model.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import cv2\n",
    "import torch\n",
    "import torchvision\n",
    "import os, glob\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Prepare Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "image_paths = glob.glob(os.path.join('IMG','*.jpg'))\n",
    "driver_obs = pd.read_csv(os.path.join('driving_log.csv'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Size of images: 24108\n",
      "Size of Car Obs: 8036\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>center</th>\n",
       "      <th>left</th>\n",
       "      <th>right</th>\n",
       "      <th>steering</th>\n",
       "      <th>throttle</th>\n",
       "      <th>brake</th>\n",
       "      <th>speed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>IMG/center_2016_12_01_13_30_48_287.jpg</td>\n",
       "      <td>IMG/left_2016_12_01_13_30_48_287.jpg</td>\n",
       "      <td>IMG/right_2016_12_01_13_30_48_287.jpg</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>22.148290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>IMG/center_2016_12_01_13_30_48_404.jpg</td>\n",
       "      <td>IMG/left_2016_12_01_13_30_48_404.jpg</td>\n",
       "      <td>IMG/right_2016_12_01_13_30_48_404.jpg</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>21.879630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>IMG/center_2016_12_01_13_31_12_937.jpg</td>\n",
       "      <td>IMG/left_2016_12_01_13_31_12_937.jpg</td>\n",
       "      <td>IMG/right_2016_12_01_13_31_12_937.jpg</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.453011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>IMG/center_2016_12_01_13_31_13_037.jpg</td>\n",
       "      <td>IMG/left_2016_12_01_13_31_13_037.jpg</td>\n",
       "      <td>IMG/right_2016_12_01_13_31_13_037.jpg</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.438419</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>IMG/center_2016_12_01_13_31_13_177.jpg</td>\n",
       "      <td>IMG/left_2016_12_01_13_31_13_177.jpg</td>\n",
       "      <td>IMG/right_2016_12_01_13_31_13_177.jpg</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.418236</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                   center  \\\n",
       "0  IMG/center_2016_12_01_13_30_48_287.jpg   \n",
       "1  IMG/center_2016_12_01_13_30_48_404.jpg   \n",
       "2  IMG/center_2016_12_01_13_31_12_937.jpg   \n",
       "3  IMG/center_2016_12_01_13_31_13_037.jpg   \n",
       "4  IMG/center_2016_12_01_13_31_13_177.jpg   \n",
       "\n",
       "                                    left  \\\n",
       "0   IMG/left_2016_12_01_13_30_48_287.jpg   \n",
       "1   IMG/left_2016_12_01_13_30_48_404.jpg   \n",
       "2   IMG/left_2016_12_01_13_31_12_937.jpg   \n",
       "3   IMG/left_2016_12_01_13_31_13_037.jpg   \n",
       "4   IMG/left_2016_12_01_13_31_13_177.jpg   \n",
       "\n",
       "                                    right  steering  throttle  brake  \\\n",
       "0   IMG/right_2016_12_01_13_30_48_287.jpg       0.0       0.0    0.0   \n",
       "1   IMG/right_2016_12_01_13_30_48_404.jpg       0.0       0.0    0.0   \n",
       "2   IMG/right_2016_12_01_13_31_12_937.jpg       0.0       0.0    0.0   \n",
       "3   IMG/right_2016_12_01_13_31_13_037.jpg       0.0       0.0    0.0   \n",
       "4   IMG/right_2016_12_01_13_31_13_177.jpg       0.0       0.0    0.0   \n",
       "\n",
       "       speed  \n",
       "0  22.148290  \n",
       "1  21.879630  \n",
       "2   1.453011  \n",
       "3   1.438419  \n",
       "4   1.418236  "
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('Size of images:',len(image_paths))\n",
    "print('Size of Car Obs:',len(driver_obs))\n",
    "doc_size = len(driver_obs)\n",
    "driver_obs.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "IMG\\center_2016_12_01_13_30_48_287.jpg\n",
