diff --git a/code/analyze.py b/code/analyze.py
index c965dd7..13540e7 100644
--- a/code/analyze.py
+++ b/code/analyze.py
@@ -106,7 +106,7 @@ def latex_table_certified_accuracy(outfile: str, radius_start: float, radius_sto
         f.write("& $r = {:.3}$".format(radius))
     f.write("\\\\\n")
 
-    f.write("\midrule\n")
+    f.write(r"\midrule\n")
 
     for i, method in enumerate(methods):
         f.write(method.legend)
@@ -153,54 +153,54 @@ def markdown_table_certified_accuracy(outfile: str, radius_start: float, radius_
 if __name__ == "__main__":
     latex_table_certified_accuracy(
         "analysis/latex/vary_noise_cifar10", 0.25, 1.5, 0.25, [
-            Line(ApproximateAccuracy("data/certify/cifar10/resnet110/noise_0.12/test/sigma_0.12"), "$\sigma = 0.12$"),
-            Line(ApproximateAccuracy("data/certify/cifar10/resnet110/noise_0.25/test/sigma_0.25"), "$\sigma = 0.25$"),
-            Line(ApproximateAccuracy("data/certify/cifar10/resnet110/noise_0.50/test/sigma_0.50"), "$\sigma = 0.50$"),
-            Line(ApproximateAccuracy("data/certify/cifar10/resnet110/noise_1.00/test/sigma_1.00"), "$\sigma = 1.00$"),
+            Line(ApproximateAccuracy("data/certify/cifar10/resnet110/noise_0.12/test/sigma_0.12"), r"$\sigma = 0.12$"),
+            Line(ApproximateAccuracy("data/certify/cifar10/resnet110/noise_0.25/test/sigma_0.25"), r"$\sigma = 0.25$"),
+            Line(ApproximateAccuracy("data/certify/cifar10/resnet110/noise_0.50/test/sigma_0.50"), r"$\sigma = 0.50$"),
+            Line(ApproximateAccuracy("data/certify/cifar10/resnet110/noise_1.00/test/sigma_1.00"), r"$\sigma = 1.00$"),
         ])
     markdown_table_certified_accuracy(
         "analysis/markdown/vary_noise_cifar10", 0.25, 1.5, 0.25, [
-            Line(ApproximateAccuracy("data/certify/cifar10/resnet110/noise_0.12/test/sigma_0.12"), "σ = 0.12"),
-            Line(ApproximateAccuracy("data/certify/cifar10/resnet110/noise_0.25/test/sigma_0.25"), "σ = 0.25"),
-            Line(ApproximateAccuracy("data/certify/cifar10/resnet110/noise_0.50/test/sigma_0.50"), "σ = 0.50"),
-            Line(ApproximateAccuracy("data/certify/cifar10/resnet110/noise_1.00/test/sigma_1.00"), "σ = 1.00"),
+            Line(ApproximateAccuracy("data/certify/cifar10/resnet110/noise_0.12/test/sigma_0.12"), r"σ = 0.12"),
+            Line(ApproximateAccuracy("data/certify/cifar10/resnet110/noise_0.25/test/sigma_0.25"), r"σ = 0.25"),
+            Line(ApproximateAccuracy("data/certify/cifar10/resnet110/noise_0.50/test/sigma_0.50"), r"σ = 0.50"),
+            Line(ApproximateAccuracy("data/certify/cifar10/resnet110/noise_1.00/test/sigma_1.00"), r"σ = 1.00"),
         ])
     latex_table_certified_accuracy(
         "analysis/latex/vary_noise_imagenet", 0.5, 3.0, 0.5, [
-            Line(ApproximateAccuracy("data/certify/imagenet/resnet50/noise_0.25/test/sigma_0.25"), "$\sigma = 0.25$"),
-            Line(ApproximateAccuracy("data/certify/imagenet/resnet50/noise_0.50/test/sigma_0.50"), "$\sigma = 0.50$"),
-            Line(ApproximateAccuracy("data/certify/imagenet/resnet50/noise_1.00/test/sigma_1.00"), "$\sigma = 1.00$"),
+            Line(ApproximateAccuracy("data/certify/imagenet/resnet50/noise_0.25/test/sigma_0.25"), r"$\sigma = 0.25$"),
+            Line(ApproximateAccuracy("data/certify/imagenet/resnet50/noise_0.50/test/sigma_0.50"), r"$\sigma = 0.50$"),
+            Line(ApproximateAccuracy("data/certify/imagenet/resnet50/noise_1.00/test/sigma_1.00"), r"$\sigma = 1.00$"),
         ])
     markdown_table_certified_accuracy(
         "analysis/markdown/vary_noise_imagenet", 0.5, 3.0, 0.5, [
