Updates....updates everywhere

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 = {}
@@ -39,28 +42,30 @@ 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
 
 
-import os
-
 
 def get_images_for_pokemon(poke_to_limit):
     pokemon = poke_to_limit[0]
@@ -69,7 +74,7 @@ def get_images_for_pokemon(poke_to_limit):
     response.download(
         {
             "keywords": pokemon + " pokemon",
-            "limit": 1,#limit,
+            "limit": limit,
             "chromedriver": "chromedriver"
             # Add chromedriver to your path or just point this var directly to your chromedriverv
         }

3 - TestTrainSplit.py

@@ -6,8 +6,8 @@ import multiprocessing
 train_dir = "./data/train/"
 test_dir = "./data/test/"
 val_dir = "./data/val/"
-train = .80
-test = .15
+train = .5
+test = .5
 val = .05
 
 

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
 )
@@ -103,7 +104,10 @@ 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),

4 - TransferLearningKeras.py

@@ -8,7 +8,7 @@ from PIL import ImageFile
 
 ImageFile.LOAD_TRUNCATED_IMAGES = True
 
-input_shape = (244, 244, 3)
+input_shape = (224, 224, 3)
 
 batch_size = 60
 model_name = "MobileNetV2FullDataset"