tf.one_hot is one of the functions which can convert a set of sparse labels to a dense one-hot representation, as of TensorFlow 0.8. In some cases, tf.nn.sparse_softmax_cross_entropy_with_logits can be used to calculate cross-entropy directly on sparse labels instead of converting them to one-hot.In case you want to to do in the old primitive way refer to the following code which uses sparse-to-dense operator:
numLabel = 10
sprsLabel= tf.reshape(lBatch, [-1, 1])
deriveSize = tf.shape(lbatch)
i = tf.reshape(tf.range(0, deriveSize, 1), [-1, 1])
conc = tf.concat(1, [i, sprsLabel])
outshape = tf.pack([deriveSize, numLabel])
labels = tf.sparse_to_dense(conc, outshape, 1.0, 0.0)
This gives a label as an output which is a one-hot matric having a size equal to batchsize x numLabel.
Hope this answer helps.
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