      "IMG\\center_2016_12_01_13_30_48_404.jpg\n",
      "IMG\\center_2016_12_01_13_31_12_937.jpg\n"
     ]
    }
   ],
   "source": [
    "print(image_paths[0])\n",
    "print(image_paths[1])\n",
    "print(image_paths[2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "driver_obs.iloc[2]['steering']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x26a8fee4f60>"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "img = plt.imread(driver_obs.iloc[3]['left'].strip(' '))\n",
    "plt.imshow(img)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [],
   "source": [
    "from torch.utils.data import Dataset, dataloader\n",
    "from torchvision import transforms\n",
    "from PIL import Image, ImageOps\n",
    "\n",
    "class CustomDataSet(Dataset):\n",
    "    def __init__(self, data_path, transform ):\n",
    "        self.data_path = data_path\n",
    "        self.transform = transform\n",
    "        \n",
    "    def __len__(self):\n",
    "        return len(self.data_path)\n",
    "        \n",
    "    def __getitem__(self, idx):\n",
    "        data = driver_obs.iloc[idx]\n",
    "        c_img = Image.open(data['center'].strip(' '))\n",
    "        l_img = Image.open(data['left'].strip(' '))\n",
    "        r_img = Image.open(data['right'].strip(' '))\n",
    "        c_img, l_img, r_img = ImageOps.grayscale(c_img), ImageOps.grayscale(l_img), ImageOps.grayscale(r_img)\n",
    "        c_img, l_img, r_img = self.transform(c_img), self.transform(l_img), self.transform(r_img)\n",
    "#         tensor_img = torch.stack((c_img, l_img, r_img)).squeeze(1)\n",
    "        return torch.cat((c_img, l_img, r_img)), torch.tensor(data['steering'])\n",
    "        \n",
    "def load_data(image_path, batch_size, train=True, shuffle=True ):\n",
    "    train_transform = transforms.Compose([\n",
    "                transforms.RandomHorizontalFlip(),\n",
    "                transforms.ToTensor(),\n",
    "                transforms.Normalize(mean=[0.485],\n",
    "                                  std=[0.229])])\n",
    "    \n",
    "    test_transform = transforms.Compose([transforms.ToTensor(),\n",
    "                                        transforms.Normalize(mean=[0.485],\n",
    "                                          std=[0.229])])\n",
    "    \n",
    "    trans = train_transform if train else test_transform\n",
    "    _data = CustomDataSet(driver_obs, trans)\n",
    "    data_loader = torch.utils.data.DataLoader(_data, batch_size=batch_size, shuffle=shuffle)\n",
    "    return data_loader\n",
    "\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torchvision\n",
    "import torch.utils.data as data_utils\n",
    "batch_size = 64\n",
    "device = 'cuda' if torch.cuda.is_available() else 'cpu'\n",
    "\n",
    "train_split = int(0.8*doc_size)\n",
    "\n",
    "train = driver_obs.iloc[:train_split]\n",
    "valid = driver_obs.iloc[train_split:]\n",
    "\n",
    "train_loader = load_data(train, batch_size, True )\n",
    "valid_loader = load_data(valid, batch_size, True )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [],
   "source": [
    "t, l = (iter(train_loader).next())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([64, 3, 160, 320])\n",
      "torch.Size([64])\n",
      "4\n",
      "3\n",
      "torch.Size([64, 3, 160, 320])\n"
     ]
    }
   ],
   "source": [
    "imshape = t.shape\n",
    "print(imshape)\n",
    "print(l.shape)\n",
    "print(label_sz)\n",
    "print(len(t[0]))\n",
    "t= t.squeeze(2)\n",
    "print(t.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Create Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Network(\n",
       "  (conv1): Conv2d(3, 24, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2))\n",
       "  (conv2): Conv2d(24, 36, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2))\n",
