CODEWITHSUNDEEP
python-3.xmachine-learningtensorflow
Candlejack
Candlejack

Reputation: 73

How to correctly encode labels with tensorflow's one-hot encoding?

I've been trying to learn Tensorflow with python 3.6 and decided on building a facial recognition program using data from the University of Essex's face data base (http://cswww.essex.ac.uk/mv/allfaces/index.html). So far I've been following Tensorflow's MNIST Expert guide, but when I start testing, my accuracy is 0 for every epoch, so I know something is wrong. I feel most shaky on how I'm handling the labels, so I figure that's where the problem is.

The labels in the dataset are either numeric IDs, like 987323, or someone's name, like "fordj". My idea to deal with this was to create a "pre-encoding" encode_labels function, which gives each unique label in the test and training sets their own unique integer value. I checked to make sure each unique label in the test and train sets have the same unique value. It also returns a dictionary so that I can easily map back to the original label from the encoded version. If I don't do this step and pass the labels as I retrieve them (i.e "fordj"), I get an error saying

UnimplementedError (see above for traceback): Cast string to int32 is not supported [[Node: Cast = CastDstT=DT_INT32, SrcT=DT_STRING, _device="/job:localhost/replica:0/task:0/device:CPU:0"]]

The way I'm interpreting this is that since many of the labels are people's names, tensorflow can't convert a label like "fordj" to a tf.int32. The code to grab labels and paths is here:

def get_paths_and_labels(path):
    """ image_paths  :  list of relative image paths
        labels       :  mix of alphanumeric characters """
    image_paths = [path + image for image in os.listdir(path)]
    labels = [i.split(".")[-3] for i in image_paths]
    labels = [i.split("/")[-1] for i in labels]
    return image_paths, labels

def encode_labels(train_labels, test_labels):
    """ Assigns a numeric value to each label since some are subject's names """
    found_labels = []
    index = 0
    mapping = {}
    for i in train_labels:
        if i in found_labels:
            continue
        mapping[i] = index
        index += 1
        found_labels.append(i)
    return [mapping[i] for i in train_labels], [mapping[i] for i in test_labels], mapping

Here is how I assign my training and testing labels. I then want to use tensorflow's one-hot encoder to encode them again for me.

def main():
    # Grabs the labels and each image's relative path 
    train_image_paths, train_labels = get_paths_and_labels(TRAIN_PATH)
    # Smallish dataset so I can read it all into memory
    train_images = [cv2.imread(image) for image in train_image_paths]

    test_image_paths, test_labels = get_paths_and_labels(TEST_PATH)
    test_images = [cv2.imread(image) for image in test_image_paths]

    num_classes = len(set(train_labels))

    # Placeholders
    x = tf.placeholder(tf.float32, shape=[None, IMAGE_SIZE[0] * IMAGE_SIZE[1]])
    y_ = tf.placeholder(tf.float32, shape=[None, num_classes])
    x_image = tf.reshape(x, [-1, IMAGE_SIZE[0], IMAGE_SIZE[1], 1])

    # One-hot labels
    train_labels, test_labels, mapping = encode_labels(train_labels, test_labels)

    train_labels = tf.one_hot(indices=tf.cast(train_labels, tf.int32), depth=num_classes)
    test_labels = tf.one_hot(indices=tf.cast(test_labels, tf.int32), depth=num_classes)

I'm sure I'm doing something wrong. I know sklearn has a LabelEncoder, though I haven't tried it out yet. Thanks for any advice on this, all help is appreciated!

Upvotes: 1

Views: 2947

Answers (1)

nessuno
nessuno

Reputation: 27042

The way I'm interpreting this is that since many of the labels are people's names, tensorflow can't convert a label like "fordj" to a tf.int32.

You're right. Tensorflow can't do that. Instead, you can create a mapping function from a nome to a unique (and progressive) ID. Once you did that, you can correctly one-encode every numeric ID with its one-hot representation. You already have the relation between the numeric ID and the string label, hence you can do something like: 

train_labels, test_labels, mapping = encode_labels(train_labels, test_labels)
numeric_train_ids = [labels[idx] for idx in train_labels]
numeric_test_ids = [labels[idx] for idx in test_labels]     

one_hot_train_labels = tf.one_hot(indices=numeric_train_ids, depth=num_classes)
one_hot_test_labels = tf.one_hot(indices=numeric_test_ids, depth=num_classes)

Upvotes: 1

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