TSMC course materials of deep learning.
Setup your python environment. We recommend to use python3.
sudo pip3 install -r requirements.txt
Use BMM or GMM to fit MNIST dataset (handwritten digits), and then use the trained model to perform classification tasks.
Demo_BMM.ipynb demos how to use Bernoalli mixture model.
Demo_GMM.ipynb demos how to use Gaussian mixture model.
Please refer to mixture.py, bmm.py, gmm.py for detailed implementations.
kmeans.py perform kmeans clustering on MNIST dataset.
python kmeans.py --path=[Path to MNIST dataset directory. Default to "../MNIST".]
--k=[Number of cluster center. Default to 10.]
--output=[File path to save cluster centers. Default to "kmeans.dat".]
--verbose=[True | False. Default to False.]
Note: Use python3 to run the code.
Neural network for MNIST and embedding visualization
In your code:
tf.summary.FileWriter('your path to log dir', ...)tensorboard command:
tensorboard --logdir <your path to log dir> --port <your port (defalut:6006)>
In this notebook, we show how to train a neural network to do multiple-class classification in MNIST dataset. Since the url in tesorflow example is broken, please download the dataset from here.
Please change the data directory path to your own.
parser.add_argument('--data_dir', type=str,
default='/home/tommy8054/pythonPlayground/MNIST_data/', # here!
help='Directory for storing input data')Put your log files to desired directory.
parser.add_argument('--log_dir', type=str,
default='/tmp/tensorflow/mnist/logs/mnist_with_summaries', # here!
help='Summaries log directory')Show training detail or embedding visiualization. True for training detail and False for embedding visiualization.
parser.add_argument('--save_log', type=bool, default=False, # here!
help='Whether save log file or not')Sparse coding using neural network
In this section, we demonstrate that sparsity constrain leads to sparse feature while training.
This is a simple example of denoising autoencoder on MNIST dataset. Pretrained weights are put in net_pretrained/dae