diff --git a/Shoe Classifier_InceptionResNetV2.ipynb b/Shoe Classifier_InceptionResNetV2.ipynb
index fc9ce7f..3ea3e8c 100644
--- a/Shoe Classifier_InceptionResNetV2.ipynb	
+++ b/Shoe Classifier_InceptionResNetV2.ipynb	
@@ -39,30 +39,24 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "def add_regularization(model, regularizer=tf.keras.regularizers.l2(0.0001)):\n",
-    "\n",
-    "    if not isinstance(regularizer, tf.keras.regularizers.Regularizer):\n",
-    "      print(\"Regularizer must be a subclass of tf.keras.regularizers.Regularizer\")\n",
-    "      return model\n",
-    "\n",
-    "    for layer in model.layers:\n",
-    "        for attr in ['kernel_regularizer']:\n",
-    "            if hasattr(layer, attr):\n",
-    "              setattr(layer, attr, regularizer)\n",
-    "\n",
-    "    # When we change the layers attributes, the change only happens in the model config file\n",
-    "    model_json = model.to_json()\n",
-    "\n",
-    "    # Save the weights before reloading the model.\n",
-    "    tmp_weights_path = os.path.join(tempfile.gettempdir(), 'tmp_weights.h5')\n",
-    "    model.save_weights(tmp_weights_path)\n",
-    "\n",
-    "    # load the model from the config\n",
-    "    model = tf.keras.models.model_from_json(model_json)\n",
-    "    \n",
-    "    # Reload the model weights\n",
-    "    model.load_weights(tmp_weights_path, by_name=True)\n",
-    "    return model"
+    "def dict_pics_jup():\n",
+    "    '''\n",
+    "    {source:target} dict used to replace source urls with image location as input\n",
+    "    '''\n",
+    "    target_dir = os.getcwd() + os.sep + \"training_images\"\n",
+    "    with open('temp_pics_source_list.txt') as f:\n",
+    "        temp_pics_source_list = json.load(f)\n",
+    "        \n",
+    "    dict_pics = {}\n",
+    "    for k in temp_pics_source_list:\n",
+    "         patt_1 = re.search(r'[^/]+(?=/\\$_|.(\\.jpg|\\.jpeg|\\.png))', k, re.IGNORECASE)\n",
+    "         patt_2 = re.search(r'(\\.jpg|\\.jpeg|\\.png)', k, re.IGNORECASE)\n",
+    "         if patt_1 and patt_2 is not None:\n",
+    "             tag = patt_1.group() + patt_2.group().lower()\n",
+    "             file_name = target_dir + os.sep + tag\n",
+    "             dict_pics.update({k:file_name})\n",
+    "    print(\"{source:target} dictionary created @ \" + target_dir)\n",
+    "    return dict_pics"
    ]
   },
   {
@@ -93,21 +87,33 @@
     }
    ],
    "source": [
-    "def dict_pics():\n",
-    "    target_dir = os.getcwd() + os.sep + \"training_images\"\n",
-    "    with open('temp_pics_source_list.txt') as f:\n",
-    "        temp_pics_source_list = json.load(f)\n",
-    "    dict_pics = {k:target_dir + os.sep + re.search(r'[^/]+(?=/\\$_|.jpg)', k, re.IGNORECASE).group() + '.jpg' for k in temp_pics_source_list}\n",
-    "    print(\"{source:target} dictionary created @ \" + target_dir)\n",
-    "    return dict_pics\n",
+    "dict_pics = dict_pics_jup()\n",
+    "\n",
+    "with open('women_cat_list.txt') as f:\n",
+    "    women_cats = json.load(f)\n",
+    "with open('men_cat_list.txt') as f:\n",
+    "    men_cats = json.load(f)\n",
+    "    \n",
+    "with open('temp_pics_source_list.txt') as f:\n",
+    "    tempics = json.load(f)\n",
+    "# list of image urls that did not get named properly which will be removed from the dataframe\n",
+    "drop_row_vals = []\n",
+    "for pic in tempics:\n",
+    "    try:\n",
+    "        dict_pics[pic]\n",
+    "    except KeyError:\n",
+    "        drop_row_vals.append(pic)\n",
+    "\n",
+    "df['PrimaryCategoryID'] = df['PrimaryCategoryID'].astype(str) # pandas thinks ids are ints\n",
+    "ddf = df[df.PictureURL.isin(drop_row_vals)==False] # remove improperly named image files\n",
+    "df = ddf[ddf.PrimaryCategoryID.isin(men_cats)==False] # removes rows of womens categories\n",
     "\n",
-    "dict_pics = dict_pics()\n",
     "blah = pd.Series(df.PictureURL)\n",
     "df = df.drop(labels=['PictureURL'], axis=1)\n",
+    "\n",
     "blah = blah.apply(lambda x: dict_pics[x])\n",
     "df = pd.concat([blah, df],axis=1)\n",
-    "df = df.groupby('PrimaryCategoryID').filter(lambda x: len(x)>25) # removes cat outliers\n",
-    "# removes non-existent image paths"
+    "df = df.groupby('PrimaryCategoryID').filter(lambda x: len(x)>25) # removes cat outliers"
    ]
   },
   {
@@ -117,8 +123,6 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "df['PrimaryCategoryID'] = df['PrimaryCategoryID'].astype(str) # pandas thinks ids are ints\n",
-    "\n",
     "df=df.sample(frac=1)"
    ]
   },
@@ -155,16 +159,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Found 4542 validated image filenames belonging to 13 classes.\n",
-      "Found 1135 validated image filenames belonging to 13 classes.\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/usr/local/lib/python3.8/dist-packages/keras_preprocessing/image/dataframe_iterator.py:279: UserWarning: Found 1 invalid image filename(s) in x_col=\"PictureURL\". These filename(s) will be ignored.\n",
-      "  warnings.warn(\n"
+      "Found 4444 validated image filenames belonging to 7 classes.\n",
+      "Found 1111 validated image filenames belonging to 7 classes.\n"
      ]
     }
    ],
@@ -2656,12 +2652,12 @@
       " global_average_pooling2d (Glob  (None, 1536)        0           ['conv_7b_ac[0][0]']             \n",
       " alAveragePooling2D)                                                                              \n",
       "                                                                                                  \n",
-      " dense (Dense)                  (None, 13)           19981       ['global_average_pooling2d[0][0]'\n",
+      " dense (Dense)                  (None, 7)            10759       ['global_average_pooling2d[0][0]'\n",
       "                                                                 ]                                \n",
       "                                                                                                  \n",
       "==================================================================================================\n",
-      "Total params: 54,356,717\n",
-      "Trainable params: 54,296,173\n",
+      "Total params: 54,347,495\n",
+      "Trainable params: 54,286,951\n",
       "Non-trainable params: 60,544\n",
       "__________________________________________________________________________________________________\n"
      ]
@@ -2671,7 +2667,7 @@
     "for layer in base_model.layers:\n",
     "    layer.trainable = True\n",
     "    \n",
-    "output = Dense(13, activation='softmax')(base_model.output)\n",
+    "output = Dense(7, activation='softmax')(base_model.output)\n",
     "model = tf.keras.Model(base_model.input, output)\n",
     "#model = add_regularization(model)\n",
     "model.summary()\n"
@@ -2715,33 +2711,21 @@
      "output_type": "stream",
      "text": [
       "Epoch 1/30\n",
-      "142/142 [==============================] - 71s 426ms/step - loss: 1.5727 - accuracy: 0.4613 - val_loss: 2.5192 - val_accuracy: 0.1233\n",
+      "139/139 [==============================] - 65s 390ms/step - loss: 1.0030 - accuracy: 0.6427 - val_loss: 2.1331 - val_accuracy: 0.1620\n",
       "Epoch 2/30\n",
-      "142/142 [==============================] - 56s 395ms/step - loss: 0.6991 - accuracy: 0.7644 - val_loss: 1.9969 - val_accuracy: 0.3568\n",
+      "139/139 [==============================] - 51s 365ms/step - loss: 0.3751 - accuracy: 0.8756 - val_loss: 1.7818 - val_accuracy: 0.4707\n",
       "Epoch 3/30\n",
-      "142/142 [==============================] - 56s 395ms/step - loss: 0.2355 - accuracy: 0.9302 - val_loss: 1.9622 - val_accuracy: 0.4626\n",
+      "139/139 [==============================] - 50s 359ms/step - loss: 0.1193 - accuracy: 0.9622 - val_loss: 1.5616 - val_accuracy: 0.6166\n",
       "Epoch 4/30\n",
-      "142/142 [==============================] - 56s 393ms/step - loss: 0.1015 - accuracy: 0.9707 - val_loss: 1.8987 - val_accuracy: 0.5207\n",
+      "139/139 [==============================] - 51s 363ms/step - loss: 0.0830 - accuracy: 0.9750 - val_loss: 1.3374 - val_accuracy: 0.6922\n",
       "Epoch 5/30\n",
-      "142/142 [==============================] - 57s 398ms/step - loss: 0.0899 - accuracy: 0.9736 - val_loss: 1.8865 - val_accuracy: 0.5727\n",
