TensorFlow - how to import data with multiple labels - tensorflow

Question

I'm trying to create a model in TensorFlow which predicts ideal item for a user by predicting a vector of numbers.
I have created a dataset in Spark and saved it as a TFRecord using Spark TensorFlow connector.
In the dataset, I have several hundreds of features and 20 labels in each row. For easier manipulation, I have given every column a prefix 'feature_' or 'label_'.
Now I'm trying to write input function for TensorFlow, but I can't figure out how to parse the data.
So far I have written this:
def dataset_input_fn():
path = ['data.tfrecord']
dataset = tf.data.TFRecordDataset(path)
def parser(record):
example = tf.train.Example()
example.ParseFromString(record)
# TODO: no idea what to do here
# features = parsed["features"]
# label = parsed["label"]
# return features, label
dataset = dataset.map(parser)
dataset = dataset.shuffle(buffer_size=10000)
dataset = dataset.batch(32)
dataset = dataset.repeat(100)
iterator = dataset.make_one_shot_iterator()
features, labels = iterator.get_next()
return features, labels
How can I split the Example into a feature set and a label set? I have tried to split the Example into two parts, but there is no way to even access it. The only way I have managed to access it is by printing the example out, which gives me something like this.
features {
...
feature {
key: "feature_wishlist_hour"
value {
int64_list {
value: 0
}
}
}
feature {
key: "label_emb_1"
value {
float_list {
value: 0.4
}
}
}
feature {
key: "label_emb_2"
value {
float_list {
value: 0.8
}
}
}
...
}

Answer 1

Your parser function should be similar to how you constructed the example proto. In your case its should be something similar to:
# example proto decode
def parser(example_proto):
keys_to_features = {'feature_wishlist_hour':tf.FixedLenFeature((), tf.int64),
'label_emb_1': tf.FixedLenFeature((), tf.float32),
'label_emb_2': tf.FixedLenFeature((), tf.float32)}
parsed_features = tf.parse_single_example(example_proto, keys_to_features)
return parsed_features['feature_wishlist_hour'], (parsed_features['label_emb_1'], parsed_features['label_emb_2'])
EDIT: From the comments it seems you are encoding each of the features as key, value pair, which is not right. Check this answer: Numpy to TFrecords: Is there a more simple way to handle batch inputs from tfrecords? on how to write it in a proper way.