Tensorflow One Hot Encoder?

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.

numpy does it!

import numpy as np
np.eye(n_labels)[target_vector]

As of TensorFlow 0.8, there is now a native one-hot op, tf.one_hot that can convert a set of sparse labels to a dense one-hot representation. This is in addition to tf.nn.sparse_softmax_cross_entropy_with_logits, which can in some cases let you compute the cross entropy directly on the sparse labels instead of converting them to one-hot.

Previous answer, in case you want to do it the old way: @Salvador's answer is correct - there (used to be) no native op to do it. Instead of doing it in numpy, though, you can do it natively in tensorflow using the sparse-to-dense operators:

num_labels = 10

# label_batch is a tensor of numeric labels to process
# 0 <= label < num_labels

sparse_labels = tf.reshape(label_batch, [-1, 1])
derived_size = tf.shape(label_batch)[0]
indices = tf.reshape(tf.range(0, derived_size, 1), [-1, 1])
concated = tf.concat(1, [indices, sparse_labels])
outshape = tf.pack([derived_size, num_labels])
labels = tf.sparse_to_dense(concated, outshape, 1.0, 0.0)

The output, labels, is a one-hot matrix of batch_size x num_labels.

Note also that as of 2016-02-12 (which I assume will eventually be part of a 0.7 release), TensorFlow also has the tf.nn.sparse_softmax_cross_entropy_with_logits op, which in some cases can let you do training without needing to convert to a one-hot encoding.

Edited to add: At the end, you may need to explicitly set the shape of labels. The shape inference doesn't recognize the size of the num_labels component. If you don't need a dynamic batch size with derived_size, this can be simplified.

Edited 2016-02-12 to change the assignment of outshape per comment below.

A simple and short way to one-hot encode any integer or list of intergers:

a = 5 
b = [1, 2, 3]
# one hot an integer
one_hot_a = tf.nn.embedding_lookup(np.identity(10), a)
# one hot a list of integers
one_hot_b = tf.nn.embedding_lookup(np.identity(max(b)+1), b)