diff --git a/DATA_MNIST/PyTorch_Non_Spike_Network/Error.png b/DATA_MNIST/PyTorch_Non_Spike_Network/Error.png
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diff --git a/DATA_MNIST/PyTorch_Non_Spike_Network/events.out.tfevents.1651325827.fedora.115860.0 b/DATA_MNIST/PyTorch_Non_Spike_Network/events.out.tfevents.1651325827.fedora.115860.0
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diff --git a/DATA_MNIST/PyTorch_Non_Spike_Network/plot.py b/DATA_MNIST/PyTorch_Non_Spike_Network/plot.py
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+++ b/DATA_MNIST/PyTorch_Non_Spike_Network/plot.py
@@ -0,0 +1,31 @@
+import os
+
+os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3"
+
+import numpy as np
+import matplotlib.pyplot as plt
+from tensorboard.backend.event_processing import event_accumulator
+
+filename: str = "events.out.tfevents.1651325827.fedora.115860.0"
+
+acc = event_accumulator.EventAccumulator(filename)
+acc.Reload()
+
+# What is available?
+# available_scalar = acc.Tags()["scalars"]
+# print("Available Scalars")
+# print(available_scalar)
+
+which_scalar: str = "Test Number Correct"
+te = acc.Scalars(which_scalar)
+
+temp: list = []
+for te_item in te:
+    temp.append((te_item[1], te_item[2]))
+temp_np = np.array(temp)
+
+plt.semilogy(temp_np[:, 0], (1.0 - (temp_np[:, 1] / 10000)) * 100)
+plt.xlabel("Epochs")
+plt.ylabel("Error [%]")
+plt.savefig("Error.png")
+plt.show()