+      "139/139 [==============================] - 50s 360ms/step - loss: 0.0609 - accuracy: 0.9815 - val_loss: 1.3717 - val_accuracy: 0.6976\n",
       "Epoch 6/30\n",
-      "142/142 [==============================] - 57s 402ms/step - loss: 0.0895 - accuracy: 0.9742 - val_loss: 1.9440 - val_accuracy: 0.5419\n",
+      "139/139 [==============================] - 51s 366ms/step - loss: 0.0593 - accuracy: 0.9818 - val_loss: 1.2600 - val_accuracy: 0.7192\n",
       "Epoch 7/30\n",
-      "142/142 [==============================] - 58s 406ms/step - loss: 0.0784 - accuracy: 0.9762 - val_loss: 2.2314 - val_accuracy: 0.5145\n",
+      "139/139 [==============================] - 51s 363ms/step - loss: 0.0468 - accuracy: 0.9860 - val_loss: 1.1707 - val_accuracy: 0.7264\n",
       "Epoch 8/30\n",
-      "142/142 [==============================] - 58s 406ms/step - loss: 0.0536 - accuracy: 0.9852 - val_loss: 2.0704 - val_accuracy: 0.5480\n",
-      "Epoch 9/30\n",
-      "142/142 [==============================] - 57s 399ms/step - loss: 0.0672 - accuracy: 0.9802 - val_loss: 2.3144 - val_accuracy: 0.5189\n",
-      "Epoch 10/30\n",
-      "142/142 [==============================] - 56s 395ms/step - loss: 0.0423 - accuracy: 0.9875 - val_loss: 2.2718 - val_accuracy: 0.5330\n",
-      "Epoch 11/30\n",
-      "142/142 [==============================] - 57s 400ms/step - loss: 0.0325 - accuracy: 0.9903 - val_loss: 2.2889 - val_accuracy: 0.5498\n",
-      "Epoch 12/30\n",
-      "142/142 [==============================] - 56s 396ms/step - loss: 0.0591 - accuracy: 0.9822 - val_loss: 2.1700 - val_accuracy: 0.5577\n",
-      "Epoch 13/30\n",
-      "142/142 [==============================] - 56s 395ms/step - loss: 0.0387 - accuracy: 0.9899 - val_loss: 2.3132 - val_accuracy: 0.5410\n",
-      "Epoch 14/30\n",
-      " 95/142 [===================>..........] - ETA: 14s - loss: 0.0344 - accuracy: 0.9888"
+      " 89/139 [==================>...........] - ETA: 14s - loss: 0.0309 - accuracy: 0.9905"
      ]
     }
    ],
diff --git a/Shoe Classifier_VGG16.ipynb b/Shoe Classifier_VGG16.ipynb
index 19bfd86..d6d1c99 100644
--- a/Shoe Classifier_VGG16.ipynb	
+++ b/Shoe Classifier_VGG16.ipynb	
@@ -83,6 +83,33 @@
    "metadata": {
     "scrolled": true
    },
+   "outputs": [],
+   "source": [
+    "def dict_pics_jup():\n",
+    "    '''\n",
+    "    {source:target} dict used to replace source urls with image location as input\n",
+    "    '''\n",
+    "    target_dir = os.getcwd() + os.sep + \"training_images\"\n",
+    "    with open('temp_pics_source_list.txt') as f:\n",
+    "        temp_pics_source_list = json.load(f)\n",
+    "        \n",
+    "    dict_pics = {}\n",
+    "    for k in temp_pics_source_list:\n",
+    "         patt_1 = re.search(r'[^/]+(?=/\\$_|.(\\.jpg|\\.jpeg|\\.png))', k, re.IGNORECASE)\n",
+    "         patt_2 = re.search(r'(\\.jpg|\\.jpeg|\\.png)', k, re.IGNORECASE)\n",
+    "         if patt_1 and patt_2 is not None:\n",
+    "             tag = patt_1.group() + patt_2.group().lower()\n",
+    "             file_name = target_dir + os.sep + tag\n",
+    "             dict_pics.update({k:file_name})\n",
+    "    print(\"{source:target} dictionary created @ \" + target_dir)\n",
+    "    return dict_pics"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "7a6146e6",
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -93,31 +120,33 @@
     }
    ],
    "source": [
-    "def dict_pics():\n",
-    "    target_dir = os.getcwd() + os.sep + \"training_images\"\n",
-    "    with open('temp_pics_source_list.txt') as f:\n",
-    "        temp_pics_source_list = json.load(f)\n",
-    "    dict_pics = {k:target_dir + os.sep + re.search(r'[^/]+(?=/\\$_|.jpg)', k, re.IGNORECASE).group() + '.jpg' for k in temp_pics_source_list}\n",
-    "    print(\"{source:target} dictionary created @ \" + target_dir)\n",
-    "    return dict_pics\n",
+    "dict_pics = dict_pics_jup()\n",
+    "\n",
+    "with open('women_cat_list.txt') as f:\n",
+    "    women_cats = json.load(f)\n",
+    "with open('men_cat_list.txt') as f:\n",
+    "    men_cats = json.load(f)\n",
+    "    \n",
+    "with open('temp_pics_source_list.txt') as f:\n",
+    "    tempics = json.load(f)\n",
+    "# list of image urls that did not get named properly which will be removed from the dataframe\n",
+    "drop_row_vals = []\n",
+    "for pic in tempics:\n",
+    "    try:\n",
+    "        dict_pics[pic]\n",
+    "    except KeyError:\n",
+    "        drop_row_vals.append(pic)\n",
+    "\n",
+    "df['PrimaryCategoryID'] = df['PrimaryCategoryID'].astype(str) # pandas thinks ids are ints\n",
+    "ddf = df[df.PictureURL.isin(drop_row_vals)==False] # remove improperly named image files\n",
+    "df = ddf[ddf.PrimaryCategoryID.isin(men_cats)==False] # removes rows of womens categories\n",
     "\n",
-    "dict_pics = dict_pics()\n",
     "blah = pd.Series(df.PictureURL)\n",
     "df = df.drop(labels=['PictureURL'], axis=1)\n",
+    "\n",
     "blah = blah.apply(lambda x: dict_pics[x])\n",
     "df = pd.concat([blah, df],axis=1)\n",
     "df = df.groupby('PrimaryCategoryID').filter(lambda x: len(x)>25) # removes cat outliers\n",
-    "# removes non-existent image paths"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "7a6146e6",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df['PrimaryCategoryID'] = df['PrimaryCategoryID'].astype(str) # pandas thinks ids are ints\n",
     "\n",
     "df=df.sample(frac=1)"
    ]
@@ -131,7 +160,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Counter({'11498': 546, '11504': 546, '11505': 546, '11632': 546, '15709': 546, '24087': 546, '45333': 546, '53120': 546, '53548': 546, '53557': 546, '55793': 546, '62107': 546, '95672': 546})\n"
+      "Counter({'11632': 6505, '45333': 6505, '53548': 6505, '53557': 6505, '55793': 6505, '62107': 6505, '95672': 6505})\n"
      ]
     }
    ],
@@ -163,16 +192,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Found 4542 validated image filenames belonging to 13 classes.\n",
-      "Found 1135 validated image filenames belonging to 13 classes.\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/usr/local/lib/python3.8/dist-packages/keras_preprocessing/image/dataframe_iterator.py:279: UserWarning: Found 1 invalid image filename(s) in x_col=\"PictureURL\". These filename(s) will be ignored.\n",
-      "  warnings.warn(\n"
+      "Found 29143 validated image filenames belonging to 7 classes.\n",
+      "Found 7285 validated image filenames belonging to 7 classes.\n"
      ]
     }
    ],
@@ -302,14 +323,14 @@
     "for layer in base_model.layers[:-1]:\n",
     "    updated_model.add(layer)\n",
     "    if layer.name in ['fc1', 'fc2']:\n",
-    "        updated_model.add(Dropout(.75))\n",
+    "        updated_model.add(Dropout(.50))\n",
     "\n",
     "model = updated_model\n",
     "\n",
     "for layer in model.layers:\n",
     "    layer.trainable = True\n",
     "\n",
-    "model.add(Dense(units=13, activation='softmax'))"
+    "model.add(Dense(units=7, activation='softmax'))"
    ]
   },
   {
@@ -374,11 +395,11 @@
       "                                                                 \n",
       " dropout_1 (Dropout)         (None, 4096)              0         \n",
       "                                                                 \n",
-      " dense (Dense)               (None, 13)                53261     \n",
+      " dense (Dense)               (None, 7)                 28679     \n",
       "                                                                 \n",
       "=================================================================\n",
-      "Total params: 134,313,805\n",
-      "Trainable params: 134,313,805\n",
+      "Total params: 134,289,223\n",
+      "Trainable params: 134,289,223\n",
       "Non-trainable params: 0\n",
       "_________________________________________________________________\n"
      ]
@@ -402,7 +423,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": null,
    "id": "9cd2ba27",
    "metadata": {
     "scrolled": false
@@ -413,77 +434,19 @@
      "output_type": "stream",
      "text": [
       "Epoch 1/30\n",
-      "142/142 [==============================] - 62s 404ms/step - loss: 2.7986 - accuracy: 0.0771 - val_loss: 2.5716 - val_accuracy: 0.0670\n",
+      "911/911 [==============================] - 329s 356ms/step - loss: 1.8477 - accuracy: 0.2577 - val_loss: 1.6306 - val_accuracy: 0.3669\n",
       "Epoch 2/30\n",
