train_teacher.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Imports\n",
    "import torch\n",
    "import torch.nn as nn  # All neural network modules, nn.Linear, nn.Conv2d, BatchNorm, Loss functions\n",
    "import torch.nn.functional as F\n",
    "import torch.optim as optim  # For all Optimization algorithms, SGD, Adam, etc.\n",
    "import torchvision.transforms as transforms  # Transformations we can perform on our dataset\n",
    "import os\n",
    "import cv2\n",
    "import pandas as pd\n",
    "from skimage import io\n",
    "from torch.optim.lr_scheduler import StepLR\n",
    "\n",
    "# import scripts\n",
    "from scripts.saveResults import  *\n",
    "from torch.utils.data import (\n",
    "    Dataset,\n",
    "    DataLoader,\n",
    ")  # Gives easier dataset managment and creates mini batches"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dataset "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "class SolarRadiance(Dataset):\n",
    "    def __init__(self, root_dir, labels, transform):\n",
    "        self.root_dir = root_dir\n",
    "        self.labels = labels\n",
    "        self.transform = transform\n",
    "        # self.data = self.load_dataset()\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.labels)\n",
    "\n",
    "    def __getitem__(self, index):\n",
    "        img_path = self.labels.iloc[index,0]\n",
    "        target = self.labels.iloc[index,1]\n",
    "\n",
    "        image = io.imread(img_path)\n",
    "        image = cv2.imread(img_path)#, cv2.IMREAD_COLOR)\n",
    "        image = cv2.resize(image, (60, 80))\n",
    "\n",
    "        if self.transform:\n",
    "             image = self.transform(image)\n",
    "\n",
    "        y_label = torch.tensor(target)\n",
    "\n",
    "        return image, y_label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_dataset(root_dir):\n",
    "        ds = pd.DataFrame()\n",
    "        dates = os.listdir(root_dir)\n",
    "\n",
    "        try:\n",
    "            for date in dates:\n",
    "                infrared_folder = os.path.join(root_dir, date, \"infrared\")\n",
    "                pyranometer_folder = os.path.join(root_dir, date, \"pyranometer\")\n",
    "                csv_path = os.path.join(pyranometer_folder, \"{date}.csv\".format(date=date))\n",
    "                if not os.path.exists(csv_path):\n",
    "                    print(\"Skipping date {date} because it does not have both infrared and pyranometer folders\".format(date=date))\n",
    "                    continue\n",
    "                ds_temp = getDs(infrared_folder, csv_path)\n",
    "                \n",
    "                # append the dataframe in the final dataframe\n",
    "                # ds = ds.append(ds_temp)\n",
    "                ds = pd.concat([ds, ds_temp], ignore_index=True)\n",
    "\n",
    "                ds['name'] = ds['name'].apply(lambda img: os.path.join(root_dir, date, 'infrared', img))\n",
    "        except Exception as e:\n",
    "            print(e)\n",
    "        return ds\n",
    "\n",
    "def getDs(path, labels):\n",
    "    pyranometer = pd.read_csv(labels)\n",
    "    images = os.listdir(path)\n",
    "    \n",
    "   \n",
    "    #convert column 1 to int\n",
    "    X = pyranometer.iloc[:,0].astype(int)\n",
    "\n",
    "    #convert to image names\n",
    "    pyranometer.iloc[:,0] = X.apply(lambda x: str(x) + 'IR.png')\n",
    "    \n",
    "    # Filter pyranometer DataFrame based on the 'x' column\n",
    "\n",
    "    filtered_pyranometer = pyranometer[pyranometer.iloc[:,0].isin(images)]\n",
    "    # Display the result\n",
    "    filtered_pyranometer.columns = ['name', 'value']\n",
    "\n",
    "    filtered_pyranometer = filtered_pyranometer.drop_duplicates(subset='name')\n",
    "    return filtered_pyranometer\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set device\n",
    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# Hyperparameters\n",
    "in_channel = 1\n",
    "batch_size = 50\n",
    "num_epochs = 50\n",
    "loss = 1 # if loss = 0 the model will be trained with RMSE loss and vice versa\n",
    "lr=0.01 # learning rate\n",
    "\n",
    "# create result directory\n",
    "result_dir = 'result/teacher'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'result/teacher'"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result_dir "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "random_seed = 42"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_dir = 'F:/Girasol/extracted/train/'\n",
    "train_data  = load_dataset(train_dir)\n",
    "torch.manual_seed(random_seed)\n",
    "train_set = SolarRadiance(root_dir= train_dir, labels=train_data, transform = transforms.Compose([transforms.ToTensor()]))\n",
    "train_loader = DataLoader(dataset=train_set, batch_size=batch_size, shuffle=True)\n",
    "\n",
    "val_dir = 'F:/Girasol/extracted/val/'\n",
    "val_data  = load_dataset(val_dir)\n",
    "torch.manual_seed(random_seed)\n",
    "random_seed = 42\n",
    "val_set = SolarRadiance(root_dir= val_dir, labels=val_data, transform = transforms.Compose([transforms.ToTensor()]))\n",
    "val_loader = DataLoader(dataset=val_set, batch_size=batch_size, shuffle=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\nijhu\\anaconda3\\envs\\pytorch\\Lib\\site-packages\\torchvision\\models\\_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.\n",
      "  warnings.warn(\n",
      "c:\\Users\\nijhu\\anaconda3\\envs\\pytorch\\Lib\\site-packages\\torchvision\\models\\_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=MobileNet_V2_Weights.IMAGENET1K_V1`. You can also use `weights=MobileNet_V2_Weights.DEFAULT` to get the most up-to-date weights.\n",
