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diff --git a/data_2/interim/ham10000_workshop__cancer_detection_and_classification__extracted_features/BiT_M_Resnet101_HAM10000_workshop__Cancer_Detection_And_Classification_valid_01_labels.csv b/data_2/interim/ham10000_workshop__cancer_detection_and_classification__extracted_features/BiT_M_Resnet101_HAM10000_workshop__Cancer_Detection_And_Classification_valid_01_labels.csv
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diff --git a/data_2/interim/ham10000_workshop__cancer_detection_and_classification__extracted_features/MobileNet_v2_HAM10000_workshop__Cancer_Detection_And_Classification_valid_01_labels.csv b/data_2/interim/ham10000_workshop__cancer_detection_and_classification__extracted_features/MobileNet_v2_HAM10000_workshop__Cancer_Detection_And_Classification_valid_01_labels.csv
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diff --git a/data_2/interim/ham10000_workshop__cancer_risk_groups__extracted_beta_features/BiT_M_Resnet101_ham10000_workshop__cancer_risk_groups_valid_01_labels.csv b/data_2/interim/ham10000_workshop__cancer_risk_groups__extracted_beta_features/BiT_M_Resnet101_ham10000_workshop__cancer_risk_groups_valid_01_labels.csv
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index 0000000000000000000000000000000000000000..7ef66c01c57e936454ff11ee81cb99e5418b5a4d
--- /dev/null
+++ b/data_2/interim/ham10000_workshop__cancer_risk_groups__extracted_features/BiT_M_Resnet101_HAM10000_workshop__Cancer_Risk_Groups_train_01_labels.csv
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+Medium-benign_cancer,ISIC_0034318.jpg,raw,0
diff --git a/data_2/interim/ham10000_workshop__cancer_risk_groups__extracted_features/MobileNet_v2_HAM10000_workshop__Cancer_Risk_Groups_valid_01_encoded.npy b/data_2/interim/ham10000_workshop__cancer_risk_groups__extracted_features/MobileNet_v2_HAM10000_workshop__Cancer_Risk_Groups_valid_01_encoded.npy
new file mode 100644
index 0000000000000000000000000000000000000000..5ab87f5a32ea7d057480455069064aeabfe0f91c
Binary files /dev/null and b/data_2/interim/ham10000_workshop__cancer_risk_groups__extracted_features/MobileNet_v2_HAM10000_workshop__Cancer_Risk_Groups_valid_01_encoded.npy differ
diff --git a/data_2/interim/ham10000_workshop__cancer_risk_groups__extracted_features/MobileNet_v2_HAM10000_workshop__Cancer_Risk_Groups_valid_01_labels.csv b/data_2/interim/ham10000_workshop__cancer_risk_groups__extracted_features/MobileNet_v2_HAM10000_workshop__Cancer_Risk_Groups_valid_01_labels.csv
new file mode 100644
index 0000000000000000000000000000000000000000..76922f51f552d3c203ab73f34cd8a36a65eb09d2
--- /dev/null
+++ b/data_2/interim/ham10000_workshop__cancer_risk_groups__extracted_features/MobileNet_v2_HAM10000_workshop__Cancer_Risk_Groups_valid_01_labels.csv
@@ -0,0 +1,148 @@
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diff --git a/notebooks/03_Feature_Extraction.ipynb b/notebooks/03_Feature_Extraction.ipynb
index f04762ac2aaa0e462f5850484d7462d2f30f27f6..d4123bb6db80ab5cd552c316beafa75dc8e9bb6f 100644
--- a/notebooks/03_Feature_Extraction.ipynb
+++ b/notebooks/03_Feature_Extraction.ipynb
@@ -23,7 +23,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -41,7 +41,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -59,7 +59,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -89,7 +89,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
@@ -103,13 +103,13 @@
       "  - 0/0 module:          MobileNet_v2\n",
       "  - 0/0 filename or url: https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/2\n",
       "  - 0/0 RGB image size : (224, 224)\n",
-      "  - 0/0 datset subsets : ['test_02', 'valid_01', 'train_01', 'test_01']\n",
+      "  - 0/0 datset subsets : ['valid_01', 'test_01', 'train_01', 'test_02']\n",
       "  - Cataloging subsets, then extracting features from all images\n",
       "  - Important: Each subset will be saved as one matrix\n",
       "\n",
       "\n",
-      "Found 73 images belonging to 7 classes.\n",
       "Found 145 images belonging to 7 classes.\n",
+      "Found 73 images belonging to 7 classes.\n",
       "Found 441 images belonging to 7 classes.\n",
       "Found 73 images belonging to 7 classes.\n",
       "INFO:tensorflow:Saver not created because there are no variables in the graph to restore\n"
@@ -122,128 +122,58 @@
       "INFO:tensorflow:Saver not created because there are no variables in the graph to restore\n"
      ]
     },
-    {
-     "ename": "UnidentifiedImageError",
-     "evalue": "cannot identify image file <_io.BytesIO object at 0x7faab04b9fb0>",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mUnidentifiedImageError\u001b[0m                    Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-6-577e7ff79add>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     66\u001b[0m             \u001b[0;31m# .. other,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     67\u001b[0m             \u001b[0msave_files\u001b[0m       \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 68\u001b[0;31m             \u001b[0mverbose\u001b[0m          \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     69\u001b[0m         )\n",
-      "\u001b[0;32m/work/amld-2021-workshop/src/utils/feature_extraction_tools.py\u001b[0m in \u001b[0;36mencode_images\u001b[0;34m(dataset_name, load_dir, subset_names, save_dir, module_name, module_location, img_target_size, generator_batch_size, generator_shuffle, save_files, verbose)\u001b[0m\n\u001b[1;32m    261\u001b[0m         \u001b[0;31m# img batch encoding, ...............................................