diff --git a/eval10_experiment5.ipynb b/eval10_experiment5.ipynb
new file mode 100644
index 0000000..b2b805a
--- /dev/null
+++ b/eval10_experiment5.ipynb
@@ -0,0 +1,552 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Load libraries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[31mmenpo 0.8.1 has requirement matplotlib<2.0,>=1.4, but you'll have matplotlib 3.0.2 which is incompatible.\u001b[0m\n",
+      "\u001b[31mmenpo 0.8.1 has requirement pillow<5.0,>=3.0, but you'll have pillow 5.4.1 which is incompatible.\u001b[0m\n",
+      "\u001b[31mmenpo 0.8.1 has requirement scipy<1.0,>=0.16, but you'll have scipy 1.2.0 which is incompatible.\u001b[0m\n",
+      "\u001b[33mYou are using pip version 10.0.1, however version 19.2.2 is available.\n",
+      "You should consider upgrading via the 'pip install --upgrade pip' command.\u001b[0m\n"
+     ]
+    }
+   ],
+   "source": [
+    "!pip install -q -r requirements.txt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
+    "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5"
+   },
+   "outputs": [],
+   "source": [
+    "import sys\n",
+    "import os\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "\n",
+    "from PIL import Image\n",
+    "\n",
+    "import torch\n",
+    "import torch.nn as nn\n",
+    "import torch.utils.data as D\n",
+    "from torch.optim.lr_scheduler import ExponentialLR\n",
+    "import torch.nn.functional as F\n",
+    "from torch.autograd import Variable\n",
+    "\n",
+    "from torchvision import transforms\n",
+    "\n",
+    "from ignite.engine import Events\n",
+    "from scripts.ignite import create_supervised_evaluator, create_supervised_trainer\n",
+    "from ignite.metrics import Loss, Accuracy\n",
+    "from ignite.contrib.handlers.tqdm_logger import ProgressBar\n",
+    "from ignite.handlers import  EarlyStopping, ModelCheckpoint\n",
+    "from ignite.contrib.handlers import LinearCyclicalScheduler, CosineAnnealingScheduler\n",
+    "\n",
+    "from tqdm import tqdm_notebook\n",
+    "\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "\n",
+    "from efficientnet_pytorch import EfficientNet\n",
+    "\n",
+    "from scripts.evaluate import eval_model, eval_model_10\n",
+    "\n",
+    "import warnings\n",
+    "warnings.filterwarnings('ignore')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Define dataset and model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "_cell_guid": "79c7e3d0-c299-4dcb-8224-4455121ee9b0",
+    "_uuid": "d629ff2d2480ee46fbb7e2d37f6b5fab8052498a"
+   },
+   "outputs": [],
+   "source": [
+    "img_dir = '../input/rxrxairgb'\n",
+    "path_data = '../input/rxrxaicsv'\n",
+    "device = 'cuda'\n",
+    "batch_size = 32\n",
+    "torch.manual_seed(0)\n",
+    "model_name = 'efficientnet-b3'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "jitter = (0.6, 1.4)\n",
+    "class ImagesDS(D.Dataset):\n",
+    "    # taken textbook from https://arxiv.org/pdf/1812.01187.pdf\n",
+    "    transform_train = transforms.Compose([\n",
+    "        transforms.RandomResizedCrop(224),\n",
+    "        transforms.ColorJitter(brightness=jitter, contrast=jitter, saturation=jitter, hue=.1),\n",
+    "        transforms.RandomHorizontalFlip(p=0.5),\n",
+    "        # PCA Noise should go here,\n",
+    "        transforms.ToTensor(),\n",
+    "        transforms.Normalize(mean=(123.68, 116.779, 103.939), std=(58.393, 57.12, 57.375))\n",
+    "    ])\n",
+    "    \n",
+    "    transform_validation = transforms.Compose([\n",
+    "        transforms.CenterCrop(224),\n",
+    "        transforms.ToTensor(),\n",
+    "        transforms.Normalize(mean=(123.68, 116.779, 103.939), std=(58.393, 57.12, 57.375))\n",
+    "    ])\n",
+    "\n",
+    "    def __init__(self, df, img_dir=img_dir, mode='train', validation=False, site=1):\n",
+    "        self.records = df.to_records(index=False)\n",
+    "        self.site = site\n",
+    "        self.mode = mode\n",
+    "        self.img_dir = img_dir\n",
+    "        self.len = df.shape[0]\n",
+    "        self.validation = validation\n",
+    "        \n",
+    "    @staticmethod\n",
+    "    def _load_img_as_tensor(file_name, validation):\n",
+    "        with Image.open(file_name) as img:\n",
+    "            if not validation:\n",
+    "                return ImagesDS.transform_train(img)\n",
+    "            else:\n",
+    "                return ImagesDS.transform_validation(img)\n",
+    "\n",
+    "    def _get_img_path(self, index, site=1):\n",
+    "        experiment, well, plate = self.records[index].experiment, self.records[index].well, self.records[index].plate\n",
+    "        return f'{self.img_dir}/{self.mode}/{experiment}_{plate}_{well}_s{site}.jpeg'\n",
+    "        \n",
+    "    def __getitem__(self, index):\n",
