TensorFlow - Read all examples from a TFRecords at once?

Question

How do you read all examples from a TFRecords at once?

I\'ve been using tf.parse_single_example to read out individual examples using code similar to th

Answer 1

I don't know whether it is still a active topic. I'd like to share the best practice I know so far, it is a question a year ago though.

In the tensorflow, we have a very useful method for the problem like this -- read or iterate the whole bunch of input data and generate training for testing data set randomly. 'tf.train.shuffle_batch' can generate the dataset base on the input stream (like reader.read()) on you behave. Like for example, you can generate a set 1000 dataset, by providing argument list like this:

reader = tf.TFRecordReader()
_, serialized = reader.read(filename_queue)
features = tf.parse_single_example(
    serialized,
    features={
        'label': tf.FixedLenFeature([], tf.string),
        'image': tf.FixedLenFeature([], tf.string)
    }
)
record_image = tf.decode_raw(features['image'], tf.uint8)

image = tf.reshape(record_image, [500, 500, 1])
label = tf.cast(features['label'], tf.string)
min_after_dequeue = 10
batch_size = 1000
capacity = min_after_dequeue + 3 * batch_size
image_batch, label_batch = tf.train.shuffle_batch(
    [image, label], batch_size=batch_size, capacity=capacity, min_after_dequeue=min_after_dequeue
)