-      "142/142 [==============================] - 53s 370ms/step - loss: 2.6351 - accuracy: 0.0790 - val_loss: 2.5728 - val_accuracy: 0.0802\n",
+      "911/911 [==============================] - 322s 353ms/step - loss: 1.4882 - accuracy: 0.4353 - val_loss: 1.4317 - val_accuracy: 0.4784\n",
       "Epoch 3/30\n",
-      "142/142 [==============================] - 53s 370ms/step - loss: 2.6157 - accuracy: 0.0852 - val_loss: 2.5786 - val_accuracy: 0.0705\n",
+      "911/911 [==============================] - 323s 354ms/step - loss: 1.3046 - accuracy: 0.5158 - val_loss: 1.2747 - val_accuracy: 0.5235\n",
       "Epoch 4/30\n",
-      "142/142 [==============================] - 53s 371ms/step - loss: 2.6087 - accuracy: 0.0821 - val_loss: 2.5501 - val_accuracy: 0.1013\n",
+      "911/911 [==============================] - 319s 350ms/step - loss: 1.1691 - accuracy: 0.5681 - val_loss: 1.2090 - val_accuracy: 0.5529\n",
       "Epoch 5/30\n",
-      "142/142 [==============================] - 53s 371ms/step - loss: 2.5420 - accuracy: 0.1182 - val_loss: 2.4237 - val_accuracy: 0.1401\n",
+      "911/911 [==============================] - 317s 348ms/step - loss: 1.0389 - accuracy: 0.6185 - val_loss: 1.1774 - val_accuracy: 0.5682\n",
       "Epoch 6/30\n",
-      "142/142 [==============================] - 52s 368ms/step - loss: 2.4275 - accuracy: 0.1640 - val_loss: 2.3050 - val_accuracy: 0.1982\n",
+      "911/911 [==============================] - 317s 348ms/step - loss: 0.9125 - accuracy: 0.6656 - val_loss: 1.2237 - val_accuracy: 0.5639\n",
       "Epoch 7/30\n",
-      "142/142 [==============================] - 52s 368ms/step - loss: 2.3270 - accuracy: 0.2008 - val_loss: 2.2103 - val_accuracy: 0.2273\n",
-      "Epoch 8/30\n",
-      "142/142 [==============================] - 54s 378ms/step - loss: 2.2011 - accuracy: 0.2294 - val_loss: 2.0607 - val_accuracy: 0.2872\n",
-      "Epoch 9/30\n",
-      "142/142 [==============================] - 54s 378ms/step - loss: 2.0790 - accuracy: 0.2781 - val_loss: 1.9376 - val_accuracy: 0.3419\n",
-      "Epoch 10/30\n",
-      "142/142 [==============================] - 52s 368ms/step - loss: 1.8708 - accuracy: 0.3417 - val_loss: 2.0407 - val_accuracy: 0.3198\n",
-      "Epoch 11/30\n",
-      "142/142 [==============================] - 52s 368ms/step - loss: 1.7588 - accuracy: 0.3743 - val_loss: 1.8968 - val_accuracy: 0.3401\n",
-      "Epoch 12/30\n",
-      "142/142 [==============================] - 52s 369ms/step - loss: 1.5927 - accuracy: 0.4251 - val_loss: 1.8735 - val_accuracy: 0.3542\n",
-      "Epoch 13/30\n",
-      "142/142 [==============================] - 53s 369ms/step - loss: 1.4704 - accuracy: 0.4626 - val_loss: 1.8489 - val_accuracy: 0.3753\n",
-      "Epoch 14/30\n",
-      "142/142 [==============================] - 52s 367ms/step - loss: 1.2452 - accuracy: 0.5484 - val_loss: 1.9595 - val_accuracy: 0.3656\n",
-      "Epoch 15/30\n",
-      "142/142 [==============================] - 52s 370ms/step - loss: 1.1410 - accuracy: 0.5885 - val_loss: 1.9159 - val_accuracy: 0.3930\n",
-      "Epoch 16/30\n",
-      "142/142 [==============================] - 52s 367ms/step - loss: 0.9705 - accuracy: 0.6510 - val_loss: 2.1161 - val_accuracy: 0.3921\n",
-      "Epoch 17/30\n",
-      "142/142 [==============================] - 52s 368ms/step - loss: 0.7992 - accuracy: 0.7041 - val_loss: 2.1271 - val_accuracy: 0.4097\n",
-      "Epoch 18/30\n",
-      "142/142 [==============================] - 52s 369ms/step - loss: 0.7544 - accuracy: 0.7305 - val_loss: 2.0093 - val_accuracy: 0.4361\n",
-      "Epoch 19/30\n",
-      "142/142 [==============================] - 52s 367ms/step - loss: 0.5651 - accuracy: 0.7961 - val_loss: 2.4199 - val_accuracy: 0.3850\n",
-      "Epoch 20/30\n",
-      "142/142 [==============================] - 52s 366ms/step - loss: 0.4566 - accuracy: 0.8342 - val_loss: 2.4058 - val_accuracy: 0.4352\n",
-      "Epoch 21/30\n",
-      "142/142 [==============================] - 52s 364ms/step - loss: 0.3841 - accuracy: 0.8622 - val_loss: 2.5407 - val_accuracy: 0.4141\n",
-      "Epoch 22/30\n",
-      "142/142 [==============================] - 52s 366ms/step - loss: 0.2496 - accuracy: 0.9086 - val_loss: 3.0696 - val_accuracy: 0.4088\n",
-      "Epoch 23/30\n",
-      "142/142 [==============================] - 52s 368ms/step - loss: 0.2847 - accuracy: 0.8985 - val_loss: 2.7929 - val_accuracy: 0.4273\n",
-      "Epoch 24/30\n",
-      "142/142 [==============================] - 52s 369ms/step - loss: 0.2035 - accuracy: 0.9293 - val_loss: 3.0300 - val_accuracy: 0.4132\n",
-      "Epoch 25/30\n",
-      "142/142 [==============================] - ETA: 0s - loss: 0.1872 - accuracy: 0.9377"
-     ]
-    },
-    {
-     "ename": "KeyboardInterrupt",
-     "evalue": "",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-18-4cd4443bbf2a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m model.fit(x=train_generator,\n\u001b[0m\u001b[1;32m      2\u001b[0m           \u001b[0msteps_per_epoch\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrain_generator\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m           \u001b[0mvalidation_data\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mvalidation_generator\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m           \u001b[0mvalidation_steps\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalidation_generator\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m           \u001b[0mepochs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m30\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/keras/utils/traceback_utils.py\u001b[0m in \u001b[0;36merror_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     62\u001b[0m     \u001b[0mfiltered_tb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     63\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 64\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     65\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# pylint: disable=broad-except\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     66\u001b[0m       \u001b[0mfiltered_tb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_process_traceback_frames\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__traceback__\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/keras/engine/training.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq, max_queue_size, workers, use_multiprocessing)\u001b[0m\n\u001b[1;32m   1250\u001b[0m                 \u001b[0mmodel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1251\u001b[0m                 steps_per_execution=self._steps_per_execution)\n\u001b[0;32m-> 1252\u001b[0;31m           val_logs = self.evaluate(\n\u001b[0m\u001b[1;32m   1253\u001b[0m               \u001b[0mx\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mval_x\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1254\u001b[0m               \u001b[0my\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mval_y\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/keras/utils/traceback_utils.py\u001b[0m in \u001b[0;36merror_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     62\u001b[0m     \u001b[0mfiltered_tb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     63\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 64\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     65\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# pylint: disable=broad-except\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     66\u001b[0m       \u001b[0mfiltered_tb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_process_traceback_frames\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__traceback__\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/keras/engine/training.py\u001b[0m in \u001b[0;36mevaluate\u001b[0;34m(self, x, y, batch_size, verbose, sample_weight, steps, callbacks, max_queue_size, workers, use_multiprocessing, return_dict, **kwargs)\u001b[0m\n\u001b[1;32m   1535\u001b[0m             \u001b[0;32mwith\u001b[0m \u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprofiler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexperimental\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTrace\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'test'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstep_num\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mstep\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_r\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1536\u001b[0m               \u001b[0mcallbacks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_test_batch_begin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstep\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1537\u001b[0;31m               \u001b[0mtmp_logs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtest_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0miterator\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1538\u001b[0m               \u001b[0;32mif\u001b[0m \u001b[0mdata_handler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshould_sync\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1539\u001b[0m                 \u001b[0mcontext\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masync_wait\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/tensorflow/python/util/traceback_utils.py\u001b[0m in \u001b[0;36merror_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    148\u001b[0m     \u001b[0mfiltered_tb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    149\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 150\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    151\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    152\u001b[0m       \u001b[0mfiltered_tb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_process_traceback_frames\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__traceback__\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/tensorflow/python/eager/def_function.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m    908\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    909\u001b[0m       \u001b[0;32mwith\u001b[0m \u001b[0mOptionalXlaContext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_jit_compile\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 910\u001b[0;31m         \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    911\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    912\u001b[0m       \u001b[0mnew_tracing_count\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexperimental_get_tracing_count\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/tensorflow/python/eager/def_function.py\u001b[0m in \u001b[0;36m_call\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m    947\u001b[0m       \u001b[0;31m# In this case we have not created variables on the first call. So we can\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    948\u001b[0m       \u001b[0;31m# run the first trace but we should fail if variables are created.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 949\u001b[0;31m       \u001b[0mresults\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_stateful_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    950\u001b[0m       \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_created_variables\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mALLOW_DYNAMIC_VARIABLE_CREATION\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    951\u001b[0m         raise ValueError(\"Creating variables on a non-first call to a function\"\n",
-      "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   3128\u001b[0m       (graph_function,\n\u001b[1;32m   3129\u001b[0m        filtered_flat_args) = self._maybe_define_function(args, kwargs)\n\u001b[0;32m-> 3130\u001b[0;31m     return graph_function._call_flat(\n\u001b[0m\u001b[1;32m   3131\u001b[0m         filtered_flat_args, captured_inputs=graph_function.captured_inputs)  # pylint: disable=protected-access\n\u001b[1;32m   3132\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36m_call_flat\u001b[0;34m(self, args, captured_inputs, cancellation_manager)\u001b[0m\n\u001b[1;32m   1957\u001b[0m         and executing_eagerly):\n\u001b[1;32m   1958\u001b[0m       \u001b[0;31m# No tape is watching; skip to running the function.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1959\u001b[0;31m       return self._build_call_outputs(self._inference_function.call(\n\u001b[0m\u001b[1;32m   1960\u001b[0m           ctx, args, cancellation_manager=cancellation_manager))\n\u001b[1;32m   1961\u001b[0m     forward_backward = self._select_forward_and_backward_functions(\n",
-      "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36mcall\u001b[0;34m(self, ctx, args, cancellation_manager)\u001b[0m\n\u001b[1;32m    596\u001b[0m       \u001b[0;32mwith\u001b[0m \u001b[0m_InterpolateFunctionError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    597\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mcancellation_manager\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 598\u001b[0;31m           outputs = execute.execute(\n\u001b[0m\u001b[1;32m    599\u001b[0m               \u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msignature\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    600\u001b[0m               \u001b[0mnum_outputs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_num_outputs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/tensorflow/python/eager/execute.py\u001b[0m in \u001b[0;36mquick_execute\u001b[0;34m(op_name, num_outputs, inputs, attrs, ctx, name)\u001b[0m\n\u001b[1;32m     56\u001b[0m   \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     57\u001b[0m     \u001b[0mctx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mensure_initialized\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 58\u001b[0;31m     tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,\n\u001b[0m\u001b[1;32m     59\u001b[0m                                         inputs, attrs, num_outputs)\n\u001b[1;32m     60\u001b[0m   \u001b[0;32mexcept\u001b[0m \u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_NotOkStatusException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
+      "147/911 [===>..........................] - ETA: 3:39 - loss: 0.7312 - accuracy: 0.7256"
      ]
     }
    ],
diff --git a/Shoe Classifier_VGG19.ipynb b/Shoe Classifier_VGG19.ipynb
index eaee681..1d20bb4 100644
--- a/Shoe Classifier_VGG19.ipynb	
+++ b/Shoe Classifier_VGG19.ipynb	
@@ -94,20 +94,23 @@
    ],
    "source": [
     "def dict_pics_jup():\n",
+    "    '''\n",
+    "    {source:target} dict used to replace source urls with image location as input\n",
+    "    '''\n",
     "    target_dir = os.getcwd() + os.sep + \"training_images\"\n",
     "    with open('temp_pics_source_list.txt') as f:\n",
     "        temp_pics_source_list = json.load(f)\n",
-    "    dict_pics = {k:target_dir + os.sep + re.search(r'[^/]+(?=/\\$_|.jpg)', k, re.IGNORECASE).group() + '.jpg' for k in temp_pics_source_list}\n",
+    "        \n",
+    "    dict_pics = {}\n",
+    "    for k in temp_pics_source_list:\n",
+    "         patt_1 = re.search(r'[^/]+(?=/\\$_|.(\\.jpg|\\.jpeg|\\.png))', k, re.IGNORECASE)\n",
+    "         patt_2 = re.search(r'(\\.jpg|\\.jpeg|\\.png)', k, re.IGNORECASE)\n",
+    "         if patt_1 and patt_2 is not None:\n",
+    "             tag = patt_1.group() + patt_2.group().lower()\n",
+    "             file_name = target_dir + os.sep + tag\n",
+    "             dict_pics.update({k:file_name})\n",
     "    print(\"{source:target} dictionary created @ \" + target_dir)\n",
-    "    return dict_pics\n",
-    "\n",
-    "dict_pics = dict_pics_jup()\n",
-    "blah = pd.Series(df.PictureURL)\n",
-    "df = df.drop(labels=['PictureURL'], axis=1)\n",
-    "blah = blah.apply(lambda x: dict_pics[x])\n",
-    "df = pd.concat([blah, df],axis=1)\n",
-    "df = df.groupby('PrimaryCategoryID').filter(lambda x: len(x)>25) # removes cat outliers\n",
-    "# removes non-existent image paths"
+    "    return dict_pics"
    ]
   },
   {
@@ -117,7 +120,33 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "dict_pics = dict_pics_jup()\n",
+    "\n",
+    "with open('women_cat_list.txt') as f:\n",
+    "    women_cats = json.load(f)\n",
+    "with open('men_cat_list.txt') as f:\n",
+    "    men_cats = json.load(f)\n",
+    "    \n",
+    "with open('temp_pics_source_list.txt') as f:\n",
+    "    tempics = json.load(f)\n",
+    "# list of image urls that did not get named properly which will be removed from the dataframe\n",
+    "drop_row_vals = []\n",
+    "for pic in tempics:\n",
+    "    try:\n",
+    "        dict_pics[pic]\n",
+    "    except KeyError:\n",
+    "        drop_row_vals.append(pic)\n",
+    "\n",
