Adding in new trained models, as well as new tools for reviewing the results.

Added in testing flow for testing our unfininshed/finished models.

Also adding a test dataset with one picture of every pokemon in the game.

ShowIncorrectImages.py

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+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+import matplotlib.image as mpimg
+from pprint import pprint
+
+df = pd.read_csv("sub1_non_transfer.csv")
+df2 = pd.read_csv("poke_evos.csv")
+
+evos = []
+
+for index, row in df2.iterrows():
+    print(row)
+    s = ""
+    s+=row["stage1"] if not pd.isnull(row["stage1"]) else ""
+    s+=row["stage2"] if not pd.isnull(row["stage2"]) else ""
+    s+=row["stage3"] if not pd.isnull(row["stage3"]) else ""
+    evos.append(s.lower().replace(" ", "-").rstrip())
+
+
+incorrect = df[df["prediction"]!= df["true_val"]]
+
+total_same_fam = 0
+# TODO: Add in support for figuring out if the pokemon are related/evolutions of one another
+for index, row in incorrect.iterrows():
+    img = mpimg.imread("./SingleImageTestSet/" + row['fname'])
+    imgplot = plt.imshow(img)
+    title = f"Predicted - {row['prediction']}, Actual - {row['true_val']}"
+    for evo in evos:
+        if row['prediction'] in evo and row['true_val'] in evo:
+            title+=f"\n same family name detected - {evo}"
+            total_same_fam+=1
+    plt.title(title)
+    plt.show()
+
+
+print(f"The total number of incorrect entries from same families is {total_same_fam} - {total_same_fam/len(incorrect)}")