Does Tensorflow use only one hot encoding to store labels?

Question

I have just started working with Tensorflow, with Caffe it was super practical reading in the data in an efficient manner but with Tensorflow I see that I have to write data loading process myself, creating TFRecords, the batching, the multiple threats, handling those threads etc. So I started with an example, inception v3, as they handle the part to read in the data. I am new to Tensorflow and relatively new to Python, so I feel like I don't understand what is going on with this part exactly (I mean yes it extends the size of the labels to label_index * no of files -but- why? Is it creating one hot encoding for labels? Do we have to? Why doesn't it just extend as much for the length or files as each file have a label? Thx.

labels.extend([label_index] * len(filenames))
texts.extend([text] * len(filenames))
filenames.extend(filenames)

The whole code is here: https://github.com/tensorflow/models/tree/master/research/inception

The part mentioned is under data/build_image_data.py and builds image dataset from an existing dataset as images stored under folders (where foldername is the label): https://github.com/tensorflow/models/blob/master/research/inception/inception/data/build_image_data.py

Answer 1

Putting together what we discussed in the comments:

You have to one-hot encode because the network architecture requires you to, not because it's Tensorflow's demand. The network is a N-class classifier, so the final layer will have one neuron per class and you'll train the network to activate the neuron matching the class the sample belongs to. One-hot encoding the label is the first step in doing this.

About the human-readable labels, the code you're referring to is located in the _find_image_files function, which in turn is used by _process_dataset to transform the dataset from a set of folders to a set TfRecord files, which are a convenient input format type for Tensorflow .

The human-readable label string is included as a feature in the Example s inside the tfrecord files as an 'extra' (probably to simplify visualization of intermediate results during training), it is not strictly necessary for the dataset and will not be used in any way in the actual optimization of the network's parameters.