+    "        img1, img2 = [self._load_img_as_tensor(self._get_img_path(index, site), self.validation) for site in [1,2]]\n",
+    "        if self.mode == 'train':\n",
+    "            return img1, img2, int(self.records[index].sirna)\n",
+    "        else:\n",
+    "            return img1, img2, self.records[index].id_code\n",
+    "\n",
+    "    def __len__(self):\n",
+    "        return self.len\n",
+    "\n",
+    "\n",
+    "class TestImagesDS(D.Dataset):\n",
+    "    transform = transforms.Compose([\n",
+    "        transforms.RandomCrop(224),\n",
+    "        transforms.ToTensor(),\n",
+    "        transforms.Normalize(mean=(123.68, 116.779, 103.939), std=(58.393, 57.12, 57.375))\n",
+    "    ])\n",
+    "\n",
+    "    def __init__(self, df, img_dir=img_dir, mode='test', validation=False, site=1):\n",
+    "        self.records = df.to_records(index=False)\n",
+    "        self.site = site\n",
+    "        self.mode = mode\n",
+    "        self.img_dir = img_dir\n",
+    "        self.len = df.shape[0]\n",
+    "        self.validation = validation\n",
+    "        \n",
+    "    @staticmethod\n",
+    "    def _load_img_as_tensor(file_name):\n",
+    "        with Image.open(file_name) as img:\n",
+    "            return TestImagesDS.transform(img)\n",
+    "\n",
+    "    def _get_img_path(self, index, site=1):\n",
+    "        experiment, well, plate = self.records[index].experiment, self.records[index].well, self.records[index].plate\n",
+    "        return f'{self.img_dir}/{self.mode}/{experiment}_{plate}_{well}_s{site}.jpeg'\n",
+    "        \n",
+    "    def get_image_pair(self, index):\n",
+    "        return [self._load_img_as_tensor(self._get_img_path(index, site)) for site in [1,2]]\n",
+    "    \n",
+    "    def __getitem__(self, index):\n",
+    "        image_pairs = [self.get_image_pair(index) for _ in range(10)]\n",
+    "        \n",
+    "        return image_pairs, self.records[index].id_code\n",
+    "\n",
+    "    def __len__(self):\n",
+    "        return self.len"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# dataframes for training, cross-validation, and testing\n",
+    "df_test = pd.read_csv(path_data+'/test.csv')\n",
+    "\n",
+    "# pytorch test dataset & loader\n",
+    "ds_test = TestImagesDS(df_test, mode='test', validation=True)\n",
+    "tloader = D.DataLoader(ds_test, batch_size=1, shuffle=False, num_workers=4)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Loaded pretrained weights for efficientnet-b3\n"
+     ]
+    }
+   ],
+   "source": [
+    "class EfficientNetTwoInputs(nn.Module):\n",
+    "    def __init__(self):\n",
+    "        super(EfficientNetTwoInputs, self).__init__()\n",
+    "        self.classes = 1108\n",
+    "        \n",
+    "        model = model = EfficientNet.from_pretrained(model_name, num_classes=1108) \n",
+    "        num_ftrs = model._fc.in_features\n",
+    "        model._fc = nn.Identity()\n",
+    "        \n",
+    "        self.resnet = model\n",
+    "        self.fc = nn.Linear(num_ftrs * 2, self.classes)\n",
+    "\n",
+    "    def forward(self, x1, x2):\n",
+    "        x1_out = self.resnet(x1)\n",
+    "        x2_out = self.resnet(x2)\n",
+    "   \n",
+    "        N, _, _, _ = x1.size()\n",
+    "        x1_out = x1_out.view(N, -1)\n",
+    "        x2_out = x2_out.view(N, -1)\n",
+    "        \n",
+    "        out = torch.cat((x1_out, x2_out), 1)\n",
+    "        out = self.fc(out)\n",
+    "\n",
+    "        return out \n",
+    "    \n",
+    "model = EfficientNetTwoInputs()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Evaluate"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "68b16e5780184c4dbf02e5aae2777d81",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "HBox(children=(IntProgress(value=0, max=19897), HTML(value='')))"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "model.cuda()\n",
+    "eval_model_10(model, tloader, 'models/Model_efficientnet-b3_31.pth', path_data)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
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+   "file_extension": ".py",
+   "mimetype": "text/x-python",
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diff --git a/experiment6.ipynb b/experiment6.ipynb
index 4de870b..10f6fda 100644
--- a/experiment6.ipynb
+++ b/experiment6.ipynb
@@ -372,8 +372,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "pbar = ProgressBar(bar_format='')\n",
-    "pbar.attach(trainer, output_transform=lambda x: {'loss': x})"
+    "# pbar = ProgressBar(bar_format='')\n",
+    "# pbar.attach(trainer, output_transform=lambda x: {'loss': x})"
    ]
   },
   {
diff --git a/experiment6.py b/experiment6.py
index 1528992..cc8a056 100644
--- a/experiment6.py
+++ b/experiment6.py
@@ -270,8 +270,8 @@ def save_best_epoch_only(engine):
 # In[13]:
 