-            Line(ApproximateAccuracy("data/certify/imagenet/resnet50/noise_0.25/test/sigma_0.25"), "σ = 0.25"),
-            Line(ApproximateAccuracy("data/certify/imagenet/resnet50/noise_0.50/test/sigma_0.50"), "σ = 0.50"),
-            Line(ApproximateAccuracy("data/certify/imagenet/resnet50/noise_1.00/test/sigma_1.00"), "σ = 1.00"),
+            Line(ApproximateAccuracy("data/certify/imagenet/resnet50/noise_0.25/test/sigma_0.25"), r"σ = 0.25"),
+            Line(ApproximateAccuracy("data/certify/imagenet/resnet50/noise_0.50/test/sigma_0.50"), r"σ = 0.50"),
+            Line(ApproximateAccuracy("data/certify/imagenet/resnet50/noise_1.00/test/sigma_1.00"), r"σ = 1.00"),
         ])
     plot_certified_accuracy(
-        "analysis/plots/vary_noise_cifar10", "CIFAR-10, vary $\sigma$", 1.5, [
-            Line(ApproximateAccuracy("data/certify/cifar10/resnet110/noise_0.12/test/sigma_0.12"), "$\sigma = 0.12$"),
-            Line(ApproximateAccuracy("data/certify/cifar10/resnet110/noise_0.25/test/sigma_0.25"), "$\sigma = 0.25$"),
-            Line(ApproximateAccuracy("data/certify/cifar10/resnet110/noise_0.50/test/sigma_0.50"), "$\sigma = 0.50$"),
-            Line(ApproximateAccuracy("data/certify/cifar10/resnet110/noise_1.00/test/sigma_1.00"), "$\sigma = 1.00$"),
+        "analysis/plots/vary_noise_cifar10", r"CIFAR-10, vary $\sigma$", 1.5, [
+            Line(ApproximateAccuracy("data/certify/cifar10/resnet110/noise_0.12/test/sigma_0.12"), r"$\sigma = 0.12$"),
+            Line(ApproximateAccuracy("data/certify/cifar10/resnet110/noise_0.25/test/sigma_0.25"), r"$\sigma = 0.25$"),
+            Line(ApproximateAccuracy("data/certify/cifar10/resnet110/noise_0.50/test/sigma_0.50"), r"$\sigma = 0.50$"),
+            Line(ApproximateAccuracy("data/certify/cifar10/resnet110/noise_1.00/test/sigma_1.00"), r"$\sigma = 1.00$"),
         ])
     plot_certified_accuracy(
-        "analysis/plots/vary_train_noise_cifar_050", "CIFAR-10, vary train noise, $\sigma=0.5$", 1.5, [
-            Line(ApproximateAccuracy("data/certify/cifar10/resnet110/noise_0.25/test/sigma_0.50"), "train $\sigma = 0.25$"),
-            Line(ApproximateAccuracy("data/certify/cifar10/resnet110/noise_0.50/test/sigma_0.50"), "train $\sigma = 0.50$"),
-            Line(ApproximateAccuracy("data/certify/cifar10/resnet110/noise_1.00/test/sigma_0.50"), "train $\sigma = 1.00$"),
+        "analysis/plots/vary_train_noise_cifar_050", r"CIFAR-10, vary train noise, $\sigma=0.5$", 1.5, [
+            Line(ApproximateAccuracy("data/certify/cifar10/resnet110/noise_0.25/test/sigma_0.50"), r"train $\sigma = 0.25$"),
+            Line(ApproximateAccuracy("data/certify/cifar10/resnet110/noise_0.50/test/sigma_0.50"), r"train $\sigma = 0.50$"),
+            Line(ApproximateAccuracy("data/certify/cifar10/resnet110/noise_1.00/test/sigma_0.50"), r"train $\sigma = 1.00$"),
         ])
     plot_certified_accuracy(
-        "analysis/plots/vary_train_noise_imagenet_050", "ImageNet, vary train noise, $\sigma=0.5$", 1.5, [
-            Line(ApproximateAccuracy("data/certify/imagenet/resnet50/noise_0.25/test/sigma_0.50"), "train $\sigma = 0.25$"),
-            Line(ApproximateAccuracy("data/certify/imagenet/resnet50/noise_0.50/test/sigma_0.50"), "train $\sigma = 0.50$"),
-            Line(ApproximateAccuracy("data/certify/imagenet/resnet50/noise_1.00/test/sigma_0.50"), "train $\sigma = 1.00$"),
+        "analysis/plots/vary_train_noise_imagenet_050", r"ImageNet, vary train noise, $\sigma=0.5$", 1.5, [
+            Line(ApproximateAccuracy("data/certify/imagenet/resnet50/noise_0.25/test/sigma_0.50"), r"train $\sigma = 0.25$"),