\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    262\u001b[0m         \u001b[0;31m#.. Load batch,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 263\u001b[0;31m         \u001b[0mimg_batch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mimg_labels\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0miterators_dct\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0msetname\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    264\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    265\u001b[0m         \u001b[0;31m#.. Extract features from the batch of images,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/opt/conda/lib/python3.7/site-packages/keras_preprocessing/image/iterator.py\u001b[0m in \u001b[0;36m__next__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    102\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    103\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__next__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\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[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 104\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnext\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    105\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    106\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mnext\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[0;32m/opt/conda/lib/python3.7/site-packages/keras_preprocessing/image/iterator.py\u001b[0m in \u001b[0;36mnext\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    114\u001b[0m         \u001b[0;31m# The transformation of images is not under thread lock\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    115\u001b[0m         \u001b[0;31m# so it can be done in parallel\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 116\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_batches_of_transformed_samples\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindex_array\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    117\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    118\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_get_batches_of_transformed_samples\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindex_array\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/opt/conda/lib/python3.7/site-packages/keras_preprocessing/image/iterator.py\u001b[0m in \u001b[0;36m_get_batches_of_transformed_samples\u001b[0;34m(self, index_array)\u001b[0m\n\u001b[1;32m    228\u001b[0m                            \u001b[0mcolor_mode\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolor_mode\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    229\u001b[0m                            \u001b[0mtarget_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtarget_size\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 230\u001b[0;31m                            interpolation=self.interpolation)\n\u001b[0m\u001b[1;32m    231\u001b[0m             \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mimg_to_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata_format\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata_format\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    232\u001b[0m             \u001b[0;31m# Pillow images should be closed after `load_img`,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/opt/conda/lib/python3.7/site-packages/keras_preprocessing/image/utils.py\u001b[0m in \u001b[0;36mload_img\u001b[0;34m(path, grayscale, color_mode, target_size, interpolation)\u001b[0m\n\u001b[1;32m    112\u001b[0m                           'The use of `load_img` requires PIL.')\n\u001b[1;32m    113\u001b[0m     \u001b[0;32mwith\u001b[0m \u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'rb'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 114\u001b[0;31m         \u001b[0mimg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpil_image\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mio\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mBytesIO\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\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    115\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mcolor_mode\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'grayscale'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    116\u001b[0m             \u001b[0;31m# if image is not already an 8-bit, 16-bit or 32-bit grayscale image\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/opt/conda/lib/python3.7/site-packages/PIL/Image.py\u001b[0m in \u001b[0;36mopen\u001b[0;34m(fp, mode, formats)\u001b[0m\n\u001b[1;32m   2966\u001b[0m         \u001b[0mwarnings\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwarn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmessage\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2967\u001b[0m     raise UnidentifiedImageError(\n\u001b[0;32m-> 2968\u001b[0;31m         \u001b[0;34m\"cannot identify image file %r\"\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mfilename\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mfilename\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mfp\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   2969\u001b[0m     )\n\u001b[1;32m   2970\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mUnidentifiedImageError\u001b[0m: cannot identify image file <_io.BytesIO object at 0x7faab04b9fb0>"
-     ]
-    }
-   ],
-   "source": [
-    "# preset values \n",
-    "generator_batch_size = 3000 # no more then 3000 images will be taken,  but we expect no more then 2000 in that tassk. \n",
-    "use_url = \"yes\" # the script is adapted only to use sys.path, but configs carries url's and ulr can be used with feature extraction function \n",
-    "\n",
-    "# extract features from images in each dataset varinat using one or more tf hub modules, \n",
-    "for dv_i, dataset_variant in enumerate(DATASET_VARIANTS):\n",
-    "    \n",
-    "    print(f\"\\n- {dv_i} - Extracting features from: {dataset_variant}\")\n",
-    "    \n",
-    "    # find names off train/valid/test subsets in dataset folder,\n",
-    "    os.chdir(os.path.join(PATH_interim, f\"{DATASET_NAME}__{dataset_variant}\"))\n",
-    "    subset_names_to_encode = []\n",
-    "    for file in glob.glob(f\"[train|valid|test]*\"):\n",
-    "        subset_names_to_encode.append(file)\n",
-    "        \n",
-    "        \n",
-    "    # Create lists with info required for feture extraction from images \n",
-    "    'this step is super usefull when many models is used for feature extraction'\n",
-    "    tfmodules          = list(TFHUB_MODELS.keys()) # names of tf hub models used \n",
-    "    module_names       = [TFHUB_MODELS[x]['module_name'] for x in tfmodules]\n",
-    "    #module_file_names  = [TFHUB_MODELS[x]['file_name'] for x in tfmodules]\n",
-    "    module_file_names  = [TFHUB_MODELS[x]['module_url'] for x in tfmodules]\n",
-    "    img_imput_size     = [TFHUB_MODELS[x]['input_size'] for x in tfmodules]\n",
-    "\n",
-    "    \n",
-    "    # extract features, from images from each subset, and store them togther as one batch array,  \n",
-    "    for i, (one_module_name, one_module_file_name, one_img_input_size) in enumerate(zip(module_names, module_file_names, img_imput_size)):\n",
-    "        '''\n",
-    "            all data subsets found in load_dir will be encoded automatically, \n",
-    "            - logfile will be created for a given datasets\n",
