diff --git a/bindsnet/network/topology.py b/bindsnet/network/topology.py
index 8ef3cb7c..95b99d51 100644
--- a/bindsnet/network/topology.py
+++ b/bindsnet/network/topology.py
@@ -191,7 +191,7 @@ def compute(self, s: torch.Tensor) -> torch.Tensor:
 
     def compute_window(self, s: torch.Tensor) -> torch.Tensor:
         # language=rst
-        """"""
+        """ """
 
         if self.s_w == None:
             # Construct a matrix of shape batch size * window size * dimension of layer
diff --git a/examples/mnist/SOM_LM-SNNs.py b/examples/mnist/SOM_LM-SNNs.py
index e7726e0f..69b1be77 100644
--- a/examples/mnist/SOM_LM-SNNs.py
+++ b/examples/mnist/SOM_LM-SNNs.py
@@ -341,7 +341,7 @@
 
 pbar = tqdm(total=n_test)
 for step, batch in enumerate(test_dataset):
-    if step > n_test:
+    if step >= n_test:
         break
     # Get next input sample.
     inputs = {"X": batch["encoded_image"].view(int(time / dt), 1, 1, 28, 28)}
diff --git a/examples/mnist/eth_mnist.py b/examples/mnist/eth_mnist.py
index 30fd3411..c96aa90a 100644
--- a/examples/mnist/eth_mnist.py
+++ b/examples/mnist/eth_mnist.py
@@ -14,11 +14,7 @@
 from bindsnet.models import DiehlAndCook2015
 from bindsnet.network.monitors import Monitor
 from bindsnet.utils import get_square_weights, get_square_assignments
-from bindsnet.evaluation import (
-    all_activity,
-    proportion_weighting,
-    assign_labels,
-)
+from bindsnet.evaluation import all_activity, proportion_weighting, assign_labels
 from bindsnet.analysis.plotting import (
     plot_input,
     plot_spikes,
@@ -168,8 +164,8 @@
 # Train the network.
 print("\nBegin training.\n")
 start = t()
-labels = []
 for epoch in range(n_epochs):
+    labels = []
 
     if epoch % progress_interval == 0:
         print("Progress: %d / %d (%.4f seconds)" % (epoch, n_epochs, t() - start))
@@ -194,9 +190,7 @@
 
             # Get network predictions.
             all_activity_pred = all_activity(
-                spikes=spike_record,
-                assignments=assignments,
-                n_labels=n_classes,
+                spikes=spike_record, assignments=assignments, n_labels=n_classes
             )
             proportion_pred = proportion_weighting(
                 spikes=spike_record,
@@ -312,7 +306,7 @@
 
 pbar = tqdm(total=n_test)
 for step, batch in enumerate(test_dataset):
-    if step > n_test:
+    if step >= n_test:
         break
     # Get next input sample.
     inputs = {"X": batch["encoded_image"].view(int(time / dt), 1, 1, 28, 28)}