gsdr

Generative Sparse Distributed Representations, a fast generative model

Generative Sparse Distributed Representations, a fast generative model written in Python (Original C++ implementation https://github.com/222464/GSDR)

Dependencies

Python 3
Python libraries
- numpy

Installation

pip install gsdr or clone and python setup.py install

Usage

(More extensive examples and IPython notebooks can be found in examples/)

With labeled data:

data, labels = ...

num_labels = 10

# Data: (batches, num_features)
# Labels: (batches,) (contains numbers from 0 to num_labels-1, eg. 10 for MNIST)

# Build the GSDR network (only one layer for now)
gsdr = GSDRStack()
gsdr.add(input_count=data.shape[1], hidden_count=256, sparsity=0.1, forced_latent_count=num_labels)

forced_latents = np.eye(num_labels)

# Train once for each data point
for i in range(data.shape[0]):
    gsdr.train(data[i], forced_latents={0: forced_latents[labels[i]]})

# Generate one example for each label
for i in range(num_labels):
    generated = gsdr.generate(forced_latents={0: forced_latents[i]})

With unlabeled data:

data = ...

# Data: (batches, num_features)

# Build the GSDR network (only one layer for now)
gsdr = GSDRStack()
gsdr.add(input_count=data.shape[1], hidden_count=256, sparsity=0.1)

# Train once for each data point
for i in range(data.shape[0]):
    gsdr.train(data[i])

states = np.eye(hidden_count)

# Generate one example for each one-hot state
for i in range(hidden_count):
    generated = gsdr.generate(states[i])