       "  (conv3): Conv2d(36, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "  (conv4): Conv2d(48, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "  (fc1): Linear(in_features=12800, out_features=2000, bias=True)\n",
       "  (fc2): Linear(in_features=2000, out_features=512, bias=True)\n",
       "  (fc3): Linear(in_features=512, out_features=1, bias=True)\n",
       "  (maxpool): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
       "  (dropout): Dropout(p=0.3, inplace=False)\n",
       ")"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "\n",
    "class Network(nn.Module):\n",
    "    def __init__(self):\n",
    "        super(Network, self).__init__()\n",
    "        \n",
    "        self.conv1 =  nn.Conv2d(3, 24, 5, 1, 2) # (160, 320)\n",
    "        self.conv2 =  nn.Conv2d(24, 36, 5, 1, 2) # (80, 160)\n",
    "        self.conv3 =  nn.Conv2d(36, 48, 3, 1, 1) # (40, 80)\n",
    "        self.conv4 =  nn.Conv2d(48, 64, 3, 1, 1) # (20, 40)\n",
    "        self.fc1 = nn.Linear(10*20*64, 2000) # (10, 20)\n",
    "        self.fc2 = nn.Linear(2000, 512)\n",
    "        self.fc3 = nn.Linear(512, 1)\n",
    "        self.maxpool= nn.MaxPool2d(2,2)\n",
    "        self.dropout = nn.Dropout(0.3)\n",
    "    \n",
    "    def forward(self, images):\n",
    "        batch_size = images.shape[0]\n",
    "        x = F.relu(self.maxpool(self.conv1(images)))\n",
    "        x = self.dropout(x)\n",
    "        x = F.relu(self.maxpool(self.conv2(x)))\n",
    "        x = self.dropout(x)\n",
    "        x = F.relu(self.maxpool(self.conv3(x)))\n",
    "        x = self.dropout(x)\n",
    "        x = F.relu(self.maxpool(self.conv4(x)))\n",
    "        x = self.dropout(x)\n",
    "        x = x.view(batch_size, -1) \n",
    "        x = F.relu(self.fc1(x))\n",
    "        x = F.relu(self.fc2(x))\n",
    "        x = self.fc3(x)\n",
    "        \n",
    "        return x.squeeze(1)\n",
    "    \n",
    "device = 'cuda' if torch.cuda.is_available() else 'cpu'\n",
    "model = Network()\n",
    "model.to(device)\n",
    "model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Test Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([64])"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t = t.to(device)\n",
    "y = model(t)\n",
    "y.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Train Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Score improved from inf to 0.014802060090005398 : saved model\n",
      "Epoch: 1/100..  Training Loss: 0.016..  Test Loss: 0.015.. \n",
      "Best Score improved from 0.014802060090005398 to 0.010702274739742279 : saved model\n",
      "Epoch: 2/100..  Training Loss: 0.012..  Test Loss: 0.011.. \n",
      "Best Score improved from 0.010702274739742279 to 0.009760057553648949 : saved model\n",
      "Epoch: 3/100..  Training Loss: 0.010..  Test Loss: 0.010.. \n",
      "Best Score improved from 0.009760057553648949 to 0.009325959719717503 : saved model\n",
      "Epoch: 4/100..  Training Loss: 0.010..  Test Loss: 0.009.. \n",
      "Best Score improved from 0.009325959719717503 to 0.00899423286318779 : saved model\n",
      "Epoch: 5/100..  Training Loss: 0.009..  Test Loss: 0.009.. \n",
      "Epoch: 6/100..  Training Loss: 0.009..  Test Loss: 0.010.. \n",
      "Best Score improved from 0.00899423286318779 to 0.008988512679934502 : saved model\n",
      "Epoch: 7/100..  Training Loss: 0.009..  Test Loss: 0.009.. \n",
      "Epoch: 8/100..  Training Loss: 0.009..  Test Loss: 0.009.. \n",
      "Best Score improved from 0.008988512679934502 to 0.008472846820950508 : saved model\n",
      "Epoch: 9/100..  Training Loss: 0.009..  Test Loss: 0.008.. \n",
      "Best Score improved from 0.008472846820950508 to 0.00802887137979269 : saved model\n",
      "Epoch: 10/100..  Training Loss: 0.009..  Test Loss: 0.008.. \n",
      "Epoch: 11/100..  Training Loss: 0.008..  Test Loss: 0.008.. \n",