-    "            - batch_labels csv file and npz file with encoded features will be created for \n",
-    "            each data subset will have:\n",
-    "            - \n",
-    "        '''\n",
-    "        print(\"\\n ................................................\")\n",
-    "        print(f\"  - {dv_i}/{i} module:          {one_module_name}\")\n",
-    "        print(f\"  - {dv_i}/{i} filename or url: {one_module_file_name}\")\n",
-    "        print(f\"  - {dv_i}/{i} RGB image size : {one_img_input_size}\")\n",
-    "        print(f\"  - {dv_i}/{i} datset subsets : {subset_names_to_encode}\")\n",
-    "        print(f\"  - Cataloging subsets, then extracting features from all images\")\n",
-    "        print(f\"  - Important: Each subset will be saved as one matrix\")\n",
-    "        print(\"\\n\")\n",
-    "\n",
-    "        # I am using modules saved in computer memory, thus I need to build fiull path to them, \n",
-    "        if use_url==\"no\":\n",
-    "            one_module_full_path = os.path.join(PATH_models, one_module_file_name)\n",
-    "        else:\n",
-    "            one_module_full_path = one_module_file_name # here I am using module url, (no path)\n",
-    " \n",
-    "        # extract features    \n",
-    "        encode_images(\n",
-    "\n",
-    "            # .. dastaset name & directories, \n",
-    "            dataset_name     = f\"{DATASET_NAME}__{dataset_variant}\",# name used when saving encoded files, logfiles and other things, related to encoding, \n",
-    "            subset_names     = subset_names_to_encode,# list, ust names of files in the load_dir, if any, \n",
-    "            load_dir         = os.path.join(PATH_interim, f\"{DATASET_NAME}__{dataset_variant}\"),   # full path to input data, ie. file folder with either folders with images names after class names, or folders with subsetnames, and folders names after each class in them, \n",
-    "            save_dir         = os.path.join(PATH_interim, f\"{DATASET_NAME}__{dataset_variant}__extracted_beta_features\"), # all new files, will be saved as one batch, with logfile, if None, load_dir will be used, \n",
-    "\n",
-    "            # .. encoding module parameters, \n",
-    "            module_name          = one_module_name, # name used when saving files\n",
-    "            module_location      = one_module_full_path, # full path to a given module, or url, \n",
-    "            img_target_size      = one_img_input_size, # image resolution in pixels, \n",
-    "            generator_batch_size = generator_batch_size, # must be larger or equal to in size of the largest subset\n",
-    "            generator_shuffle    = False, \n",
-    "\n",
-    "            # .. other, \n",
-    "            save_files       = True,\n",
-    "            verbose          = False                            \n",
-    "        )"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\n",
-      "- 0 - Extracting features from: cancer_detection_and_classification\n",
       "\n",
       " ................................................\n",
-      "  - 0/0 module:          MobileNet_v2\n",
-      "  - 0/0 filename or url: https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/2\n",
-      "  - 0/0 RGB image size : (224, 224)\n",
-      "  - 0/0 datset subsets : ['test_02', 'valid_01', 'train_01', 'test_01']\n",
+      "  - 0/1 module:          BiT_M_Resnet101\n",
+      "  - 0/1 filename or url: https://tfhub.dev/google/bit/m-r101x1/1\n",
+      "  - 0/1 RGB image size : (224, 224)\n",
+      "  - 0/1 datset subsets : ['valid_01', 'test_01', 'train_01', 'test_02']\n",
       "  - Cataloging subsets, then extracting features from all images\n",
       "  - Important: Each subset will be saved as one matrix\n",
       "\n",
       "\n",
-      "Found 73 images belonging to 7 classes.\n",
       "Found 145 images belonging to 7 classes.\n",
+      "Found 73 images belonging to 7 classes.\n",
       "Found 441 images belonging to 7 classes.\n",
       "Found 73 images belonging to 7 classes.\n",
+      "WARNING:tensorflow:From /Users/pawel/anaconda3/envs/exts-ml/lib/python3.6/site-packages/tensorflow_core/python/ops/resource_variable_ops.py:1781: calling BaseResourceVariable.__init__ (from tensorflow.python.ops.resource_variable_ops) with constraint is deprecated and will be removed in a future version.\n",
+      "Instructions for updating:\n",
+      "If using Keras pass *_constraint arguments to layers.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING:tensorflow:From /Users/pawel/anaconda3/envs/exts-ml/lib/python3.6/site-packages/tensorflow_core/python/ops/resource_variable_ops.py:1781: calling BaseResourceVariable.__init__ (from tensorflow.python.ops.resource_variable_ops) with constraint is deprecated and will be removed in a future version.\n",
+      "Instructions for updating:\n",
+      "If using Keras pass *_constraint arguments to layers.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "- 1 - Extracting features from: cancer_risk_groups\n",
+      "\n",
+      " ................................................\n",
+      "  - 1/0 module:          MobileNet_v2\n",
+      "  - 1/0 filename or url: https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/2\n",
+      "  - 1/0 RGB image size : (224, 224)\n",
+      "  - 1/0 datset subsets : ['valid_01', 'test_01', 'train_01', 'test_02']\n",
+      "  - Cataloging subsets, then extracting features from all images\n",
+      "  - Important: Each subset will be saved as one matrix\n",
+      "\n",
+      "\n",
+      "Found 147 images belonging to 3 classes.\n",
+      "Found 73 images belonging to 3 classes.\n",
+      "Found 443 images belonging to 3 classes.\n",
+      "Found 73 images belonging to 3 classes.\n",
       "INFO:tensorflow:Saver not created because there are no variables in the graph to restore\n"
      ]
     },
@@ -255,344 +185,27 @@
      ]
     },
     {
-     "ename": "UnidentifiedImageError",
-     "evalue": "cannot identify image file <_io.BytesIO object at 0x7faab0563fb0>",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mUnidentifiedImageError\u001b[0m                    Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-9-1cf21f7f72fd>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m    386\u001b[0m             \u001b[0;31m# .. other,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    387\u001b[0m             \u001b[0msave_files\u001b[0m       \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 388\u001b[0;31m             \u001b[0mverbose\u001b[0m          \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    389\u001b[0m         )    \n",