      "  warnings.warn(msg)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Sequential(\n",
       "  (0): Sequential(\n",
       "    (0): Sequential(\n",
       "      (0): Conv2dNormActivation(\n",
       "        (0): Conv2d(3, 32, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
       "        (1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (2): ReLU6(inplace=True)\n",
       "      )\n",
       "      (1): InvertedResidual(\n",
       "        (conv): Sequential(\n",
       "          (0): Conv2dNormActivation(\n",
       "            (0): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False)\n",
       "            (1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "            (2): ReLU6(inplace=True)\n",
       "          )\n",
       "          (1): Conv2d(32, 16, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (2): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "      )\n",
       "      (2): InvertedResidual(\n",
       "        (conv): Sequential(\n",
       "          (0): Conv2dNormActivation(\n",
       "            (0): Conv2d(16, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (1): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "            (2): ReLU6(inplace=True)\n",
       "          )\n",
       "          (1): Conv2dNormActivation(\n",
       "            (0): Conv2d(96, 96, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=96, bias=False)\n",
       "            (1): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "            (2): ReLU6(inplace=True)\n",
       "          )\n",
       "          (2): Conv2d(96, 24, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (3): BatchNorm2d(24, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "      )\n",
       "      (3): InvertedResidual(\n",
       "        (conv): Sequential(\n",
       "          (0): Conv2dNormActivation(\n",
       "            (0): Conv2d(24, 144, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (1): BatchNorm2d(144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "            (2): ReLU6(inplace=True)\n",
       "          )\n",
       "          (1): Conv2dNormActivation(\n",
       "            (0): Conv2d(144, 144, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=144, bias=False)\n",
       "            (1): BatchNorm2d(144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "            (2): ReLU6(inplace=True)\n",
       "          )\n",
       "          (2): Conv2d(144, 24, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (3): BatchNorm2d(24, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "      )\n",
       "      (4): InvertedResidual(\n",
       "        (conv): Sequential(\n",
       "          (0): Conv2dNormActivation(\n",
       "            (0): Conv2d(24, 144, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (1): BatchNorm2d(144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "            (2): ReLU6(inplace=True)\n",
       "          )\n",
       "          (1): Conv2dNormActivation(\n",
       "            (0): Conv2d(144, 144, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=144, bias=False)\n",
       "            (1): BatchNorm2d(144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "            (2): ReLU6(inplace=True)\n",
       "          )\n",
       "          (2): Conv2d(144, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (3): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "      )\n",
       "      (5): InvertedResidual(\n",
       "        (conv): Sequential(\n",
       "          (0): Conv2dNormActivation(\n",
       "            (0): Conv2d(32, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "            (2): ReLU6(inplace=True)\n",
       "          )\n",
       "          (1): Conv2dNormActivation(\n",
       "            (0): Conv2d(192, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=192, bias=False)\n",
       "            (1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "            (2): ReLU6(inplace=True)\n",
       "          )\n",
       "          (2): Conv2d(192, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (3): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "      )\n",
       "      (6): InvertedResidual(\n",
       "        (conv): Sequential(\n",
       "          (0): Conv2dNormActivation(\n",
       "            (0): Conv2d(32, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "            (2): ReLU6(inplace=True)\n",
       "          )\n",
       "          (1): Conv2dNormActivation(\n",
       "            (0): Conv2d(192, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=192, bias=False)\n",
       "            (1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "            (2): ReLU6(inplace=True)\n",
       "          )\n",
       "          (2): Conv2d(192, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (3): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "      )\n",
       "      (7): InvertedResidual(\n",
       "        (conv): Sequential(\n",
       "          (0): Conv2dNormActivation(\n",
       "            (0): Conv2d(32, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "            (2): ReLU6(inplace=True)\n",
       "          )\n",
       "          (1): Conv2dNormActivation(\n",
       "            (0): Conv2d(192, 192, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=192, bias=False)\n",
       "            (1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "            (2): ReLU6(inplace=True)\n",
       "          )\n",
