Deep Embedding Clustering (DEC) in Tensorflow
Tensorflow implementation of Unsupervised Deep Embedding for Clustering Analysis.
Installation
>>> pip3 install -r requirements.txtTraining
usage: train.py [-h] [--batch-size BATCH_SIZE] [--gpu-index GPU_INDEX]
optional arguments:
-h, --help show this help message and exit
--batch-size BATCH_SIZE
Train Batch Size
--gpu-index GPU_INDEX
GPU Index NumberVisualize
The inference.py returns the latent representation ($z$), and exports the z.tsv, meta.tsv (label information).
usage: inference.py [-h] [--gpu-index GPU_INDEX]
optional arguments:
-h, --help show this help message and exit
--gpu-index GPU_INDEX
GPU Index NumberFor visualization, we use t-SNE by importing z.tsv, meta.tsv into Tensorboard.
The visualization using MNIST shows as follow.
