How to use a tfrecord file for training an autoencoder

[JohnMarie]

I used a tfrecord file to train the stacked autoencoder. My function is:

import tensorflow as tf
import numpy as np
import readers
import pre_precessing
from app_flag import FLAGS
from StackedAutoencoder import StackedAutoencoder


def write_and_encode(data_list, tfrecord_filename):
    writer = tf.python_io.TFRecordWriter(tfrecord_filename)
    for label, data_matrix in data_list:
        example = tf.train.Example(features=tf.train.Features(
            feature={
                "label": tf.train.Feature(int64_list=tf.train.Int64List(value=[label])),
                "data_raw": tf.train.Feature(bytes_list=tf.train.BytesList(value=[data_matrix.tostring()]))
            }
        ))
        writer.write(example.SerializeToString())

    writer.close()


def read_and_decode(tfrecord_filename):
    reader = tf.TFRecordReader()
    filename_queue = tf.train.string_input_producer([tfrecord_filename],)
    _, serialized_example = reader.read(filename_queue)
    feature = tf.parse_single_example(serialized_example,
                                      features={
                                          "label": tf.FixedLenFeature([], tf.int64),
                                          "data_raw": tf.FixedLenFeature([], tf.string)
                                      })
    data = tf.decode_raw(feature["data_raw"], tf.float64)
    data = tf.reshape(data, [FLAGS.image_rows, FLAGS.image_cols])
    return data, feature["label"]



def train_input_fn():

    tfrecord_file = "../resources/train_tfrecord"  
    dataset = tf.data.TFRecordDataset(tfrecord_file)
    dataset = dataset.map(parser)

    train_dataset = dataset.repeat(FLAGS.num_epochs).batch(FLAGS.batch_size)
    train_iterator = train_dataset.make_one_shot_iterator()

    features, labels = train_iterator.get_next()

    return features, labels


def parser(record_line):

    features = {
        "label": tf.FixedLenFeature([], tf.int64),
        "data_raw": tf.FixedLenFeature([], tf.string)
    }
    parsed = tf.parse_single_example(record_line, features=features)
    label = tf.cast(parsed["label"], tf.int32) - 1  
    data = tf.decode_raw(parsed["data_raw"], tf.float64)
    data = tf.reshape(data, [FLAGS.image_rows, FLAGS.image_cols])
    data = tf.cast(data, tf.float32)
    return data, label




def write_user_instances_to_tfrecord():
    
    users = ["0"+str(i) for i in range(1, 10)]
    users.extend([str(i) for i in range(10, 17)])
    users.extend(["32", "40", "41", "42", "43", "49", "50", "51"])

   
    instances = []
    for user in users:
        train_data = readers.read_user_files(user)
        for label, instance in train_data.items():
            instances.append((label, instance))

 
    formalized_instances = pre_precessing.extend_to_maxsize(instances)

   
    train_instances = formalized_instances[:100]
    write_and_encode(train_instances, "../resources/train_tfrecord")

 

def main():
    build_stacked_ae("../resources/train_tfrecord")

def build_stacked_ae(path):
    """
    Build the stacked auto-encoder neural network, and evaluate its performance
    """
    ############### Stacked Auto-Encoders ##############
    features,labels=train_input_fn()
    ae = StackedAutoencoder(features,labels,5)
    ae.create_autoencoder()
    result = ae.evaluate_autoencoder()
    return result[1] * 100
    print("Accuracy: %.2f%%" % (result[1] * 100))

if __name__ == "__main__":
    main() 

I can't fix the error any help please?