      "Epoch: 12/100..  Training Loss: 0.008..  Test Loss: 0.008.. \n",
      "Epoch: 13/100..  Training Loss: 0.008..  Test Loss: 0.008.. \n",
      "Best Score improved from 0.00802887137979269 to 0.007650389336049557 : saved model\n",
      "Epoch: 14/100..  Training Loss: 0.008..  Test Loss: 0.008.. \n",
      "Epoch: 15/100..  Training Loss: 0.008..  Test Loss: 0.008.. \n",
      "Epoch: 16/100..  Training Loss: 0.008..  Test Loss: 0.008.. \n",
      "Best Score improved from 0.007650389336049557 to 0.007253348361700773 : saved model\n",
      "Epoch: 17/100..  Training Loss: 0.008..  Test Loss: 0.007.. \n",
      "Best Score improved from 0.007253348361700773 to 0.007062544580549002 : saved model\n",
      "Epoch: 18/100..  Training Loss: 0.008..  Test Loss: 0.007.. \n",
      "Epoch: 19/100..  Training Loss: 0.008..  Test Loss: 0.007.. \n",
      "Epoch: 20/100..  Training Loss: 0.008..  Test Loss: 0.008.. \n",
      "Epoch: 21/100..  Training Loss: 0.007..  Test Loss: 0.007.. \n",
      "Best Score improved from 0.007062544580549002 to 0.007011833600699902 : saved model\n",
      "Epoch: 22/100..  Training Loss: 0.007..  Test Loss: 0.007.. \n",
      "Best Score improved from 0.007011833600699902 to 0.0069178068079054356 : saved model\n",
      "Epoch: 23/100..  Training Loss: 0.007..  Test Loss: 0.007.. \n",
      "Best Score improved from 0.0069178068079054356 to 0.006609923671931028 : saved model\n",
      "Epoch: 24/100..  Training Loss: 0.007..  Test Loss: 0.007.. \n",
      "Best Score improved from 0.006609923671931028 to 0.006581461522728205 : saved model\n",
      "Epoch: 25/100..  Training Loss: 0.007..  Test Loss: 0.007.. \n",
      "Best Score improved from 0.006581461522728205 to 0.0063774604350328445 : saved model\n",
      "Epoch: 26/100..  Training Loss: 0.007..  Test Loss: 0.006.. \n",
      "Epoch: 27/100..  Training Loss: 0.007..  Test Loss: 0.007.. \n",
      "Best Score improved from 0.0063774604350328445 to 0.005924871191382408 : saved model\n",
      "Epoch: 28/100..  Training Loss: 0.007..  Test Loss: 0.006.. \n",
      "Epoch: 29/100..  Training Loss: 0.007..  Test Loss: 0.006.. \n",
      "Best Score improved from 0.005924871191382408 to 0.005641131196171045 : saved model\n",
      "Epoch: 30/100..  Training Loss: 0.006..  Test Loss: 0.006.. \n",
      "Best Score improved from 0.005641131196171045 to 0.005552380811423063 : saved model\n",
      "Epoch: 31/100..  Training Loss: 0.006..  Test Loss: 0.006.. \n",
      "Epoch: 32/100..  Training Loss: 0.006..  Test Loss: 0.006.. \n",
      "Epoch: 33/100..  Training Loss: 0.006..  Test Loss: 0.006.. \n",
      "Best Score improved from 0.005552380811423063 to 0.005438609980046749 : saved model\n",
      "Epoch: 34/100..  Training Loss: 0.006..  Test Loss: 0.005.. \n",
      "Best Score improved from 0.005438609980046749 to 0.005418014246970415 : saved model\n",
      "Epoch: 35/100..  Training Loss: 0.006..  Test Loss: 0.005.. \n",
      "Best Score improved from 0.005418014246970415 to 0.005399991758167744 : saved model\n",
      "Epoch: 36/100..  Training Loss: 0.006..  Test Loss: 0.005.. \n",
      "Best Score improved from 0.005399991758167744 to 0.004948005545884371 : saved model\n",
      "Epoch: 37/100..  Training Loss: 0.006..  Test Loss: 0.005.. \n",
      "Epoch: 38/100..  Training Loss: 0.006..  Test Loss: 0.005.. \n",
      "Best Score improved from 0.004948005545884371 to 0.004879713524132967 : saved model\n",
      "Epoch: 39/100..  Training Loss: 0.006..  Test Loss: 0.005.. \n",
      "Epoch: 40/100..  Training Loss: 0.006..  Test Loss: 0.005.. \n",
      "Epoch: 41/100..  Training Loss: 0.006..  Test Loss: 0.005.. \n",
      "Epoch: 42/100..  Training Loss: 0.006..  Test Loss: 0.005.. \n",
      "Best Score improved from 0.004879713524132967 to 0.004723665304481983 : saved model\n",
      "Epoch: 43/100..  Training Loss: 0.006..  Test Loss: 0.005.. \n",
