v0.0.9

What's Changed

Add an option to specify which layers in the provided architecture will be treated as probabilistic. For example,

# Initialize NN architecture
architecture = nb.FlaxMLP(hidden_dims = [64, 32, 16, 8], target_dim=1)

# Make the first and output layers probabilistic and the rest deterministic
probabilistic_layer_names = ['Dense0', 'Dense4']

# Intitalize and train a PBNN model
model = nb.PartialBNN(architecture, probabilistic_layer_names=probabilistic_layer_names)
model.fit(X_measured, y_measured, num_warmup=1000, num_samples=1000)

Full Changelog: 0.0.7...0.0.9

v0.0.7

What's Changed

• Add ConvNets by @ziatdinovmax in #10
• Add option to set a custom prior over pre-trained priors by @ziatdinovmax in #13
• Add example with heteroskedastic BNN and PBNN by @ziatdinovmax in #17

Full Changelog: 0.0.5...0.0.7

v0.0.5

What's Changed

• Flax nets by @ziatdinovmax in #2
• Trained priors by @ziatdinovmax in #4
• Jax version by @ziatdinovmax in #5

Full Changelog: 0.0.2...0.0.5

v0.0.2

Merge pull request #1 from ziatdinovmax/hsk

Add heteroskedastic and uncertain input BNN