How to read (decode) tfrecords with tf.data API

I am not sure why storing the encoded png causes the evaluation to not work, but here is a possible way of working around the problem. Since you mentioned that you would like to use the tf.data way of creating input pipelines, I'll show how to use it with your toy example:

label = np.asarray([[1,2,3], [4,5,6]]).reshape(2, 3, -1) sample = np.stack((label + 200).reshape(2, 3, -1)) 

First, the data has to be saved to the TFRecord file. The difference from what you did is that the image is not encoded to png.

def _bytes_feature(value): return tf.train.Feature(bytes_list=tf.train.BytesList(value=[value])) writer = tf.python_io.TFRecordWriter("toy.tfrecord") example = tf.train.Example(features=tf.train.Features(feature={ 'label_raw': _bytes_feature(tf.compat.as_bytes(label.tostring())), 'sample_raw': _bytes_feature(tf.compat.as_bytes(sample.tostring()))})) writer.write(example.SerializeToString()) writer.close() 

What happens in the code above is that the arrays are turned into strings (1d objects) and then stored as bytes features.

Then, to read the data back using the tf.data.TFRecordDataset and tf.data.Iterator class:

filename = 'toy.tfrecord' # Create a placeholder that will contain the name of the TFRecord file to use data_path = tf.placeholder(dtype=tf.string, name="tfrecord_file") # Create the dataset from the TFRecord file dataset = tf.data.TFRecordDataset(data_path) # Use the map function to read every sample from the TFRecord file (_read_from_tfrecord is shown below) dataset = dataset.map(_read_from_tfrecord) # Create an iterator object that enables you to access all the samples in the dataset iterator = tf.data.Iterator.from_structure(dataset.output_types, dataset.output_shapes) label_tf, sample_tf = iterator.get_next() # Similarly to tf.Variables, the iterators have to be initialised iterator_init = iterator.make_initializer(dataset, name="dataset_init") with tf.Session() as sess: # Initialise the iterator passing the name of the TFRecord file to the placeholder sess.run(iterator_init, feed_dict={data_path: filename}) # Obtain the images and labels back read_label, read_sample = sess.run([label_tf, sample_tf]) 

The function _read_from_tfrecord() is:

def _read_from_tfrecord(example_proto): feature = { 'label_raw': tf.FixedLenFeature([], tf.string), 'sample_raw': tf.FixedLenFeature([], tf.string) } features = tf.parse_example([example_proto], features=feature) # Since the arrays were stored as strings, they are now 1d label_1d = tf.decode_raw(features['label_raw'], tf.int64) sample_1d = tf.decode_raw(features['sample_raw'], tf.int64) # In order to make the arrays in their original shape, they have to be reshaped. label_restored = tf.reshape(label_1d, tf.stack([2, 3, -1])) sample_restored = tf.reshape(sample_1d, tf.stack([2, 3, -1])) return label_restored, sample_restored 

Instead of hard-coding the shape [2, 3, -1], you could also store that too into the TFRecord file, but for simplicity I didn't do it.

I made a little gist with a working example.

Hope this helps!