     "df['PrimaryCategoryID'] = df['PrimaryCategoryID'].astype(str) # pandas thinks ids are ints\n",
+    "ddf = df[df.PictureURL.isin(drop_row_vals)==False] # remove improperly named image files\n",
+    "df = ddf[ddf.PrimaryCategoryID.isin(men_cats)==False] # removes rows of womens categories\n",
+    "\n",
+    "blah = pd.Series(df.PictureURL)\n",
+    "df = df.drop(labels=['PictureURL'], axis=1)\n",
+    "\n",
+    "blah = blah.apply(lambda x: dict_pics[x])\n",
+    "df = pd.concat([blah, df],axis=1)\n",
+    "df = df.groupby('PrimaryCategoryID').filter(lambda x: len(x)>25) # removes cat outliers\n",
     "\n",
     "df=df.sample(frac=1)"
    ]
@@ -304,7 +333,7 @@
     "#model.add(Dense(64, activation='softmax'))\n",
     "# model.add(Dropout(.25))\n",
     "\n",
-    "model.add(Dense(units=13, activation='softmax'))"
+    "model.add(Dense(units=7, activation='softmax'))"
    ]
   },
   {
diff --git a/Shoe Classifier_Xception.ipynb b/Shoe Classifier_Xception.ipynb
index d9d36a5..303f505 100644
--- a/Shoe Classifier_Xception.ipynb	
+++ b/Shoe Classifier_Xception.ipynb	
@@ -36,50 +36,17 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "8d94196d",
+   "id": "a5c72863",
    "metadata": {},
    "outputs": [],
    "source": [
-    "def add_regularization(model, regularizer=tf.keras.regularizers.l2(0.0001)):\n",
-    "\n",
-    "    if not isinstance(regularizer, tf.keras.regularizers.Regularizer):\n",
-    "      print(\"Regularizer must be a subclass of tf.keras.regularizers.Regularizer\")\n",
-    "      return model\n",
-    "\n",
-    "    for layer in model.layers:\n",
-    "        for attr in ['kernel_regularizer']:\n",
-    "            if hasattr(layer, attr):\n",
-    "              setattr(layer, attr, regularizer)\n",
-    "\n",
-    "    # When we change the layers attributes, the change only happens in the model config file\n",
-    "    model_json = model.to_json()\n",
-    "\n",
-    "    # Save the weights before reloading the model.\n",
-    "    tmp_weights_path = os.path.join(tempfile.gettempdir(), 'tmp_weights.h5')\n",
-    "    model.save_weights(tmp_weights_path)\n",
-    "\n",
-    "    # load the model from the config\n",
-    "    model = tf.keras.models.model_from_json(model_json)\n",
-    "    \n",
-    "    # Reload the model weights\n",
-    "    model.load_weights(tmp_weights_path, by_name=True)\n",
-    "    return model"
+    "#image_faults.faulty_images() # removes faulty images\n",
+    "df = pd.read_csv('expanded_class.csv', index_col=[0], low_memory=False)\n"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 3,
-   "id": "a5c72863",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "image_faults.faulty_images() # removes faulty images\n",
-    "df = pd.read_csv('expanded_class.csv', index_col=[0], low_memory=False)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -93,19 +60,23 @@
     "        \n",
     "    dict_pics = {}\n",
     "    for k in temp_pics_source_list:\n",
-    "         patt_1 = re.search(r'[^/]+(?=/\\$_|.(\\.jpg|\\.jpeg|\\.png))', k, re.IGNORECASE)\n",
-    "         patt_2 = re.search(r'(\\.jpg|\\.jpeg|\\.png)', k, re.IGNORECASE)\n",
-    "         if patt_1 and patt_2 is not None:\n",
-    "             tag = patt_1.group() + patt_2.group().lower()\n",
-    "             file_name = target_dir + os.sep + tag\n",
-    "             dict_pics.update({k:file_name})\n",
+    "        try:\n",
+    "            \n",
+    "             patt_1 = re.search(r'[^/]+(?=/\\$_|.(\\.jpg|\\.jpeg|\\.png))', k, re.IGNORECASE)\n",
+    "             patt_2 = re.search(r'(\\.jpg|\\.jpeg|\\.png)', k, re.IGNORECASE)\n",
+    "             if patt_1 and patt_2 is not None:\n",
+    "                 tag = patt_1.group() + patt_2.group().lower()\n",
+    "                 file_name = target_dir + os.sep + tag\n",
+    "                 dict_pics.update({k:file_name})\n",
+    "        except TypeError:\n",
+    "            print(k)\n",
     "    print(\"{source:target} dictionary created @ \" + target_dir)\n",
     "    return dict_pics\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 4,
    "id": "1057a442",
    "metadata": {
     "scrolled": true
@@ -115,12 +86,19 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "nan\n",
       "{source:target} dictionary created @ /tf/training_images\n"
      ]
     }
    ],
    "source": [
     "dict_pics = dict_pics_jup()\n",
+    "\n",
+    "with open('women_cat_list.txt') as f:\n",
+    "    women_cats = json.load(f)\n",
+    "with open('men_cat_list.txt') as f:\n",
+    "    men_cats = json.load(f)\n",
+    "    \n",
     "with open('temp_pics_source_list.txt') as f:\n",
     "    tempics = json.load(f)\n",
     "# list of image urls that did not get named properly which will be removed from the dataframe\n",
@@ -130,9 +108,11 @@
     "        dict_pics[pic]\n",
     "    except KeyError:\n",
     "        drop_row_vals.append(pic)\n",
-    "        \n",
-    "df = df[df.PictureURL.isin(drop_row_vals)==False]\n",
-    "# TODO drop men's or women's categories here\n",
+    "\n",
+    "df['PrimaryCategoryID'] = df['PrimaryCategoryID'].astype(str) # pandas thinks ids are ints\n",
+    "ddf = df[df.PictureURL.isin(drop_row_vals)==False] # remove improperly named image files\n",
+    "df = ddf[ddf.PrimaryCategoryID.isin(men_cats)==False] # removes rows of womens categories\n",
+    "\n",
     "blah = pd.Series(df.PictureURL)\n",
     "df = df.drop(labels=['PictureURL'], axis=1)\n",
     "\n",
@@ -143,31 +123,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 5,
    "id": "7a6146e6",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "17"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "df['PrimaryCategoryID'] = df['PrimaryCategoryID'].astype(str) # pandas thinks ids are ints\n",
-    "\n",
-    "df=df.sample(frac=1)\n",
-    "len(drop_row_vals)"
+    "df=df.sample(frac=1)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 6,
    "id": "114cc3c0",
    "metadata": {},
    "outputs": [],
@@ -179,36 +145,28 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 7,
    "id": "506aa5cf",
    "metadata": {},
    "outputs": [],
    "source": [
-    "train, test = train_test_split(train, test_size=0.1, random_state=42)\n",
+    "train, test = train_test_split(train, test_size=0.2, random_state=42)\n",
     "# stratify=train['PrimaryCategoryID']\n",
     "# train['PrimaryCategoryID'].value_counts()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 8,
    "id": "4d72eb90",
    "metadata": {},
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/usr/local/lib/python3.8/dist-packages/keras_preprocessing/image/dataframe_iterator.py:279: UserWarning: Found 1 invalid image filename(s) in x_col=\"PictureURL\". These filename(s) will be ignored.\n",
-      "  warnings.warn(\n"
-     ]
-    },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Found 53005 validated image filenames belonging to 13 classes.\n",
-      "Found 13251 validated image filenames belonging to 13 classes.\n"
+      "Found 12276 validated image filenames belonging to 7 classes.\n",
+      "Found 3068 validated image filenames belonging to 7 classes.\n"
      ]
     }
    ],
@@ -222,6 +180,7 @@
     "                             #height_shift_range= 0.2,\n",
     "                             #rotation_range= 90,\n",
     "                             preprocessing_function=tf.keras.applications.xception.preprocess_input)\n",
+    "\n",
     "train_generator=datagen.flow_from_dataframe(\n",
     "    dataframe=train[:len(train)],\n",
     "    directory='./training_images',\n",
@@ -248,7 +207,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 9,
    "id": "7b70f37f",
    "metadata": {},
    "outputs": [],
@@ -258,7 +217,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 10,
    "id": "1ed54bf5",
    "metadata": {},
    "outputs": [],
@@ -275,7 +234,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 11,
    "id": "85934565",
    "metadata": {},
    "outputs": [],
@@ -286,7 +245,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 12,
    "id": "6322bcad",
    "metadata": {},
    "outputs": [
@@ -306,7 +265,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 13,
    "id": "07fd25c6",
    "metadata": {},
    "outputs": [],
@@ -319,7 +278,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 14,
    "id": "b31af79e",
    "metadata": {},
    "outputs": [],
@@ -330,7 +289,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 15,
    "id": "fe06f2bf",