      "Best Score improved from 0.004723665304481983 to 0.004645208828151226 : saved model\n",
      "Epoch: 44/100..  Training Loss: 0.005..  Test Loss: 0.005.. \n",
      "Epoch: 45/100..  Training Loss: 0.005..  Test Loss: 0.005.. \n",
      "Epoch: 46/100..  Training Loss: 0.006..  Test Loss: 0.005.. \n",
      "Epoch: 47/100..  Training Loss: 0.005..  Test Loss: 0.005.. \n",
      "Best Score improved from 0.004645208828151226 to 0.004406709223985672 : saved model\n",
      "Epoch: 48/100..  Training Loss: 0.005..  Test Loss: 0.004.. \n",
      "Best Score improved from 0.004406709223985672 to 0.004340056795626879 : saved model\n",
      "Epoch: 49/100..  Training Loss: 0.005..  Test Loss: 0.004.. \n",
      "Epoch: 50/100..  Training Loss: 0.005..  Test Loss: 0.005.. \n",
      "Best Score improved from 0.004340056795626879 to 0.004298505373299122 : saved model\n",
      "Epoch: 51/100..  Training Loss: 0.005..  Test Loss: 0.004.. \n",
      "Epoch: 52/100..  Training Loss: 0.005..  Test Loss: 0.005.. \n",
      "Epoch: 53/100..  Training Loss: 0.005..  Test Loss: 0.004.. \n",
      "Best Score improved from 0.004298505373299122 to 0.0035645668394863605 : saved model\n",
      "Epoch: 54/100..  Training Loss: 0.005..  Test Loss: 0.004.. \n",
      "Epoch: 55/100..  Training Loss: 0.005..  Test Loss: 0.004.. \n",
      "Epoch: 56/100..  Training Loss: 0.005..  Test Loss: 0.004.. \n",
      "Epoch: 57/100..  Training Loss: 0.005..  Test Loss: 0.004.. \n",
      "Epoch: 58/100..  Training Loss: 0.005..  Test Loss: 0.004.. \n",
      "Epoch: 59/100..  Training Loss: 0.004..  Test Loss: 0.005.. \n",
      "Epoch: 60/100..  Training Loss: 0.004..  Test Loss: 0.004.. \n",
      "Epoch: 61/100..  Training Loss: 0.004..  Test Loss: 0.004.. \n",
      "Epoch: 62/100..  Training Loss: 0.004..  Test Loss: 0.004.. \n",
      "Epoch: 63/100..  Training Loss: 0.004..  Test Loss: 0.004.. \n",
      "Best Score improved from 0.0035645668394863605 to 0.0034805587492883205 : saved model\n",
      "Epoch: 64/100..  Training Loss: 0.004..  Test Loss: 0.003.. \n",
      "Epoch: 65/100..  Training Loss: 0.004..  Test Loss: 0.004.. \n",
      "Best Score improved from 0.0034805587492883205 to 0.0032809164840728045 : saved model\n",
      "Epoch: 66/100..  Training Loss: 0.004..  Test Loss: 0.003.. \n",
      "Best Score improved from 0.0032809164840728045 to 0.0032656912226229906 : saved model\n",
      "Epoch: 67/100..  Training Loss: 0.004..  Test Loss: 0.003.. \n",
      "Best Score improved from 0.0032656912226229906 to 0.0032195369713008404 : saved model\n",
      "Epoch: 68/100..  Training Loss: 0.004..  Test Loss: 0.003.. \n",
      "Epoch: 69/100..  Training Loss: 0.004..  Test Loss: 0.003.. \n",
      "Epoch: 70/100..  Training Loss: 0.004..  Test Loss: 0.003.. \n",
      "Best Score improved from 0.0032195369713008404 to 0.003138576867058873 : saved model\n",
      "Epoch: 71/100..  Training Loss: 0.004..  Test Loss: 0.003.. \n",
      "Best Score improved from 0.003138576867058873 to 0.002997261704877019 : saved model\n",
      "Epoch: 72/100..  Training Loss: 0.004..  Test Loss: 0.003.. \n",
      "Epoch: 73/100..  Training Loss: 0.004..  Test Loss: 0.004.. \n",
      "Epoch: 74/100..  Training Loss: 0.004..  Test Loss: 0.003.. \n",
      "Epoch: 75/100..  Training Loss: 0.004..  Test Loss: 0.004.. \n",
      "Epoch: 76/100..  Training Loss: 0.004..  Test Loss: 0.003.. \n",
      "Best Score improved from 0.002997261704877019 to 0.0028903589118272066 : saved model\n",
      "Epoch: 77/100..  Training Loss: 0.004..  Test Loss: 0.003.. \n",
      "Epoch: 78/100..  Training Loss: 0.004..  Test Loss: 0.003.. \n",
      "Best Score improved from 0.0028903589118272066 to 0.002644506050273776 : saved model\n",
      "Epoch: 79/100..  Training Loss: 0.004..  Test Loss: 0.003.. \n",
