Generative Sparse Distributed Representations, a fast generative model written in Python (Original C++ implementation https://github.com/222464/GSDR)
- Python 3
- Python libraries
- numpy
pip install gsdr or clone and python setup.py install
(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=labels.shape[0])
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])