data['electrodes_loc3']
nb_electrodes=word_data['nb_electrodes']
label=word_data['label']
word_data=word_data['word']
#reshape word_data from list of list (nb_channel,nb_time points) to list (nb_channel*nb_timepoints)
word_data=np.reshape(word_data, [-1])
context = tf.train.Features(feature={
"word/word_id": _bytes_feature(word),
"word/nb_channels": _int64_feature(nb_electrodes),
"word/label": _int64_feature(int(label))
})
feature_lists = tf.train.FeatureLists(feature_list={
"word/electrode_x_coordinates":_float_feature_list(electrodes_coordinates[:,0]),
"word/electrode_y_coordinates":_float_feature_list(electrodes_coordinates[:,1]),
"word/electrode_z_coordinates":_float_feature_list(electrodes_coordinates[:,2]),
"word/electrode_location3":_int64_feature_list(loc3_to_id(electrodes_loc3,loc3_dict)),
"word/data": _float_feature_list(word_data)})
sequence_example = tf.train.SequenceExample(context=context ,feature_lists=feature_lists)
return sequence_example
从tf_record阅读：
context, sequence = tf.parse_single_sequence_example(serialized,
context_features={
nb_channels: tf.FixedLenFeature([], dtype=tf.int64),
label: tf.FixedLenFeature([], dtype=tf.int64)
},
sequence_features={
electrode_x_coordinates: tf.FixedLenSequenceFeature([], dtype=tf.float32),
electrode_y_coordinates: tf.FixedLenSequenceFeature([], dtype=tf.float32),
electrode_z_coordinates: tf.FixedLenSequenceFeature([], dtype=tf.float32),
electrode_location3: tf.FixedLenSequenceFeature([], dtype=tf.int64),
word_data: tf.FixedLenSequenceFeature([], dtype=tf.float32)
}
)
encoded_nb_channels = context[nb_channels]
encoded_label = context[label]
encoded_electrode_x_coordinates = sequence[electrode_x_coordinates]
encoded_electrode_y_coordinates = sequence[electrode_y_coordinates]
encoded_electrode_z_coordinates = sequence[electrode_z_coordinates]
encoded_electrode_location3 = sequence[electrode_location3]
encoded_word_data = sequence[word_data]