Tensorflow One Hot Encoder ?

      For TensorFlow 0.8, There is a native one-hot op and tf.one_hot that can convert a set of sparse labels to a dense one-hot representation.

      This is an inclusion to tf.nn.sparse_softmax_cross_entropy_with_logits, For some cases Instead of converting them to one-hot let compute the cross entropy directly on the sparse labels.

When you want to do it in old way by using 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 labels, is a one-hot matrix of batch_size x num_labels.

In Tensorflow, the tf.one_hot() is easy to use.

Here, the depth=4 and 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)

Note that when you provide index=-1 it will get all zeros in your one-hot vector

A simple way to one-hot encoder is:

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)