-      "\u001b[0;32m<ipython-input-9-1cf21f7f72fd>\u001b[0m in \u001b[0;36mencode_images\u001b[0;34m(dataset_name, load_dir, subset_names, save_dir, module_name, module_location, img_target_size, generator_batch_size, generator_shuffle, save_files, verbose)\u001b[0m\n\u001b[1;32m    240\u001b[0m         \u001b[0;31m# img batch encoding, ...............................................\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    241\u001b[0m         \u001b[0;31m#.. Load batch,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 242\u001b[0;31m         \u001b[0mimg_batch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mimg_labels\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0miterators_dct\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0msetname\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    243\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    244\u001b[0m         \u001b[0;31m#.. Extract features from the batch of images,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/opt/conda/lib/python3.7/site-packages/keras_preprocessing/image/iterator.py\u001b[0m in \u001b[0;36m__next__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    102\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    103\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__next__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\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[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 104\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnext\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    105\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    106\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mnext\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[0;32m/opt/conda/lib/python3.7/site-packages/keras_preprocessing/image/iterator.py\u001b[0m in \u001b[0;36mnext\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    114\u001b[0m         \u001b[0;31m# The transformation of images is not under thread lock\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    115\u001b[0m         \u001b[0;31m# so it can be done in parallel\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 116\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_batches_of_transformed_samples\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindex_array\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    117\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    118\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_get_batches_of_transformed_samples\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindex_array\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/opt/conda/lib/python3.7/site-packages/keras_preprocessing/image/iterator.py\u001b[0m in \u001b[0;36m_get_batches_of_transformed_samples\u001b[0;34m(self, index_array)\u001b[0m\n\u001b[1;32m    228\u001b[0m                            \u001b[0mcolor_mode\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolor_mode\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    229\u001b[0m                            \u001b[0mtarget_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtarget_size\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 230\u001b[0;31m                            interpolation=self.interpolation)\n\u001b[0m\u001b[1;32m    231\u001b[0m             \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mimg_to_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata_format\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata_format\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    232\u001b[0m             \u001b[0;31m# Pillow images should be closed after `load_img`,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/opt/conda/lib/python3.7/site-packages/keras_preprocessing/image/utils.py\u001b[0m in \u001b[0;36mload_img\u001b[0;34m(path, grayscale, color_mode, target_size, interpolation)\u001b[0m\n\u001b[1;32m    112\u001b[0m                           'The use of `load_img` requires PIL.')\n\u001b[1;32m    113\u001b[0m     \u001b[0;32mwith\u001b[0m \u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'rb'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 114\u001b[0;31m         \u001b[0mimg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpil_image\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mio\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mBytesIO\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\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    115\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mcolor_mode\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'grayscale'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    116\u001b[0m             \u001b[0;31m# if image is not already an 8-bit, 16-bit or 32-bit grayscale image\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/opt/conda/lib/python3.7/site-packages/PIL/Image.py\u001b[0m in \u001b[0;36mopen\u001b[0;34m(fp, mode, formats)\u001b[0m\n\u001b[1;32m   2966\u001b[0m         \u001b[0mwarnings\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwarn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmessage\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2967\u001b[0m     raise UnidentifiedImageError(\n\u001b[0;32m-> 2968\u001b[0;31m         \u001b[0;34m\"cannot identify image file %r\"\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mfilename\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mfilename\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mfp\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   2969\u001b[0m     )\n\u001b[1;32m   2970\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mUnidentifiedImageError\u001b[0m: cannot identify image file <_io.BytesIO object at 0x7faab0563fb0>"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      " ................................................\n",
+      "  - 1/1 module:          BiT_M_Resnet101\n",
+      "  - 1/1 filename or url: https://tfhub.dev/google/bit/m-r101x1/1\n",
+      "  - 1/1 RGB image size : (224, 224)\n",
+      "  - 1/1 datset subsets : ['valid_01', 'test_01', 'train_01', 'test_02']\n",
+      "  - Cataloging subsets, then extracting features from all images\n",
+      "  - Important: Each subset will be saved as one matrix\n",
+      "\n",
+      "\n",
+      "Found 147 images belonging to 3 classes.\n",
+      "Found 73 images belonging to 3 classes.\n",
