diff --git a/1 - ImageGatherer.py b/1 - ImageGatherer.py
index 4e92693..932193c 100644
--- a/1 - ImageGatherer.py	
+++ b/1 - ImageGatherer.py	
@@ -5,6 +5,9 @@ import json
 from pprint import pprint
 from google_images_download import google_images_download
 
+total_per = 10
+form_increment = 1
+
 
 def create_forms_dict(df):
     poke_dict = {}
@@ -39,22 +42,26 @@ def process_pokemon_names(df):
     pprint(poke_dict)
     pokes_to_limits = []
     for pokemon, form_list in poke_dict.items():
-        if len(form_list) == 0:
-            print(pokemon)
-            pokes_to_limits.append((pokemon, 200))
+        print(pokemon)
+        num_forms = len(form_list)
+        if num_forms == 0:
+            pokes_to_limits.append((pokemon, total_per))
 
-        elif len(form_list) == 1:
-            pokes_to_limits.append((pokemon, 150))
-            pokes_to_limits.append((search_term(form_list[0]), 50))
+        elif num_forms == 1:
+            pokes_to_limits.append((pokemon, total_per - form_increment))
+            pokes_to_limits.append((search_term(form_list[0]), form_increment))
 
-        elif len(form_list) == 2:
-            pokes_to_limits.append((pokemon, 100))
+        elif num_forms == 2:
+            pokes_to_limits.append((pokemon, total_per - form_increment * num_forms))
             for form in form_list:
-                pokes_to_limits.append((search_term(form), 50))
+                pokes_to_limits.append((search_term(form), form_increment))
 
-        elif len(form_list) >= 3:
+        elif num_forms >= 3:
+            revised_increment = int(total_per / len(form_list))
             for form in form_list:
-                pokes_to_limits.append((search_term(form), int(200 / len(form_list))))
+                pokes_to_limits.append((pokemon, total_per - revised_increment * num_forms))
+
+                pokes_to_limits.append((search_term(form), revised_increment))
 
     return pokes_to_limits
 
diff --git a/3 - TestTrainSplit.py b/3 - TestTrainSplit.py
index b99f8a1..71b13ff 100644
--- a/3 - TestTrainSplit.py	
+++ b/3 - TestTrainSplit.py	
@@ -1,6 +1,6 @@
 import os
 from random import random
-from shutil import copyfile, rmtree
+from shutil import rmtree
 from pathlib import Path
 import multiprocessing
 
diff --git a/4 - TrainingModelKeras.py b/4 - TrainingModelKeras.py
index 0c6e57f..9f492c2 100644
--- a/4 - TrainingModelKeras.py	
+++ b/4 - TrainingModelKeras.py	
@@ -11,6 +11,7 @@ from keras.callbacks import ModelCheckpoint, EarlyStopping, TensorBoard
 from keras.layers import Dense, Dropout, GlobalAveragePooling2D
 from keras.models import Sequential
 from keras.preprocessing.image import ImageDataGenerator
+from keras.utils import multi_gpu_model
 
 from sklearn.metrics import accuracy_score, confusion_matrix, classification_report
 
@@ -22,7 +23,7 @@ from PIL import ImageFile
 ImageFile.LOAD_TRUNCATED_IMAGES = True
 
 input_shape = (224, 224, 3)
-batch_size = 96
+batch_size = 32
 
 model_name = "mobilenet-fixed-data"
 
@@ -53,7 +54,7 @@ val_idg = ImageDataGenerator(
 )
 
 val_gen = val_idg.flow_from_directory(
-    './data/val',
+    './data/test',
     target_size=(input_shape[0], input_shape[1]),
     batch_size=batch_size
 )
@@ -102,7 +103,10 @@ add_model.add(Dropout(0.5))
 add_model.add(Dense(512, activation='relu'))
 add_model.add(Dense(len(train_gen.class_indices), activation='softmax'))  # Decision layer
 
-model = add_model
+#TODO: Add in gpu support
+model = multi_gpu_model(add_model, 2)
+# model = add_model
+
 model.compile(loss='categorical_crossentropy',
               # optimizer=optimizers.SGD(lr=1e-4, momentum=0.9),
               optimizer=optimizers.Adam(lr=1e-4),