      "Epoch: 80/100..  Training Loss: 0.004..  Test Loss: 0.003.. \n",
      "Epoch: 81/100..  Training Loss: 0.003..  Test Loss: 0.003.. \n",
      "Epoch: 82/100..  Training Loss: 0.003..  Test Loss: 0.003.. \n",
      "Epoch: 83/100..  Training Loss: 0.003..  Test Loss: 0.003.. \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 84/100..  Training Loss: 0.003..  Test Loss: 0.003.. \n",
      "Epoch: 85/100..  Training Loss: 0.003..  Test Loss: 0.003.. \n",
      "Best Score improved from 0.002644506050273776 to 0.002481092931702733 : saved model\n",
      "Epoch: 86/100..  Training Loss: 0.003..  Test Loss: 0.002.. \n",
      "Epoch: 87/100..  Training Loss: 0.003..  Test Loss: 0.003.. \n",
      "Best Score improved from 0.002481092931702733 to 0.002347343135625124 : saved model\n",
      "Epoch: 88/100..  Training Loss: 0.003..  Test Loss: 0.002.. \n",
      "Epoch: 89/100..  Training Loss: 0.003..  Test Loss: 0.003.. \n",
      "Epoch: 90/100..  Training Loss: 0.003..  Test Loss: 0.002.. \n",
      "Epoch: 91/100..  Training Loss: 0.003..  Test Loss: 0.003.. \n",
      "Epoch: 92/100..  Training Loss: 0.003..  Test Loss: 0.003.. \n",
      "Epoch: 93/100..  Training Loss: 0.003..  Test Loss: 0.003.. \n",
      "Epoch: 94/100..  Training Loss: 0.003..  Test Loss: 0.003.. \n",
      "Epoch: 95/100..  Training Loss: 0.003..  Test Loss: 0.003.. \n",
      "Best Score improved from 0.002347343135625124 to 0.002159740310162306 : saved model\n",
      "Epoch: 96/100..  Training Loss: 0.003..  Test Loss: 0.002.. \n",
      "Epoch: 97/100..  Training Loss: 0.003..  Test Loss: 0.002.. \n",
      "Epoch: 98/100..  Training Loss: 0.003..  Test Loss: 0.002.. \n",
      "Epoch: 99/100..  Training Loss: 0.003..  Test Loss: 0.003.. \n",
      "Epoch: 100/100..  Training Loss: 0.003..  Test Loss: 0.002.. \n"
     ]
    }
   ],
   "source": [
    "from torch import optim\n",
    "import numpy as np\n",
    "criterion = nn.MSELoss()\n",
    "optimizer = optim.Adam(model.parameters())\n",
    "\n",
    "epochs = 100\n",
    "steps = 0\n",
    "best_score = np.inf\n",
    "improv_stop_gap = 0\n",
    "train_losses, test_losses = [], []\n",
    "for e in range(epochs):\n",
    "    running_loss = 0\n",
    "    for x, y in train_loader:\n",
    "        images = x.to(device)\n",
    "        labels = y.to(device)\n",
    "        optimizer.zero_grad()\n",
    "        y_pred = model(images)\n",
    "        loss = criterion(y_pred, labels.float())\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "        \n",
    "        running_loss += loss.item()\n",
    "        \n",
    "    else:\n",
    "        test_loss = 0\n",
    "        \n",
    "        # Turn off gradients for validation, saves memory and computations\n",
    "        with torch.no_grad():\n",
    "            model.eval()\n",
    "            for x, y in valid_loader:\n",
    "                images = x.to(device)\n",
    "                labels = y.to(device)\n",
    "                logits = model(images)\n",
    "                test_loss += criterion(logits.squeeze(0), labels.float())\n",
    "        \n",
    "        model.train()\n",
    "        \n",
    "        train_losses.append(running_loss/len(train_loader))\n",
    "        test_losses.append(test_loss/len(valid_loader))\n",
    "        \n",
    "        if test_losses[-1] < best_score:\n",
    "            print(\"Best Score improved from {} to {} : saved model\".format(best_score, test_losses[-1]) )\n",
    "            best_score = test_losses[-1]\n",
    "            torch.save(model.state_dict(), 'checkpoint.pth')\n",
    "            improv_stop_gap = 0\n",
    "            \n",
    "        if improv_stop_gap > 10:\n",
    "            break\n",
    "            \n",
    "        improv_stop_gap += 1\n",
    "            \n",
    "        print(\"Epoch: {}/{}.. \".format(e+1, epochs),\n",
