Mini Example

Probabilistic Models

Create a PGBN model:

from pydpm._model import PGBN

# create the model and deploy it on gpu or cpu
model = PGBN([128, 64, 32], device='gpu')
model.initial(train_data)
train_local_params = model.train(100, train_data)
train_local_params = model.test(100, train_data)
test_local_params = model.test(100, test_data)

# evaluate the model with classification accuracy
# the demo accuracy can achieve 0.8549
results = ACC(train_local_params.Theta[0], test_local_params.Theta[0], train_label, test_label, 'SVM')

# save the model after training
model.save()
  

Distribution Sampling Functions

Implement Gamma Distribution Sampling:

from pydpm._sampler import Basic_Sampler

sampler = Basic_Sampler('gpu')
a = sampler.gamma(np.ones(100)*5, 1, times=10)
b = sampler.gamma(np.ones([100, 100])*5, 1, times=10)
  