From d57d5f65f32349ab9fb7a00e5b261c9fb6c3f417 Mon Sep 17 00:00:00 2001
From: rink <rink@ssec.wisc.edu>
Date: Tue, 27 May 2025 15:17:03 -0500
Subject: [PATCH] add option to pass in absolute tolerance
---
modules/deeplearning/mc_dropout_regression.py | 18 +++++++++---------
1 file changed, 9 insertions(+), 9 deletions(-)
diff --git a/modules/deeplearning/mc_dropout_regression.py b/modules/deeplearning/mc_dropout_regression.py
index fbf1873f..924f57f9 100644
--- a/modules/deeplearning/mc_dropout_regression.py
+++ b/modules/deeplearning/mc_dropout_regression.py
@@ -8,15 +8,15 @@ def create_mnist_dataset():
return tf.keras.datasets.mnist.load_data()
-def create_toy_regression_dataset(xmin=-10., xmax=10, noise_std=.2):
- x_trn = np.linspace(-2.0, 2.0, 1000, dtype=np.float32)
- y_trn = np.sin(x_trn) + np.random.normal(0, noise_std, size=1000).astype(np.float32)
+def create_toy_regression_dataset(xmin=-10., xmax=10, num_points=1000, noise_std=.05):
+ x_trn = np.linspace(-2.0, 2.0, num_points, dtype=np.float32)
+ y_trn = np.sin(x_trn) + np.random.normal(0, noise_std, size=num_points).astype(np.float32)
- x_gt = np.linspace(xmin, xmax, 1000, dtype=np.float32)
+ x_gt = np.linspace(xmin, xmax, num_points, dtype=np.float32)
y_gt = np.sin(x_gt)
- x_tst = np.linspace(xmin, xmax, 1000, dtype=np.float32)
- y_tst = np.sin(x_tst) + np.random.normal(0, noise_std, size=1000).astype(np.float32)
+ x_tst = np.linspace(xmin, xmax, num_points, dtype=np.float32)
+ y_tst = np.sin(x_tst) + np.random.normal(0, noise_std, size=num_points).astype(np.float32)
return x_gt, y_gt, x_trn, y_trn, x_tst, y_tst
@@ -199,18 +199,18 @@ def predict(model, x, samples=20):
y_std = np.sqrt(y_variance)
return y_mean, y_std
-def run():
+def run(num_points=1000, samples=20):
xmin = -10.
xmax = 10.
- x_gt, y_gt, x_trn, y_trn, x_tst, y_tst = create_toy_regression_dataset(xmin=xmin, xmax=xmax, noise_std=0.05)
+ x_gt, y_gt, x_trn, y_trn, x_tst, y_tst = create_toy_regression_dataset(xmin=xmin, xmax=xmax, num_points=num_points, noise_std=0.05)
model = define()
model = train(x_trn[:, np.newaxis],
y_trn[:, np.newaxis],
model)
- yhat_mean, yhat_std = predict(model, x_tst[:, np.newaxis], samples=20)
+ yhat_mean, yhat_std = predict(model, x_tst[:, np.newaxis], samples=samples)
plot_regression_model_analysis(gt=(x_gt, y_gt),
trn=(x_trn, y_trn),
--
GitLab