Create an int list feature to save as tfrecord in tensorflow?

Question

How can I create a tensorflow record from a list?

From the documentation here it seems possible. There's also this example where they convert a numpy array into a byte array using the .tostring() from numpy. However when I try to pass in:

labels = np.asarray([[1,2,3],[4,5,6]])
...
example = tf.train.Example(features=tf.train.Features(feature={
    'height': _int64_feature(rows),
    'width': _int64_feature(cols),
    'depth': _int64_feature(depth),
    'label': _int64_feature(labels[index]),
    'image_raw': _bytes_feature(image_raw)}))
writer.write(example.SerializeToString())

I get the error:

TypeError: array([1, 2, 3]) has type type 'numpy.ndarray', but expected one of: (type 'int', type 'long')

Which doesn't help me to figure out how to store a list of integers into the tfrecord. I've tried looking through the docs.

Answer 1

Int64List, BytesList and FloatList expect an iterator of the underlying elements (repeated field). In the case of your function _int64_feature you use a list as an iterator.

When you pass a scalar, your _int64_feature creates an array of one int64 element in it (exactly as expected). But when you pass an ndarray you create a list of one ndarray and pass it to a function which expects a list of int64.

So just remove construction of the array from your function: int64_list=tf.train.Int64List(value=value)