 
-pbar = ProgressBar(bar_format='')
-pbar.attach(trainer, output_transform=lambda x: {'loss': x})
+# pbar = ProgressBar(bar_format='')
+# pbar.attach(trainer, output_transform=lambda x: {'loss': x})
 
 
 # #### Train
diff --git a/scripts/evaluate.py b/scripts/evaluate.py
index b055d72..8ba2de1 100644
--- a/scripts/evaluate.py
+++ b/scripts/evaluate.py
@@ -4,6 +4,7 @@
 from scipy import misc
 import torch
 from tqdm import tqdm_notebook
+from collections import Counter
 
 from sklearn.model_selection import train_test_split
 
@@ -19,6 +20,27 @@ def eval_model(model, loader, file_path, path_data, device='cuda'):
             idx = output.max(dim=-1)[1].cpu().numpy()
             preds = np.append(preds, idx, axis=0)
 
+    submission = pd.read_csv(path_data + '/test.csv')
+    submission['sirna'] = preds.astype(int)
+    submission.to_csv(f'submission.csv', index=False, columns=['id_code','sirna'])
+    
+def eval_model_10(model, loader, file_path, path_data, device='cuda'):
+    model.load_state_dict(torch.load(file_path))
+    model.eval()
+    with torch.no_grad():
+        preds = np.empty(0)
+        for image_pairs, _ in tqdm_notebook(loader):
+            idx_counter = Counter()
+            for image_pair in image_pairs:
+                x1, x2 = image_pair
+                x1 = x1.to(device)
+                x2 = x2.to(device)
+                output = model(x1,x2)
+                idx = output.max(dim=-1)[1].cpu().numpy()
+                idx_counter.update(idx)
+            
+            preds = np.append(preds, [idx_counter.most_common(1)[0][0]], axis=0)
+
     submission = pd.read_csv(path_data + '/test.csv')
     submission['sirna'] = preds.astype(int)
     submission.to_csv(f'submission.csv', index=False, columns=['id_code','sirna'])
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