       "          (2): Conv2d(192, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (3): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "      )\n",
       "      (8): InvertedResidual(\n",
       "        (conv): Sequential(\n",
       "          (0): Conv2dNormActivation(\n",
       "            (0): Conv2d(64, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "            (2): ReLU6(inplace=True)\n",
       "          )\n",
       "          (1): Conv2dNormActivation(\n",
       "            (0): Conv2d(384, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=384, bias=False)\n",
       "            (1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "            (2): ReLU6(inplace=True)\n",
       "          )\n",
       "          (2): Conv2d(384, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (3): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "      )\n",
       "      (9): InvertedResidual(\n",
       "        (conv): Sequential(\n",
       "          (0): Conv2dNormActivation(\n",
       "            (0): Conv2d(64, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "            (2): ReLU6(inplace=True)\n",
       "          )\n",
       "          (1): Conv2dNormActivation(\n",
       "            (0): Conv2d(384, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=384, bias=False)\n",
       "            (1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "            (2): ReLU6(inplace=True)\n",
       "          )\n",
       "          (2): Conv2d(384, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (3): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "      )\n",
       "      (10): InvertedResidual(\n",
       "        (conv): Sequential(\n",
       "          (0): Conv2dNormActivation(\n",
       "            (0): Conv2d(64, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "            (2): ReLU6(inplace=True)\n",
       "          )\n",
       "          (1): Conv2dNormActivation(\n",
       "            (0): Conv2d(384, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=384, bias=False)\n",
       "            (1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "            (2): ReLU6(inplace=True)\n",
       "          )\n",
       "          (2): Conv2d(384, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (3): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "      )\n",
       "      (11): InvertedResidual(\n",
       "        (conv): Sequential(\n",
       "          (0): Conv2dNormActivation(\n",
       "            (0): Conv2d(64, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "            (2): ReLU6(inplace=True)\n",
       "          )\n",
       "          (1): Conv2dNormActivation(\n",
       "            (0): Conv2d(384, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=384, bias=False)\n",
       "            (1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "            (2): ReLU6(inplace=True)\n",
       "          )\n",
       "          (2): Conv2d(384, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (3): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "      )\n",
       "      (12): InvertedResidual(\n",
       "        (conv): Sequential(\n",
       "          (0): Conv2dNormActivation(\n",
       "            (0): Conv2d(96, 576, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (1): BatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "            (2): ReLU6(inplace=True)\n",
       "          )\n",
       "          (1): Conv2dNormActivation(\n",
       "            (0): Conv2d(576, 576, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=576, bias=False)\n",
       "            (1): BatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "            (2): ReLU6(inplace=True)\n",
       "          )\n",
       "          (2): Conv2d(576, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (3): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "      )\n",
       "      (13): InvertedResidual(\n",
       "        (conv): Sequential(\n",
       "          (0): Conv2dNormActivation(\n",
       "            (0): Conv2d(96, 576, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (1): BatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "            (2): ReLU6(inplace=True)\n",
       "          )\n",
       "          (1): Conv2dNormActivation(\n",
       "            (0): Conv2d(576, 576, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=576, bias=False)\n",
       "            (1): BatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "            (2): ReLU6(inplace=True)\n",
       "          )\n",
       "          (2): Conv2d(576, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (3): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "      )\n",
       "      (14): InvertedResidual(\n",
       "        (conv): Sequential(\n",
       "          (0): Conv2dNormActivation(\n",
       "            (0): Conv2d(96, 576, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (1): BatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "            (2): ReLU6(inplace=True)\n",
       "          )\n",
       "          (1): Conv2dNormActivation(\n",
       "            (0): Conv2d(576, 576, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=576, bias=False)\n",
       "            (1): BatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "            (2): ReLU6(inplace=True)\n",
       "          )\n",
       "          (2): Conv2d(576, 160, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (3): BatchNorm2d(160, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "      )\n",
       "      (15): InvertedResidual(\n",
       "        (conv): Sequential(\n",
       "          (0): Conv2dNormActivation(\n",