+      "Found 443 images belonging to 3 classes.\n",
+      "Found 73 images belonging to 3 classes.\n"
      ]
     }
    ],
    "source": [
-    "import os # allow changing, and navigating files and folders, \n",
-    "import sys\n",
-    "import re # module to use regular expressions, \n",
-    "import glob # lists names in folders that match Unix shell patterns\n",
-    "import random # functions that use and generate random numbers\n",
-    "\n",
-    "import numpy as np # support for multi-dimensional arrays and matrices\n",
-    "import pandas as pd # library for data manipulation and analysis\n",
-    "import seaborn as sns # advance plots, for statistics, \n",
-    "import matplotlib.pyplot as plt # for making plots, \n",
-    "import matplotlib as mpl # to get some basif functions, heping with plot mnaking \n",
-    "import tensorflow as tf\n",
-    "import tensorflow_hub as hub\n",
-    "import scipy.stats as stats  # library for statistics and technical programming, \n",
-    "import tensorflow.keras as keras  \n",
-    "\n",
-    "from PIL import Image, ImageDraw\n",
-    "from IPython.display import display\n",
-    "from tensorflow.keras import backend as K # used for housekeeping of tf models,\n",
-    "import matplotlib.patches as mpatches\n",
-    "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
-    "from src.utils.image_augmentation import * # some basic tools create for image augmentastion using keras tools, here used for building batch_labels table,  \n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "# Function, .................................................................................................................\n",
-    "def encode_images(*, \n",
-    "    # .. dastaset name & directories, \n",
-    "    dataset_name=None,# dataset name used when saving encoded files, logfiles and other things, related to encoding, \n",
-    "    load_dir=None,   # full path to input data, ie. file folder with either folders with images names after class names, or folders with subsetnames, and folders names after each class in them, \n",
-    "    subset_names=None,# list, ust names of files in the load_dir, if any, \n",
-    "    save_dir=None, # all new files, will be saved as one batch, with logfile, if None, load_dir will be used, \n",
-    "\n",
-    "    # .. encoding module parameters, \n",
-    "    module_name=None, \n",
-    "    module_location, # full path to a given module,\n",
-    "    img_target_size, # image resolution in pixels, \n",
-    "    generator_batch_size =1000, \n",
-    "    generator_shuffle    =False, \n",
-    "        \n",
-    "    # .. other, \n",
-    "    save_files=True,\n",
-    "    verbose=True                             \n",
-    "):\n",
-    "    \"\"\"\n",
-    "        Function does the following:\n",
-    "        - extracts features from rgb figures, \n",
-    "        - saves each batch in npy file format, [img_nr, x_pixel, y_pixel, 3]\n",
-    "        - saves csv file with info on each image in that file, \n",
-    "        - creates log file with info on files with images that were encoded and \n",
-    "        \n",
-    "        # Arguments\n",
-    "        . datase_tname          : str, an arbitrary name used at the beginning of all files created\n",
-    "        . module_name          : str  eg: \"mobilenet\", used as above in each file create with that function, \n",
-    "        . module_location      : str, either ulr to tfhub module used for feature extraction,\n",
-    "                                      or path to directory with locally saved module, \n",
-    "        . load_dir             : str, path to directory that contains the folder with folders containgn classes, \n",
-    "        . save_dir             : str, path to directory whther all files will be stored, \n",
-    "        . folders_in_load_dir  : list with strings, each string is an exact folder name, that contains images stored in folders with class names, \n",
-    "        . img_target_size      : tuple, (int, int), img (height, width) size in pixesl, Caution, this functions works only with RGB FILES, \n",
-    "                                 thus, the final, batch dimensions are [?, height, width, 3]\n",
-    "        . generator_batch_size : int, value for keras, ImageDataGenerator.flow_from_directory, batch size, \n",
-    "                                 the generator will use only max available images, if less is in the dataset,\n",
-    "                                 or only the first 10000 or other requested number, if more is available, \n",
-    "                                 be carefull to set generator_shuffle = True, while using a subset of images to have all classes\n",
-    "                                 represented, \n",
-    "        . generator_shuffle    : bool, value for keras, ImageDataGenerator.flow_from_directory,                  \n",
-    "        . ...\n",
-    "        . verbose              : bool, default==False, \n",
-    "        . save_files           : bool, if True, encoded batch, logfile, and labels, will be saved ib save_dir, \n",
-    "                                       if False, encoded batch and labels will be returned as dictionaries in list\n",
-    "                                       with folders_in_load_dir as keys, no log file will be created, \n",
-    "        # Returns\n",
-    "        . log file             : pd.dataFrame, with basic info on each dataset encoded, and names used to save files for it, \n",
-    "        \n",
-    "        # saves\n",
-    "        . log file             : pd.dataFrame, saved in save_dir as .csv\n",
-    "        . img_batch_info       : pd.dataFrame, saved in save_dir as .csv\n",
-    "        . img_batch_features   : .npy with [(images found in a given set), feture_number], with encoded features from the given module, \n",
-    "        \n",
-    "        # example - use case, \n",
-    "\n",
-    "        # ... from, \n",
-    "        module_path =  os.path.join(basedir,\"models/imagenet_mobilenet_v2_100_224_feature_vector_2\") # path to local directory, \n",
-    "        module_path = 'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/2' # as url\n",
-    "        # ...\n",
-    "        module_name = \"mobilenet\"\n",
-    "        module_img_imput_size = (224, 224)  \n",
-    "        # ....\n",
-    "        encodedimageges_dct, lables_dct = encode_images_with_tfhubmodule( \n",
-    "                    datasetname=\"augmented_swissroads\", \n",
-    "                    module_name=module_name, \n",