+            Line(ApproximateAccuracy("data/certify/imagenet/resnet50/noise_0.50/test/sigma_0.50"), r"train $\sigma = 0.50$"),
+            Line(ApproximateAccuracy("data/certify/imagenet/resnet50/noise_1.00/test/sigma_0.50"), r"train $\sigma = 1.00$"),
         ])
     plot_certified_accuracy(
-        "analysis/plots/vary_noise_imagenet", "ImageNet, vary $\sigma$", 4, [
-            Line(ApproximateAccuracy("data/certify/imagenet/resnet50/noise_0.25/test/sigma_0.25"), "$\sigma = 0.25$"),
-            Line(ApproximateAccuracy("data/certify/imagenet/resnet50/noise_0.50/test/sigma_0.50"), "$\sigma = 0.50$"),
-            Line(ApproximateAccuracy("data/certify/imagenet/resnet50/noise_1.00/test/sigma_1.00"), "$\sigma = 1.00$"),
+        "analysis/plots/vary_noise_imagenet", r"ImageNet, vary $\sigma$", 4, [
+            Line(ApproximateAccuracy("data/certify/imagenet/resnet50/noise_0.25/test/sigma_0.25"), r"$\sigma = 0.25$"),
+            Line(ApproximateAccuracy("data/certify/imagenet/resnet50/noise_0.50/test/sigma_0.50"), r"$\sigma = 0.50$"),
+            Line(ApproximateAccuracy("data/certify/imagenet/resnet50/noise_1.00/test/sigma_1.00"), r"$\sigma = 1.00$"),
         ])
     plot_certified_accuracy(
         "analysis/plots/high_prob", "Approximate vs. High-Probability", 2.0, [
diff --git a/code/datasets.py b/code/datasets.py
index dc34462..19c42c5 100644
--- a/code/datasets.py
+++ b/code/datasets.py
@@ -70,7 +70,7 @@ def _imagenet(split: str) -> Dataset:
     elif split == "test":
         subdir = os.path.join(dir, "val")
         transform = transforms.Compose([
-            transforms.Scale(256),
+            transforms.Resize(256),
             transforms.CenterCrop(224),
             transforms.ToTensor()
         ])
diff --git a/code/train_utils.py b/code/train_utils.py
index 123ae8c..d8cd859 100644
--- a/code/train_utils.py
+++ b/code/train_utils.py
@@ -30,7 +30,7 @@ def accuracy(output, target, topk=(1,)):
 
         res = []
         for k in topk:
-            correct_k = correct[:k].view(-1).float().sum(0, keepdim=True)
+            correct_k = correct[:k].reshape(-1).float().sum(0, keepdim=True)
             res.append(correct_k.mul_(100.0 / batch_size))
         return res
 
diff --git a/requirements.txt b/requirements.txt
new file mode 100644
index 0000000..5ec3925
--- /dev/null
+++ b/requirements.txt
@@ -0,0 +1,67 @@
+ansicon==1.89.0
+asttokens==3.0.0
+blessed==1.21.0
+colorama==0.4.6
+comm==0.2.2
+contourpy==1.3.3
+cycler==0.12.1
+debugpy==1.8.14
+decorator==5.2.1
+executing==2.2.0
+filelock==3.13.1
+fonttools==4.59.1
+fsspec==2024.6.1
+gpustat==1.1.1
+ipykernel==6.29.5
+ipython==9.2.0
+ipython_pygments_lexers==1.1.1
+jedi==0.19.2
+Jinja2==3.1.4
+jinxed==1.3.0
+joblib==1.5.1
+jupyter_client==8.6.3
+jupyter_core==5.7.2
+kiwisolver==1.4.9
+MarkupSafe==2.1.5
+matplotlib==3.10.5
+matplotlib-inline==0.1.7
+mpmath==1.3.0
+nest-asyncio==1.6.0
+networkx==3.3
+numpy==1.26.4
+nvidia-ml-py==13.580.65
+opencv-python==4.11.0.86
+packaging==25.0
+pandas==2.3.2
+parso==0.8.4
+patsy==1.0.1
+pillow==11.0.0
+platformdirs==4.3.8
+prompt_toolkit==3.0.51
+psutil==7.0.0
+pure_eval==0.2.3
+Pygments==2.19.1
+pyparsing==3.2.3
+python-dateutil==2.9.0.post0
+pytz==2025.2
+pywin32==310
+pyzmq==26.4.0
+scikit-learn==1.7.0
+scipy==1.16.0
+seaborn==0.13.2
+setGPU==0.0.7
+six==1.17.0
+skorch==1.1.0
+stack-data==0.6.3
+statsmodels==0.14.5
+sympy==1.13.3
+tabulate==0.9.0
+threadpoolctl==3.6.0
+torch==2.8.0+cu126
+torchvision==0.23.0+cu126
+tornado==6.4.2
+tqdm==4.67.1
+traitlets==5.14.3
+typing_extensions==4.13.2
+tzdata==2025.2
+wcwidth==0.2.13