reader template: https://github.com/chiphuyen/tf-stanford-tutorials/blob/master/examples/05_csv_reader.py

reader.py

"""
"""

import tensorflow as tf

def batch_generator(filenames, BATCH_SIZE):
    """ filenames is the list of files you want to read from. 
    In this case, it contains only heart.csv
    """
    filename_queue = tf.train.string_input_producer(filenames)
    reader = tf.TextLineReader(skip_header_lines=1) # skip the first line in the file
    _, value = reader.read(filename_queue)

    # record_defaults are the default values in case some of our columns are empty
    #-------------
    # TODO
    # record_defaults =
    #-------------

    # read data
    content = tf.decode_csv(value, record_defaults=record_defaults) 

    # pack all features into a tensor
    #-------------
    # TODO
    # features =
    #-------------

    # assign label
    #-------------
    # TODO
    # label =
    #-------------

    # minimum number elements in the queue after a dequeue, used to ensure 
    # that the samples are sufficiently mixed
    min_after_dequeue = 10 * BATCH_SIZE

    # the maximum number of elements in the queue
    capacity = 20 * BATCH_SIZE

    # shuffle the data to generate BATCH_SIZE sample pairs
    data_batch, label_batch = tf.train.shuffle_batch([features, label], batch_size=BATCH_SIZE, 
                                        capacity=capacity, min_after_dequeue=min_after_dequeue)

    return data_batch, label_batch

def generate_batches(data_batch, label_batch):
    with tf.Session() as sess:
        coord = tf.train.Coordinator()
        threads = tf.train.start_queue_runners(coord=coord)
        for _ in range(10): # generate 10 batches
            features, labels = sess.run([data_batch, label_batch])
            print features
        coord.request_stop()
        coord.join(threads)