-    "                    module_location=module_path, \n",
-    "                    load_dir= os.path.join(basedir, \"data/interim/augmented_swissroads/\"), \n",
-    "                    folders_in_load_dir=[\"test\"], \n",
-    "                    save_dir=os.path.join(basedir, \"data/interim/augmented_swissroads/\"),\n",
-    "                    img_target_size=module_img_imput_size,                     \n",
-    "                    save_files=True,\n",
-    "                    verbose=True\n",
-    "            )\n",
-    "           \n",
-    "    \"\"\"\n",
-    "\n",
-    "    # ...........................................................................\n",
-    "    # set up, \n",
-    "    \n",
-    "    if load_dir==None:\n",
-    "        load_dir = os.path.dirname(os.getcwd())\n",
-    "    else:\n",
-    "        pass\n",
-    "    \n",
-    "    if save_dir==None:\n",
-    "        save_dir = load_dir\n",
-    "    else:\n",
-    "        pass\n",
-    "        \n",
-    "    if module_name==None:\n",
-    "        module_name = \"tfmodule\"\n",
-    "    else:\n",
-    "        pass\n",
-    "    \n",
-    "    if dataset_name==None:\n",
-    "        dataset_name = \"encodeddata\"\n",
-    "    else:\n",
-    "        pass\n",
-    "    \n",
-    "    # create save directory, if not available, \n",
-    "    try:\n",
-    "        os.mkdir(save_dir)\n",
-    "        if verbose==True:\n",
-    "            print(f\"Created: {save_dir}\")\n",
-    "    except: \n",
-    "        pass\n",
-    "    \n",
-    "                \n",
-    "    \n",
-    "    # ........................................................................\n",
-    "    # Create iterators for each dataset, \n",
-    "    \n",
-    "    # Create Image generator, with rescaling, - I assumed we will wirk on rgb pictures, \n",
-    "    datagen = ImageDataGenerator(rescale=1/255)\n",
-    "\n",
-    "    #.. collect iterators for each subset, if available, and place them in dictionary, \n",
-    "    iterators_dct = dict()\n",
-    "    \n",
-    "    # .. - there is more then one subset of the data, eg validation and test and train data \n",
-    "    if subset_names!=None:\n",
-    "        for setname in subset_names:    \n",
-    "            iterators_dct[setname] = datagen.flow_from_directory(\n",
-    "                os.path.join(load_dir, setname), # each subset is loaded separately, \n",
-    "                \n",
-    "                #batch_size=generator_batch_size, # it will use only max available pictures in a given folder,  \n",
-    "                batch_size=1, # it will use only max available pictures in a given folder,  \n",
-    "                                \n",
-    "                target_size=img_target_size, \n",
-    "                shuffle=generator_shuffle     # here I use generator only to explote the data\n",
-    "                )\n",
-    "    else:\n",
-    "        iterators_dct[dataset_name] = datagen.flow_from_directory(\n",
-    "                load_dir,\n",
-    "                batch_size=generator_batch_size, # it will use only max available pictures in a given folder,  \n",
-    "                target_size=img_target_size, \n",
-    "                shuffle=generator_shuffle     # here I use generator only to explote the data\n",
-    "                )\n",
-    "        \n",
-    "    # ..\n",
-    "    if verbose==True: \n",
-    "        print(f\"\\n\\n{''.join(['.']*80)}\\n Creating DataGenerators for: {dataset_name}; {module_name};\\n{''.join(['.']*80)}\\n\")\n",
-    "    else:\n",
-    "        pass    \n",
-    "    \n",
-    "    \n",
-    "    \n",
-    "    # .......................................................................    \n",
-    "    # finally, in case there are no subsets, use the same name for subset as for dataset in all subsequent datasets, \n",
-    "    if subset_names==None:\n",
-    "        subset_names = [dataset_name]\n",
-    "    else:\n",
-    "        pass    \n",
-    "    \"important - it must be done here !\"\n",
-    "    \n",
-    "    \n",
-    "    \n",
-    "    # .......................................................................  \n",
-    "    # Create tf graph,\n",
-    "    img_graph = tf.Graph()\n",
-    "\n",
-    "    with img_graph.as_default():\n",
-    "\n",
-    "        # load the module, by default i set TF 1.x models, and if they dont work, I assumed that you will load TF 2.X modules with hub.load() functions used as below\n",
-    "        try:\n",
-    "            feature_extractor = hub.Module(module_location, trainable=False) # tf1 modules, tahes a bit of time, to check if there is an error, but it was the fasted I could use with coding, \n",
-    "        except:\n",
-    "            feature_extractor = hub.KerasLayer(module_location) # tf2 modules,\n",
-    "            \n",
-    "        # Create input placeholder for imput data, \n",
-    "        input_imgs = tf.compat.v1.placeholder(dtype=tf.float32, shape=[None, img_target_size[0], img_target_size[1], 3])\n",
-    "\n",
-    "        # A node with the extracted high-level features\n",
-    "        imgs_features = feature_extractor(input_imgs)\n",
-    "\n",
-    "        # Collect initializers\n",
-    "        init_op = tf.group([                              # groups different type of initializers into one init_op\n",
-    "            tf.compat.v1.global_variables_initializer(),  # used for almost all tf graphs, \n",
-    "            tf.compat.v1.tables_initializer()             # speciffic for tfhub\n",
-    "        ])\n",
-    "\n",
-    "    img_graph.finalize() # Good practice: make the graph \"read-only\"\n",
-    "\n",
-    "    #.. create and initialize the session, \n",
-    "    \"following instruction on: https://www.tensorflow.org/hub/common_issues\"\n",
-    "    sess = tf.compat.v1.Session(graph=img_graph)\n",
-    "    sess.run(init_op)    \n",
-    "    \n",
-    "    #.. info on tf graph, \n",
-    "    if verbose==True:\n",
-    "        print(f\"\\n{''.join(['.']*80)}\\n TF Graph;\")\n",
-    "        print(f\"Feature extraction Module, from: {module_location}\")\n",
-    "        #print(\"Signatures: \",feature_extractor.get_signature_names()) # eg: ['default', 'image_feature_vector'] - signatures, can be used to extract subset of NN layers\n",
-    "        #print(\"Expected Imput shape, dtype: \", feature_extractor.get_input_info_dict()) # eg: {'images': <hub.ParsedTensorInfo shape=(?, 224, 224, 3) dtype=float32 is_sparse=False>}\n",