    "metadata": {},
    "outputs": [
@@ -741,13 +700,13 @@
       " global_average_pooling2d (Glob  (None, 2048)        0           ['block14_sepconv2_act[0][0]']   \n",
       " alAveragePooling2D)                                                                              \n",
       "                                                                                                  \n",
-      " dense (Dense)                  (None, 13)           26637       ['global_average_pooling2d[0][0]'\n",
+      " dense (Dense)                  (None, 7)            14343       ['global_average_pooling2d[0][0]'\n",
       "                                                                 ]                                \n",
       "                                                                                                  \n",
       "==================================================================================================\n",
-      "Total params: 20,888,117\n",
-      "Trainable params: 20,833,589\n",
-      "Non-trainable params: 54,528\n",
+      "Total params: 20,875,823\n",
+      "Trainable params: 14,343\n",
+      "Non-trainable params: 20,861,480\n",
       "__________________________________________________________________________________________________\n"
      ]
     }
@@ -756,7 +715,7 @@
     "for layer in base_model.layers:\n",
     "    layer.trainable = True\n",
     "    \n",
-    "output = Dense(13, activation='softmax')(base_model.output)\n",
+    "output = Dense(7, activation='softmax')(base_model.output)\n",
     "model = tf.keras.Model(base_model.input, output)\n",
     "#model = add_regularization(model)\n",
     "model.summary()\n"
@@ -764,7 +723,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 16,
    "id": "ea620129",
    "metadata": {},
    "outputs": [],
@@ -777,7 +736,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 17,
    "id": "fd5d1246",
    "metadata": {},
    "outputs": [],
@@ -789,7 +748,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 18,
    "id": "9cd2ba27",
    "metadata": {
     "scrolled": false
@@ -800,13 +759,7 @@
      "output_type": "stream",
      "text": [
       "Epoch 1/30\n",
-      "829/829 [==============================] - 786s 942ms/step - loss: 1.5037 - accuracy: 0.4896 - val_loss: 1.2946 - val_accuracy: 0.5520\n",
-      "Epoch 2/30\n",
-      "829/829 [==============================] - 726s 875ms/step - loss: 0.8550 - accuracy: 0.7117 - val_loss: 1.3593 - val_accuracy: 0.5593\n",
-      "Epoch 3/30\n",
-      "829/829 [==============================] - 750s 905ms/step - loss: 0.3322 - accuracy: 0.8993 - val_loss: 1.5304 - val_accuracy: 0.5542\n",
-      "Epoch 4/30\n",
-      "172/829 [=====>........................] - ETA: 7:57 - loss: 0.1030 - accuracy: 0.9787"
+      "134/192 [===================>..........] - ETA: 31s - loss: 8.9588 - accuracy: 0.1273"
      ]
     },
     {
@@ -816,21 +769,16 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-19-4cd4443bbf2a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m model.fit(x=train_generator,\n\u001b[0m\u001b[1;32m      2\u001b[0m           \u001b[0msteps_per_epoch\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrain_generator\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m           \u001b[0mvalidation_data\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mvalidation_generator\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m           \u001b[0mvalidation_steps\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalidation_generator\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m           \u001b[0mepochs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m30\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m<ipython-input-18-4cd4443bbf2a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m model.fit(x=train_generator,\n\u001b[0m\u001b[1;32m      2\u001b[0m           \u001b[0msteps_per_epoch\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrain_generator\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m           \u001b[0mvalidation_data\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mvalidation_generator\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m           \u001b[0mvalidation_steps\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalidation_generator\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m           \u001b[0mepochs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m30\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/keras/utils/traceback_utils.py\u001b[0m in \u001b[0;36merror_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     62\u001b[0m     \u001b[0mfiltered_tb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     63\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 64\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     65\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# pylint: disable=broad-except\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     66\u001b[0m       \u001b[0mfiltered_tb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_process_traceback_frames\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__traceback__\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/keras/engine/training.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq, max_queue_size, workers, use_multiprocessing)\u001b[0m\n\u001b[1;32m   1219\u001b[0m               \u001b[0mlogs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtmp_logs\u001b[0m  \u001b[0;31m# No error, now safe to assign to logs.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1220\u001b[0m               \u001b[0mend_step\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mstep\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mdata_handler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstep_increment\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1221\u001b[0;31m               \u001b[0mcallbacks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_train_batch_end\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mend_step\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlogs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1222\u001b[0m               \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstop_training\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1223\u001b[0m                 \u001b[0;32mbreak\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/keras/callbacks.py\u001b[0m in \u001b[0;36mon_train_batch_end\u001b[0;34m(self, batch, logs)\u001b[0m\n\u001b[1;32m    434\u001b[0m     \"\"\"\n\u001b[1;32m    435\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_should_call_train_batch_hooks\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 436\u001b[0;31m       \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call_batch_hook\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mModeKeys\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTRAIN\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'end'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlogs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlogs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    437\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    438\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0mon_test_batch_begin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlogs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/keras/callbacks.py\u001b[0m in \u001b[0;36m_call_batch_hook\u001b[0;34m(self, mode, hook, batch, logs)\u001b[0m\n\u001b[1;32m    293\u001b[0m       \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call_batch_begin_hook\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmode\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlogs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    294\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0mhook\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'end'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 295\u001b[0;31m       \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call_batch_end_hook\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmode\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlogs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    296\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    297\u001b[0m       raise ValueError(\n",