       "            (0): Conv2d(160, 960, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (1): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "            (2): ReLU6(inplace=True)\n",
       "          )\n",
       "          (1): Conv2dNormActivation(\n",
       "            (0): Conv2d(960, 960, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=960, bias=False)\n",
       "            (1): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "            (2): ReLU6(inplace=True)\n",
       "          )\n",
       "          (2): Conv2d(960, 160, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (3): BatchNorm2d(160, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "      )\n",
       "      (16): InvertedResidual(\n",
       "        (conv): Sequential(\n",
       "          (0): Conv2dNormActivation(\n",
       "            (0): Conv2d(160, 960, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (1): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "            (2): ReLU6(inplace=True)\n",
       "          )\n",
       "          (1): Conv2dNormActivation(\n",
       "            (0): Conv2d(960, 960, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=960, bias=False)\n",
       "            (1): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "            (2): ReLU6(inplace=True)\n",
       "          )\n",
       "          (2): Conv2d(960, 160, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (3): BatchNorm2d(160, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "      )\n",
       "      (17): InvertedResidual(\n",
       "        (conv): Sequential(\n",
       "          (0): Conv2dNormActivation(\n",
       "            (0): Conv2d(160, 960, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (1): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "            (2): ReLU6(inplace=True)\n",
       "          )\n",
       "          (1): Conv2dNormActivation(\n",
       "            (0): Conv2d(960, 960, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=960, bias=False)\n",
       "            (1): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "            (2): ReLU6(inplace=True)\n",
       "          )\n",
       "          (2): Conv2d(960, 320, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (3): BatchNorm2d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "      )\n",
       "      (18): Conv2dNormActivation(\n",
       "        (0): Conv2d(320, 1280, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (1): BatchNorm2d(1280, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (2): ReLU6(inplace=True)\n",
       "      )\n",
       "    )\n",
       "  )\n",
       "  (1): AdaptiveAvgPool2d(output_size=1)\n",
       "  (2): Flatten(start_dim=1, end_dim=-1)\n",
       "  (3): LinearRegressionHead(\n",
       "    (fc): Linear(in_features=1280, out_features=1, bias=True)\n",
       "  )\n",
       ")"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import torchvision.models as models\n",
    "\n",
    "# Load the pre-trained MobileNet model without its final classification layer\n",
    "mobilenet = models.mobilenet_v2(pretrained=True)\n",
    "# Remove the last layer (classification layer)\n",
    "mobilenet = nn.Sequential(*list(mobilenet.children())[:-1])\n",
    "\n",
    "# Add custom layers for regression\n",
    "class LinearRegressionHead(nn.Module):\n",
    "    def __init__(self, in_features):\n",
    "        super(LinearRegressionHead, self).__init__()\n",
    "        self.fc = nn.Linear(in_features, 1)  # Output 1 value for linear regression\n",
    "    \n",
    "    def forward(self, x):\n",
    "        x = self.fc(x)\n",
    "        return x\n",
    "\n",
    "# Append the custom regression head to the pre-trained model\n",
    "in_features = mobilenet[-1][-1].out_channels  # Get the number of output channels from the last layer\n",
    "model = nn.Sequential(\n",
    "    mobilenet,\n",
    "    nn.AdaptiveAvgPool2d(1),  \n",
    "    nn.Flatten(),\n",
    "    LinearRegressionHead(in_features)\n",
    ")\n",
    "model.to(device)\n",
    "# Example usage:\n",
    "# input_data = torch.randn(1, 3, 224, 224)  # Example input data with 3 channels (RGB) and 224x224 size\n",
    "# output = model(input_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Loss and Optimizers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "if loss == 0:\n",
    "    # create a function (this my favorite choice)\n",
    "    def RMSELoss(yhat,y):\n",
    "        return torch.sqrt(torch.mean((yhat-y)**2))\n",
    "    criterion = RMSELoss\n",
    "else:\n",
    "    # Define the model, loss function, and optimizer\n",
    "    criterion = nn.MSELoss()\n",
    "    \n",
    "\n",
    "optimizer = torch.optim.Adam(model.parameters(), lr=lr)\n",
    "# Create a StepLR scheduler\n",
    "scheduler = StepLR(optimizer, step_size=1, gamma=0.5)\n",
    "\n",
    "# set initial loswest_loss to an infinite number\n",
    "lowest_loss = 10000000\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\nijhu\\anaconda3\\envs\\pytorch\\Lib\\site-packages\\torch\\optim\\lr_scheduler.py:136: UserWarning: Detected call of `lr_scheduler.step()` before `optimizer.step()`. In PyTorch 1.1.0 and later, you should call them in the opposite order: `optimizer.step()` before `lr_scheduler.step()`.  Failure to do this will result in PyTorch skipping the first value of the learning rate schedule. See more details at https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate\n",