    "              \"Training Loss: {:.3f}.. \".format(train_losses[-1]),\n",
    "              \"Test Loss: {:.3f}.. \".format(test_losses[-1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [],
   "source": [
    "def predict(images, device):\n",
    "    print(images[0].shape)\n",
    "    c_img = Image.fromarray(images[0])\n",
    "    l_img = Image.fromarray(images[1])\n",
    "    r_img = Image.fromarray(images[2])\n",
    "    c_img, l_img, r_img = ImageOps.grayscale(c_img), ImageOps.grayscale(l_img), ImageOps.grayscale(r_img)\n",
    "    c_img, l_img, r_img = self.transform(c_img), self.transform(l_img), self.transform(r_img)\n",
    "    tensor_image = torch.cat((c_img, l_img, r_img)).to(device)\n",
    "    steering_angle = model(tensor_image)\n",
    "    return steering_angle.item()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([160, 320])\n"
     ]
    },
    {
     "ename": "AttributeError",
     "evalue": "'Tensor' object has no attribute '__array_interface__'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-134-93bd23bfba83>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mpredict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mt\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdevice\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m<ipython-input-133-6d277dc123c7>\u001b[0m in \u001b[0;36mpredict\u001b[1;34m(images, device)\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mpredict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mimages\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdevice\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      2\u001b[0m     \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mimages\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m     \u001b[0mc_img\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mImage\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfromarray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mimages\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      4\u001b[0m     \u001b[0ml_img\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mImage\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfromarray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mimages\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m     \u001b[0mr_img\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mImage\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfromarray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mimages\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\envs\\drlnd\\lib\\site-packages\\PIL\\Image.py\u001b[0m in \u001b[0;36mfromarray\u001b[1;34m(obj, mode)\u001b[0m\n\u001b[0;32m   2702\u001b[0m     \u001b[1;33m.\u001b[0m\u001b[1;33m.\u001b[0m \u001b[0mversionadded\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m:\u001b[0m \u001b[1;36m1.1\u001b[0m\u001b[1;36m.6\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2703\u001b[0m     \"\"\"\n\u001b[1;32m-> 2704\u001b[1;33m     \u001b[0marr\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mobj\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__array_interface__\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2705\u001b[0m     \u001b[0mshape\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0marr\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"shape\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2706\u001b[0m     \u001b[0mndim\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mAttributeError\u001b[0m: 'Tensor' object has no attribute '__array_interface__'"
     ]
    }
   ],
   "source": [
    "predict(t[0], device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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  "kernelspec": {
   "display_name": "drlnd",
   "language": "python",
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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