-      "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/keras/callbacks.py\u001b[0m in \u001b[0;36m_call_batch_end_hook\u001b[0;34m(self, mode, batch, logs)\u001b[0m\n\u001b[1;32m    314\u001b[0m       \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_batch_times\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch_time\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    315\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 316\u001b[0;31m     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call_batch_hook_helper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhook_name\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlogs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    317\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    318\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_batch_times\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m>=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_num_batches_for_timing_check\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/keras/callbacks.py\u001b[0m in \u001b[0;36m_call_batch_hook_helper\u001b[0;34m(self, hook_name, batch, logs)\u001b[0m\n\u001b[1;32m    352\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0mcallback\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcallbacks\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    353\u001b[0m       \u001b[0mhook\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcallback\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhook_name\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 354\u001b[0;31m       \u001b[0mhook\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlogs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    355\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    356\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_check_timing\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/keras/callbacks.py\u001b[0m in \u001b[0;36mon_train_batch_end\u001b[0;34m(self, batch, logs)\u001b[0m\n\u001b[1;32m   1030\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1031\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0mon_train_batch_end\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlogs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1032\u001b[0;31m     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_batch_update_progbar\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlogs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1033\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1034\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0mon_test_batch_end\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlogs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/keras/callbacks.py\u001b[0m in \u001b[0;36m_batch_update_progbar\u001b[0;34m(self, batch, logs)\u001b[0m\n\u001b[1;32m   1102\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mverbose\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1103\u001b[0m       \u001b[0;31m# Only block async when verbose = 1.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1104\u001b[0;31m       \u001b[0mlogs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf_utils\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msync_to_numpy_or_python_type\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlogs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1105\u001b[0m       \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprogbar\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mseen\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlogs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfinalize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1106\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/keras/utils/tf_utils.py\u001b[0m in \u001b[0;36msync_to_numpy_or_python_type\u001b[0;34m(tensors)\u001b[0m\n\u001b[1;32m    552\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mt\u001b[0m  \u001b[0;31m# Don't turn ragged or sparse tensors to NumPy.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    553\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 554\u001b[0;31m   \u001b[0;32mreturn\u001b[0m \u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnest\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmap_structure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_to_single_numpy_or_python_type\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtensors\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    555\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    556\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/tensorflow/python/util/nest.py\u001b[0m in \u001b[0;36mmap_structure\u001b[0;34m(func, *structure, **kwargs)\u001b[0m\n\u001b[1;32m    867\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    868\u001b[0m   return pack_sequence_as(\n\u001b[0;32m--> 869\u001b[0;31m       \u001b[0mstructure\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mentries\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    870\u001b[0m       expand_composites=expand_composites)\n\u001b[1;32m    871\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/tensorflow/python/util/nest.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m    867\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    868\u001b[0m   return pack_sequence_as(\n\u001b[0;32m--> 869\u001b[0;31m       \u001b[0mstructure\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mentries\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    870\u001b[0m       expand_composites=expand_composites)\n\u001b[1;32m    871\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
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-      "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/ops.py\u001b[0m in \u001b[0;36mnumpy\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1147\u001b[0m     \"\"\"\n\u001b[1;32m   1148\u001b[0m     \u001b[0;31m# TODO(slebedev): Consider avoiding a copy for non-CPU or remote tensors.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1149\u001b[0;31m     \u001b[0mmaybe_arr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_numpy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# pylint: disable=protected-access\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1150\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mmaybe_arr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmaybe_arr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndarray\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mmaybe_arr\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1151\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/ops.py\u001b[0m in \u001b[0;36m_numpy\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1113\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0m_numpy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1114\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1115\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_numpy_internal\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1116\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_NotOkStatusException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# pylint: disable=protected-access\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1117\u001b[0m       \u001b[0;32mraise\u001b[0m \u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_status_to_exception\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;32mNone\u001b[0m  \u001b[0;31m# pylint: disable=protected-access\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/keras/engine/training.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq, max_queue_size, workers, use_multiprocessing)\u001b[0m\n\u001b[1;32m   1214\u001b[0m                 _r=1):\n\u001b[1;32m   1215\u001b[0m               \u001b[0mcallbacks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_train_batch_begin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstep\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1216\u001b[0;31m               \u001b[0mtmp_logs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0miterator\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1217\u001b[0m               \u001b[0;32mif\u001b[0m \u001b[0mdata_handler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshould_sync\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1218\u001b[0m                 \u001b[0mcontext\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masync_wait\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/tensorflow/python/util/traceback_utils.py\u001b[0m in \u001b[0;36merror_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    148\u001b[0m     \u001b[0mfiltered_tb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    149\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 150\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    151\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    152\u001b[0m       \u001b[0mfiltered_tb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_process_traceback_frames\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__traceback__\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/tensorflow/python/eager/def_function.