      "  warnings.warn(\"Detected call of `lr_scheduler.step()` before `optimizer.step()`. \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch  0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 931/931 [05:56<00:00,  2.61it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "val Loss: 2460.4130\n",
      "mean_loss 2460.4130196406923lowest_loss10000000\n",
      "Epoch  1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 931/931 [02:13<00:00,  6.98it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "val Loss: 2397.0911\n",
      "mean_loss 2397.091097075364lowest_loss2460.4130196406923\n",
      "Epoch  2\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 931/931 [02:16<00:00,  6.82it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "val Loss: 2193.7916\n",
      "mean_loss 2193.791599931388lowest_loss2397.091097075364\n",
      "Epoch  3\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 931/931 [02:18<00:00,  6.71it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "val Loss: 2255.2980\n",
      "Epoch  4\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 931/931 [02:22<00:00,  6.52it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "val Loss: 2110.9859\n",
      "mean_loss 2110.985855102539lowest_loss2193.791599931388\n",
      "Epoch  5\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 931/931 [02:21<00:00,  6.59it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "val Loss: 2088.0493\n",
      "mean_loss 2088.049296148892lowest_loss2110.985855102539\n",
      "Epoch  6\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 931/931 [02:22<00:00,  6.54it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "val Loss: 1970.7537\n",
      "mean_loss 1970.7536931531183lowest_loss2088.049296148892\n",
      "Epoch  7\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 931/931 [02:22<00:00,  6.52it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "val Loss: 2020.3211\n",
      "Epoch  8\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 931/931 [02:26<00:00,  6.37it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "val Loss: 1991.7829\n",
      "Epoch  9\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 931/931 [02:25<00:00,  6.38it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "val Loss: 2003.1829\n",
      "Epoch  10\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 931/931 [02:27<00:00,  6.32it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "val Loss: 1972.3091\n",
      "Epoch  11\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 931/931 [02:24<00:00,  6.44it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "val Loss: 1992.5373\n",
      "Epoch  12\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 931/931 [02:23<00:00,  6.50it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "val Loss: 2003.4105\n",
      "Epoch  13\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 931/931 [02:24<00:00,  6.45it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "val Loss: 1997.6877\n",
      "Epoch  14\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 931/931 [02:25<00:00,  6.41it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "val Loss: 2000.3624\n",
      "Epoch  15\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 931/931 [02:25<00:00,  6.41it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "val Loss: 1995.1401\n",
      "Epoch  16\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 931/931 [02:28<00:00,  6.28it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "val Loss: 2004.8254\n",
      "Epoch  17\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 931/931 [02:27<00:00,  6.33it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "val Loss: 1988.2025\n",
      "Epoch  18\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 931/931 [02:28<00:00,  6.26it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "val Loss: 1994.0474\n",
      "Epoch  19\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 931/931 [02:24<00:00,  6.43it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "val Loss: 2007.2000\n",
      "Epoch  20\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 931/931 [02:27<00:00,  6.31it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "val Loss: 1982.1879\n",
      "Epoch  21\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 931/931 [02:26<00:00,  6.34it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "val Loss: 1998.6458\n",
      "Epoch  22\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 931/931 [02:27<00:00,  6.31it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "val Loss: 1985.3537\n",
      "Epoch  23\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 931/931 [02:27<00:00,  6.30it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "val Loss: 1984.9317\n",
      "Epoch  24\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 931/931 [02:27<00:00,  6.31it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "val Loss: 1999.3867\n",
      "Epoch  25\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 931/931 [02:27<00:00,  6.32it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "val Loss: 1988.5682\n",
      "Epoch  26\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 931/931 [02:24<00:00,  6.45it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "val Loss: 1984.0484\n",
      "Epoch  27\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 931/931 [02:25<00:00,  6.39it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "val Loss: 1988.7952\n",
      "Epoch  28\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 931/931 [02:23<00:00,  6.49it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "val Loss: 1989.0843\n",
      "Epoch  29\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 931/931 [03:08<00:00,  4.94it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "val Loss: 1998.1693\n",
