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Variational inference/Bayesian neural network: fixed data dims in ann_input/ann_output #506

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Jan 25, 2023
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Variational inference/Bayesian neural network: fixed data dims in ann_input/ann_output #506

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fixed data dims
earlbellinger Jan 25, 2023
733af95
pre-commit
OriolAbril Jan 25, 2023
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294 changes: 221 additions & 73 deletions examples/variational_inference/bayesian_neural_network_advi.ipynb
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6 changes: 4 additions & 2 deletions examples/variational_inference/bayesian_neural_network_advi.myst.md
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Original file line number Diff line number Diff line change
Expand Up @@ -130,8 +130,8 @@ def construct_nn(ann_input, ann_output):
# "obs_id": np.arange(X_train.shape[0]),
}
with pm.Model(coords=coords) as neural_network:
ann_input = pm.Data("ann_input", X_train, mutable=True)
ann_output = pm.Data("ann_output", Y_train, mutable=True)
ann_input = pm.Data("ann_input", X_train, mutable=True, dims=("obs_id", "train_cols"))
ann_output = pm.Data("ann_output", Y_train, mutable=True, dims="obs_id")

# Weights from input to hidden layer
weights_in_1 = pm.Normal(
Expand All @@ -157,6 +157,7 @@ def construct_nn(ann_input, ann_output):
act_out,
observed=ann_output,
total_size=Y_train.shape[0], # IMPORTANT for minibatches
dims="obs_id",
)
return neural_network

Expand Down Expand Up @@ -340,6 +341,7 @@ You might argue that the above network isn't really deep, but note that we could

- This notebook was originally authored as a [blog post](https://twiecki.github.io/blog/2016/06/01/bayesian-deep-learning/) by Thomas Wiecki in 2016
- Updated by Chris Fonnesbeck for PyMC v4 in 2022
- Updated by Oriol Abril-Pla and Earl Bellinger in 2023

## Watermark

Expand Down