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m    908\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    909\u001b[0m       \u001b[0;32mwith\u001b[0m \u001b[0mOptionalXlaContext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_jit_compile\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 910\u001b[0;31m         \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    911\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    912\u001b[0m       \u001b[0mnew_tracing_count\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexperimental_get_tracing_count\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/tensorflow/python/eager/def_function.py\u001b[0m in \u001b[0;36m_call\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m    940\u001b[0m       \u001b[0;31m# In this case we have created variables on the first call, so we run the\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    941\u001b[0m       \u001b[0;31m# defunned version which is guaranteed to never create variables.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 942\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_stateless_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# pylint: disable=not-callable\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    943\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_stateful_fn\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    944\u001b[0m       \u001b[0;31m# Release the lock early so that multiple threads can perform the call\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   3128\u001b[0m       (graph_function,\n\u001b[1;32m   3129\u001b[0m        filtered_flat_args) = self._maybe_define_function(args, kwargs)\n\u001b[0;32m-> 3130\u001b[0;31m     return graph_function._call_flat(\n\u001b[0m\u001b[1;32m   3131\u001b[0m         filtered_flat_args, captured_inputs=graph_function.captured_inputs)  # pylint: disable=protected-access\n\u001b[1;32m   3132\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36m_call_flat\u001b[0;34m(self, args, captured_inputs, cancellation_manager)\u001b[0m\n\u001b[1;32m   1957\u001b[0m         and executing_eagerly):\n\u001b[1;32m   1958\u001b[0m       \u001b[0;31m# No tape is watching; skip to running the function.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1959\u001b[0;31m       return self._build_call_outputs(self._inference_function.call(\n\u001b[0m\u001b[1;32m   1960\u001b[0m           ctx, args, cancellation_manager=cancellation_manager))\n\u001b[1;32m   1961\u001b[0m     forward_backward = self._select_forward_and_backward_functions(\n",
+      "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36mcall\u001b[0;34m(self, ctx, args, cancellation_manager)\u001b[0m\n\u001b[1;32m    596\u001b[0m       \u001b[0;32mwith\u001b[0m \u001b[0m_InterpolateFunctionError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    597\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mcancellation_manager\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 598\u001b[0;31m           outputs = execute.execute(\n\u001b[0m\u001b[1;32m    599\u001b[0m               \u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msignature\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    600\u001b[0m               \u001b[0mnum_outputs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_num_outputs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/tensorflow/python/eager/execute.py\u001b[0m in \u001b[0;36mquick_execute\u001b[0;34m(op_name, num_outputs, inputs, attrs, ctx, name)\u001b[0m\n\u001b[1;32m     56\u001b[0m   \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     57\u001b[0m     \u001b[0mctx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mensure_initialized\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 58\u001b[0;31m     tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,\n\u001b[0m\u001b[1;32m     59\u001b[0m                                         inputs, attrs, num_outputs)\n\u001b[1;32m     60\u001b[0m   \u001b[0;32mexcept\u001b[0m \u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_NotOkStatusException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
      ]
     }
diff --git a/ebay_api.py b/ebay_api.py
index 9a77e43..0368612 100644
--- a/ebay_api.py
+++ b/ebay_api.py
@@ -183,8 +183,6 @@ class ShoppingApi:
 
         parent_cats = ['3034', '93427'] # Women's and Men's shoe departments
         cat_list = []
-        # TODO make sep lists for women's and men's shoe cats. Needed to train
-        # mens and women's cats separately. This might improve val. acc. during training
 
         with open('temp_oauth_token.txt') as f:
             access_token = json.load(f)
@@ -408,17 +406,35 @@ class CurateData:
         '''
         expand = input("expand image list or use primary listing image? (y or n): ")
         if ('y' or 'Y') in expand:
-            expanded_class = class_training.explode('PictureURL').reset_index(drop=True)
-            expanded_class = expanded_class.dropna(subset=['PictureURL'])
-            expanded_class = expanded_class.drop_duplicates(subset=['PictureURL']).reset_index(drop=True)
+            count = input('how many images? All [A] or the first <n> images?')
+            if 'A' in count:
+                expanded_class = class_training.explode('PictureURL').reset_index(drop=True)
+                expanded_class = expanded_class.dropna(subset=['PictureURL'])
+                expanded_class = expanded_class.drop_duplicates(subset=['PictureURL']).reset_index(drop=True)
 
-            expanded_dropd = dropd.explode('PictureURL').reset_index(drop=True)
-            expanded_dropd = expanded_dropd.dropna(subset=['PictureURL'])
-            expanded_dropd = expanded_dropd.drop_duplicates(subset=['PictureURL']).reset_index(drop=True)
+                expanded_dropd = dropd.explode('PictureURL').reset_index(drop=True)
+                expanded_dropd = expanded_dropd.dropna(subset=['PictureURL'])
+                expanded_dropd = expanded_dropd.drop_duplicates(subset=['PictureURL']).reset_index(drop=True)
 
-            expanded_dropd = self.extract_df(expanded_dropd) # convert lists to values
+                expanded_dropd = self.extract_df(expanded_dropd) # convert lists to values
 
-            temp_pics_source_list = list(set(expanded_class.PictureURL.to_list())) 
+                temp_pics_source_list = list(set(expanded_class.PictureURL.to_list())) 
+            else:
+                count = int(count)
+                class_training['PictureURL'] = class_training['PictureURL'].apply(lambda x: x[0:count] if len(x)>0 else np.nan)
+                expanded_class = class_training.explode('PictureURL').reset_index(drop=True)
+                expanded_class = expanded_class.dropna(subset=['PictureURL'])
+                expanded_class = expanded_class.drop_duplicates(subset=['PictureURL']).reset_index(drop=True)
+
+                dropd = dropd.dropna(subset=['PictureURL'])
+                dropd['PictureURL'] = dropd['PictureURL'].apply(lambda x: x[0:count] if len(x)>0 else np.nan)
+                expanded_dropd = dropd.explode('PictureURL').reset_index(drop=True)
+                expanded_dropd = expanded_dropd.dropna(subset=['PictureURL'])
+
+                expanded_dropd = self.extract_df(expanded_dropd) # convert lists to values
+
+                # retrieves picture URLs from master raw_data.txt and rewrites temp_pics_source_list.txt
+                temp_pics_source_list = list(set(expanded_class.PictureURL.to_list())) # TODO
 
         else:
             class_training['PictureURL'] = class_training['PictureURL'].apply(lambda x: x[0] if len(x)>0 else np.nan)