-    "        #print(\"Expected Output shape, dtype: \",feature_extractor.get_output_info_dict()) # eg: {'default': <hub.ParsedTensorInfo shape=(?, 1280) dtype=float32 is_sparse=False>}\n",
-    "\n",
-    "        \n",
-    "        \n",
-    "        \n",
-    "    # .......................................................................   \n",
-    "    # Use iterators, and tf graph, extract high level features,\n",
-    "    \"extract features and labels with each batch, and save as npz, file\"\n",
-    "    \n",
-    "    # objects to store new before saving or returning, \n",
-    "    file_sets = list()\n",
-    "    encoded_batch_dict = dict()\n",
-    "    batch_labels_dict = dict()\n",
-    "    \n",
-    "    # ..\n",
-    "    for setname in subset_names:\n",
-    "              \n",
-    "        # img batch encoding, ...............................................\n",
-    "        #.. Load batch,\n",
-    "        img_batch, img_labels = next(iterators_dct[setname])\n",
-    "\n",
-    "        #.. Extract features from the batch of images, \n",
-    "        img_batch_features = sess.run(imgs_features, feed_dict={input_imgs: img_batch})\n",
-    "        \n",
-    "        #.. add to dict\n",
-    "        encoded_batch_dict[setname] = img_batch_features \n",
-    "        \n",
-    "        # create human-friendly labels, ..................................... \n",
-    "        'prepare info on each image'\n",
-    "        to_forget, img_batch_info = create_augmented_images(external_generator=iterators_dct[setname], augm_img_nr=0) # used only to get info df, \n",
-    "        \n",
-    "        #.. add to dict\n",
-    "        batch_labels_dict[setname] = img_batch_info\n",
-    "        \n",
-    "        # info\n",
-    "        if verbose==True: \n",
-    "            print(f\"{''.join(['.']*80)}\\n Ecoding imgages in one batch for < {setname} > dataset;\")\n",
-    "            print(f\"Feature number = {img_batch_features.shape}\")\n",
-    "            print(f\"label table shape = {img_batch_info.shape}\")        \n",
-    "        \n",
-    "\n",
-    "        \n",
-    "        # save files in save_dir, ...........................................\n",
-    "        if save_files==True:\n",
-    "\n",
-    "            # save img_batch_features as .npy file\n",
-    "            os.chdir(save_dir)\n",
-    "            encoded_img_file_name = f\"{module_name}_{dataset_name}_{setname}_encoded.npy\"\n",
-    "            np.save(encoded_img_file_name , img_batch_features)\n",
-    "\n",
-    "            # save labels as csv, \n",
-    "            batch_label_file_name = f\"{module_name}_{dataset_name}_{setname}_labels.csv\"\n",
-    "            img_batch_info.to_csv(batch_label_file_name, header=True, index=False)\n",
-    "            \n",
-    "            # info on the above, \n",
-    "            if verbose==True: \n",
-    "                print(f\"Saved as:\\n{encoded_img_file_name} and {batch_label_file_name}\")\n",
-    "                print(f\"saved in:\\n{os.getcwd()}\")        \n",
-    "\n",
-    "            # create log file required for easy loading of the batch and lebel files,   \n",
-    "            file_sets.append({\n",
-    "                \"module_name\": module_name,\n",
-    "                \"datasetname\": setname,\n",
-    "                \"img_batch_features_filename\": encoded_img_file_name,\n",
-    "                \"img_batch_info_filename\": batch_label_file_name,\n",
-    "                \"batch_size\": img_batch_info.shape[0],\n",
-    "                \"created_dt\":pd.to_datetime(\"now\"),\n",
-    "                \"module_source\": module_location\n",
-    "            }) \n",
-    "            \n",
-    "            \n",
-    "    # .......................................................................             \n",
-    "    # Save log table for entire datset, with all subsets in different rows,  \n",
-    "    if save_files==True:\n",
-    "        \n",
-    "        os.chdir(save_dir)\n",
-    "        summary_table_filename = f\"{module_name}_{dataset_name}_logfile.csv\"\n",
-    "        summary_table = pd.DataFrame(file_sets)\n",
-    "        summary_table.to_csv(summary_table_filename, header=True, index=False)\n",
-    "\n",
-    "        #.. info\n",
-    "        if verbose==True: \n",
-    "            print(f\"{''.join(['.']*80)}\\n Creating logfile for < {dataset_name} >;\") \n",
-    "            print(f\"saved as:  {summary_table_filename}\") \n",
-    "            print(f\"in: {save_dir}\") \n",
-    "  \n",
-    "    \n",
-    "    # return the batch and labels, just in case, \n",
-    "    return encoded_batch_dict, batch_labels_dict\n",
-    "\n",
-    "\n",
-    "  \n",
-    "\n",
-    "\n",
-    "    \n",
-    "    \n",
-    "    \n",
-    "    \n",
     "# preset values \n",
     "generator_batch_size = 3000 # no more then 3000 images will be taken,  but we expect no more then 2000 in that tassk. \n",
     "use_url = \"yes\" # the script is adapted only to use sys.path, but configs carries url's and ulr can be used with feature extraction function \n",
@@ -661,770 +274,36 @@
     "            # .. other, \n",
     "            save_files       = True,\n",
     "            verbose          = False                            \n",
-    "        )    "
+    "        )"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 10,
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [],
    "source": [
-    "import os\n",
-    "import cv2\n",
-    "bad_list=[]\n",
-    "dir=PATH_interim"
+    "# Troubleshot"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
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-      "file /work/amld-2021-workshop/data/interim/ham10000_workshop__cancer_risk_groups/test_02/Medium-benign_cancer/ISIC_0028059.jpg is not a valid image file \n",
-      "file /work/amld-2021-workshop/data/interim/ham10000_workshop__cancer_risk_groups/test_02/Medium-benign_cancer/ISIC_0024707.jpg is not a valid image file \n",
-      "file /work/amld-2021-workshop/data/interim/ham10000_workshop__cancer_risk_groups/test_02/Medium-benign_cancer/ISIC_0024504.jpg is not a valid image file \n",
-      "file /work/amld-2021-workshop/data/interim/ham10000_workshop__cancer_risk_groups/test_02/Medium-benign_cancer/ISIC_0024420.jpg is not a valid image file \n",
-      "file /work/amld-2021-workshop/data/interim/ham10000_workshop__cancer_risk_groups/test_02/Medium-benign_cancer/ISIC_0029345.jpg is not a valid image file \n",
