From a95b334249c063530887c1e4153092c61643de6c Mon Sep 17 00:00:00 2001
From: Deepak CH <deepak.23BCS10092@ms.sst.scaler.com>
Date: Fri, 2 Aug 2024 07:54:38 +0000
Subject: [PATCH 1/2] Fix Minibatch alignment in Bayesian Neural Network
example
---
.../bayesian_neural_network_advi.ipynb | 17 +++++++++++------
1 file changed, 11 insertions(+), 6 deletions(-)
diff --git a/examples/variational_inference/bayesian_neural_network_advi.ipynb b/examples/variational_inference/bayesian_neural_network_advi.ipynb
index 731de639a..bc46b320a 100644
--- a/examples/variational_inference/bayesian_neural_network_advi.ipynb
+++ b/examples/variational_inference/bayesian_neural_network_advi.ipynb
@@ -190,7 +190,7 @@
},
"outputs": [],
"source": [
- "def construct_nn(ann_input, ann_output):\n",
+ "def construct_nn():\n",
" n_hidden = 5\n",
"\n",
" # Initialize random weights between each layer\n",
@@ -204,9 +204,14 @@
" \"train_cols\": np.arange(X_train.shape[1]),\n",
" \"obs_id\": np.arange(X_train.shape[0]),\n",
" }\n",
+ " \n",
" with pm.Model(coords=coords) as neural_network:\n",
- " ann_input = pm.Data(\"ann_input\", X_train, dims=(\"obs_id\", \"train_cols\"))\n",
- " ann_output = pm.Data(\"ann_output\", Y_train, dims=\"obs_id\")\n",
+ " # Define minibatch variables\n",
+ " minibatch_x, minibatch_y = pm.Minibatch(X_train, Y_train, batch_size=50)\n",
+ " \n",
+ " # Define data variables using minibatches\n",
+ " ann_input = pm.Data(\"ann_input\", minibatch_x, mutable=True, dims=(\"obs_id\", \"train_cols\"))\n",
+ " ann_output = pm.Data(\"ann_output\", minibatch_y, mutable=True, dims=\"obs_id\")\n",
"\n",
" # Weights from input to hidden layer\n",
" weights_in_1 = pm.Normal(\n",
@@ -231,13 +236,13 @@
" \"out\",\n",
" act_out,\n",
" observed=ann_output,\n",
- " total_size=Y_train.shape[0], # IMPORTANT for minibatches\n",
+ " total_size=X_train.shape[0], # IMPORTANT for minibatches\n",
" dims=\"obs_id\",\n",
" )\n",
" return neural_network\n",
"\n",
- "\n",
- "neural_network = construct_nn(X_train, Y_train)"
+ "# Create the neural network model\n",
+ "neural_network = construct_nn()\n"
]
},
{
From 6c2effefc2fc1d21852593ffeeebe68e487edfeb Mon Sep 17 00:00:00 2001
From: Win Wang <wang.win0+git@gmail.com>
Date: Tue, 5 Nov 2024 11:18:46 -0500
Subject: [PATCH 2/2] Run: pre-commit run all-files
---
.../bayesian_neural_network_advi.ipynb | 7 ++++---
.../bayesian_neural_network_advi.myst.md | 16 +++++++++++-----
2 files changed, 15 insertions(+), 8 deletions(-)
diff --git a/examples/variational_inference/bayesian_neural_network_advi.ipynb b/examples/variational_inference/bayesian_neural_network_advi.ipynb
index bc46b320a..a21624b2f 100644
--- a/examples/variational_inference/bayesian_neural_network_advi.ipynb
+++ b/examples/variational_inference/bayesian_neural_network_advi.ipynb
@@ -204,11 +204,11 @@
" \"train_cols\": np.arange(X_train.shape[1]),\n",
" \"obs_id\": np.arange(X_train.shape[0]),\n",
" }\n",
- " \n",
+ "\n",
" with pm.Model(coords=coords) as neural_network:\n",
" # Define minibatch variables\n",
" minibatch_x, minibatch_y = pm.Minibatch(X_train, Y_train, batch_size=50)\n",
- " \n",
+ "\n",
" # Define data variables using minibatches\n",
" ann_input = pm.Data(\"ann_input\", minibatch_x, mutable=True, dims=(\"obs_id\", \"train_cols\"))\n",
" ann_output = pm.Data(\"ann_output\", minibatch_y, mutable=True, dims=\"obs_id\")\n",
@@ -241,8 +241,9 @@
" )\n",
" return neural_network\n",
"\n",
+ "\n",
"# Create the neural network model\n",
- "neural_network = construct_nn()\n"
+ "neural_network = construct_nn()"
]
},
{
diff --git a/examples/variational_inference/bayesian_neural_network_advi.myst.md b/examples/variational_inference/bayesian_neural_network_advi.myst.md
index 04dc6a4f1..0c3649a09 100644
--- a/examples/variational_inference/bayesian_neural_network_advi.myst.md
+++ b/examples/variational_inference/bayesian_neural_network_advi.myst.md
@@ -114,7 +114,7 @@ A neural network is quite simple. The basic unit is a [perceptron](https://en.wi
jupyter:
outputs_hidden: true
---
-def construct_nn(ann_input, ann_output):
+def construct_nn():
n_hidden = 5
# Initialize random weights between each layer
@@ -128,9 +128,14 @@ def construct_nn(ann_input, ann_output):
"train_cols": np.arange(X_train.shape[1]),
"obs_id": np.arange(X_train.shape[0]),
}
+
with pm.Model(coords=coords) as neural_network:
- ann_input = pm.Data("ann_input", X_train, dims=("obs_id", "train_cols"))
- ann_output = pm.Data("ann_output", Y_train, dims="obs_id")
+ # Define minibatch variables
+ minibatch_x, minibatch_y = pm.Minibatch(X_train, Y_train, batch_size=50)
+
+ # Define data variables using minibatches
+ ann_input = pm.Data("ann_input", minibatch_x, mutable=True, dims=("obs_id", "train_cols"))
+ ann_output = pm.Data("ann_output", minibatch_y, mutable=True, dims="obs_id")
# Weights from input to hidden layer
weights_in_1 = pm.Normal(
@@ -155,13 +160,14 @@ def construct_nn(ann_input, ann_output):
"out",
act_out,
observed=ann_output,
- total_size=Y_train.shape[0], # IMPORTANT for minibatches
+ total_size=X_train.shape[0], # IMPORTANT for minibatches
dims="obs_id",
)
return neural_network
-neural_network = construct_nn(X_train, Y_train)
+# Create the neural network model
+neural_network = construct_nn()
```
That's not so bad. The `Normal` priors help regularize the weights. Usually we would add a constant `b` to the inputs but I omitted it here to keep the code cleaner.