Tensorflow One Hot Encoder?

Question

Does tensorflow have something similar to scikit learn\'s one hot encoder for processing categorical data? Would using a placeholder of tf.string behave as categorical data

Answer 1

tf.one_hot() is available in TF and easy to use.

Lets assume you have 4 possible categories (cat, dog, bird, human) and 2 instances (cat, human). So your depth=4 and your indices=[0, 3]

import tensorflow as tf
res = tf.one_hot(indices=[0, 3], depth=4)
with tf.Session() as sess:
    print sess.run(res)

Keep in mind that if you provide index=-1 you will get all zeros in your one-hot vector.

Old answer, when this function was not available.

After looking though the python documentation, I have not found anything similar. One thing that strengthen my belief that it does not exist is that in their own example they write one_hot manually.

def dense_to_one_hot(labels_dense, num_classes=10):
  """Convert class labels from scalars to one-hot vectors."""
  num_labels = labels_dense.shape[0]
  index_offset = numpy.arange(num_labels) * num_classes
  labels_one_hot = numpy.zeros((num_labels, num_classes))
  labels_one_hot.flat[index_offset + labels_dense.ravel()] = 1
  return labels_one_hot

You can also do this in scikitlearn.