-      "file /work/amld-2021-workshop/data/interim/ham10000_workshop__cancer_risk_groups/test_02/Medium-benign_cancer/ISIC_0024890.jpg is not a valid image file \n",
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-      "file /work/amld-2021-workshop/data/interim/ham10000_workshop__cancer_risk_groups/test_02/Low-skin_lession/ISIC_0029671.jpg is not a valid image file \n",
-      "file /work/amld-2021-workshop/data/interim/ham10000_workshop__cancer_risk_groups/test_02/Low-skin_lession/ISIC_0028919.jpg is not a valid image file \n",
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-      "file /work/amld-2021-workshop/data/interim/ham10000_workshop__cancer_risk_groups/test_02/Low-skin_lession/ISIC_0031472.jpg is not a valid image file \n",
-      "file /work/amld-2021-workshop/data/interim/ham10000_workshop__cancer_risk_groups/test_02/Low-skin_lession/ISIC_0031871.jpg is not a valid image file \n",
-      "file /work/amld-2021-workshop/data/interim/ham10000_workshop__cancer_risk_groups/test_02/Low-skin_lession/ISIC_0027288.jpg is not a valid image file \n",
-      "file /work/amld-2021-workshop/data/interim/ham10000_workshop__cancer_risk_groups/test_02/Low-skin_lession/ISIC_0030718.jpg is not a valid image file \n",
-      "file /work/amld-2021-workshop/data/interim/ham10000_workshop__cancer_risk_groups/test_02/Low-skin_lession/ISIC_0024775.jpg is not a valid image file \n",
-      "file /work/amld-2021-workshop/data/interim/ham10000_workshop__cancer_risk_groups/test_02/Low-skin_lession/ISIC_0032089.jpg is not a valid image file \n",
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-      "file /work/amld-2021-workshop/data/interim/ham10000_workshop__cancer_risk_groups/test_01/Low-skin_lession/ISIC_0024423.jpg is not a valid image file \n",
-      "file /work/amld-2021-workshop/data/interim/ham10000_workshop__cancer_risk_groups/test_01/Low-skin_lession/ISIC_0029896.jpg is not a valid image file \n",
-      "file /work/amld-2021-workshop/data/interim/ham10000_workshop__cancer_risk_groups/test_01/Low-skin_lession/ISIC_0029965.jpg is not a valid image file \n",
-      "file /work/amld-2021-workshop/data/interim/ham10000_workshop__cancer_risk_groups/test_01/Low-skin_lession/ISIC_0026300.jpg is not a valid image file \n",
-      "file /work/amld-2021-workshop/data/interim/ham10000_workshop__cancer_risk_groups/test_01/Low-skin_lession/ISIC_0029103.jpg is not a valid image file \n",
-      "file /work/amld-2021-workshop/data/interim/ham10000_workshop__cancer_risk_groups/test_01/Low-skin_lession/ISIC_0028078.jpg is not a valid image file \n",
-      "file /work/amld-2021-workshop/data/interim/ham10000_workshop__cancer_risk_groups/test_01/Low-skin_lession/ISIC_0024316.jpg is not a valid image file \n",
-      "file /work/amld-2021-workshop/data/interim/ham10000_workshop__cancer_risk_groups/test_01/Low-skin_lession/ISIC_0033170.jpg is not a valid image file \n",
-      "file /work/amld-2021-workshop/data/interim/ham10000_workshop__cancer_risk_groups/test_01/Low-skin_lession/ISIC_0028757.jpg is not a valid image file \n",
-      "file /work/amld-2021-workshop/data/interim/ham10000_workshop__cancer_risk_groups/test_01/Low-skin_lession/ISIC_0029654.jpg is not a valid image file \n",
-      "file /work/amld-2021-workshop/data/interim/ham10000_workshop__cancer_risk_groups/test_01/Low-skin_lession/ISIC_0024603.jpg is not a valid image file \n",
-      "file /work/amld-2021-workshop/data/interim/ham10000_workshop__cancer_risk_groups/test_01/Low-skin_lession/ISIC_0030493.jpg is not a valid image file \n",
-      "file /work/amld-2021-workshop/data/interim/ham10000_workshop__cancer_risk_groups/test_01/Low-skin_lession/ISIC_0030392.jpg is not a valid image file \n",
-      "file /work/amld-2021-workshop/data/interim/ham10000_workshop__cancer_risk_groups/test_01/Low-skin_lession/ISIC_0033769.jpg is not a valid image file \n",
-      "file /work/amld-2021-workshop/data/interim/ham10000_workshop__cancer_risk_groups/test_01/Low-skin_lession/ISIC_0025428.jpg is not a valid image file \n",
-      "file /work/amld-2021-workshop/data/interim/ham10000_workshop__cancer_risk_groups/test_01/Low-skin_lession/ISIC_0030684.jpg is not a valid image file \n",
-      "file /work/amld-2021-workshop/data/interim/ham10000_workshop__cancer_risk_groups/test_01/Low-skin_lession/ISIC_0030237.jpg is not a valid image file \n",
-      "file /work/amld-2021-workshop/data/interim/ham10000_workshop__cancer_risk_groups/test_01/Low-skin_lession/ISIC_0024759.jpg is not a valid image file \n"
+     "ename": "NotADirectoryError",
+     "evalue": "[Errno 20] Not a directory: '/Users/pawel/Documents/GitHub_beta/amld-2021-workshop/data_2/interim/ham10000_workshop__cancer_risk_groups/train_01/.DS_Store'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mNotADirectoryError\u001b[0m                        Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-5-4eaf145ef37a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     17\u001b[0m             \u001b[0mclass_path\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdpath\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mklass\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# path to class directory\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     18\u001b[0m             \u001b[0;31m#print(class_path)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 19\u001b[0;31m             \u001b[0mfile_list\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlistdir\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mclass_path\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# create list of files in class directory\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     20\u001b[0m             \u001b[0;32mfor\u001b[0m \u001b[0mf\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mfile_list\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;31m# iterate through the files\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     21\u001b[0m                 \u001b[0mfpath\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjoin\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mclass_path\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mNotADirectoryError\u001b[0m: [Errno 20] Not a directory: '/Users/pawel/Documents/GitHub_beta/amld-2021-workshop/data_2/interim/ham10000_workshop__cancer_risk_groups/train_01/.DS_Store'"
      ]
     }
    ],
    "source": [
     "# script to test if all images are in correcto format\n",
+    "'shoudl be run in case the ablce function can not recognise image formatting'\n",
     "\n",
     "\n",
     "import os\n",