      "Epoch  30\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 931/931 [03:13<00:00,  4.82it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "val Loss: 1988.6336\n",
      "Epoch  31\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 931/931 [03:05<00:00,  5.01it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "val Loss: 1976.9391\n",
      "Epoch  32\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 931/931 [03:26<00:00,  4.50it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "val Loss: 1999.5554\n",
      "Epoch  33\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 931/931 [03:57<00:00,  3.92it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "val Loss: 1995.5444\n",
      "Epoch  34\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 931/931 [03:26<00:00,  4.52it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "val Loss: 1992.5218\n",
      "Epoch  35\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 931/931 [02:58<00:00,  5.22it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "val Loss: 2000.3170\n",
      "Epoch  36\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 931/931 [02:54<00:00,  5.32it/s]\n"
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "val Loss: 2005.8640\n",
      "Epoch  37\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 931/931 [03:23<00:00,  4.57it/s]\n"
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "val Loss: 1981.7991\n",
      "Epoch  38\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 931/931 [03:05<00:00,  5.01it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "val Loss: 1986.2976\n",
      "Epoch  39\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 931/931 [02:59<00:00,  5.18it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "val Loss: 1971.9513\n",
      "Epoch  40\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 931/931 [02:43<00:00,  5.68it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "val Loss: 1990.7742\n",
      "Epoch  41\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 931/931 [02:43<00:00,  5.71it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "val Loss: 2025.6206\n",
      "Epoch  42\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 931/931 [02:40<00:00,  5.78it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "val Loss: 1986.7532\n",
      "Epoch  43\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 931/931 [02:42<00:00,  5.73it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "val Loss: 1979.9230\n",
      "Epoch  44\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 931/931 [02:39<00:00,  5.82it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "val Loss: 1995.7264\n",
      "Epoch  45\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 931/931 [02:37<00:00,  5.92it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "val Loss: 1998.1357\n",
      "Epoch  46\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 931/931 [02:36<00:00,  5.95it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "val Loss: 2000.1554\n",
      "Epoch  47\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 931/931 [03:23<00:00,  4.57it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "val Loss: 1995.8798\n",
      "Epoch  48\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 931/931 [03:50<00:00,  4.04it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "val Loss: 1978.3641\n",
      "Epoch  49\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 931/931 [02:40<00:00,  5.81it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "val Loss: 1995.8167\n"
     ]
    }
   ],
   "source": [
    "losses = []\n",
    "\n",
    "from tqdm import tqdm\n",
    "# Train the model\n",
    "for epoch in range(num_epochs):\n",
    "    scheduler.step()\n",
    "    print('Epoch ',epoch)\n",
    "    for i, (inputs, targets) in enumerate(tqdm(train_loader)):\n",
    "        # Zero the gradients\n",
    "        optimizer.zero_grad()\n",
    "        inputs = inputs.to(device)\n",
    "        targets = targets.to(device)\n",
    "        # Forward pass\n",
    "        outputs = model(inputs)\n",
    "\n",
    "        loss = criterion(outputs[:,0], targets.float())\n",
    "\n",
    "        # Backward pass and optimization\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "\n",
    "    # Evaluate the model on the test data\n",
    "    with torch.no_grad():\n",
    "        total_loss = 0\n",
    "        for inputs, targets in val_loader:\n",
    "            inputs = inputs.to(device)\n",
    "            targets = targets.to(device)\n",
    "            outputs = model(inputs)\n",
    "            loss = criterion(outputs[:,0], targets.float())\n",
    "            total_loss += loss.item()\n",
    "        mean_loss = total_loss / len(val_loader)\n",
    "        print(f'val Loss: {mean_loss:.4f}')\n",
    "        losses.append(mean_loss)\n",
    "        if  mean_loss<lowest_loss:\n",
    "            print('mean_loss '+str(mean_loss)+'lowest_loss'+str(lowest_loss))\n",
    "            lowest_loss = mean_loss\n",
    "            torch.save(model.state_dict(), os.path.join(result_dir,'teacher.pth'))\n",
    "            "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# save the losses per epoch\n",
    "import pickle\n",
    "\n",
    "# Save the list to a file\n",
    "with open(result_dir+'/losses.pkl', 'wb') as file:\n",
    "    pickle.dump(losses, file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2460.4130196406923, 2397.091097075364, 2193.791599931388, 2255.298031510978, 2110.985855102539, 2088.049296148892, 1970.7536931531183, 2020.3211417362609, 1991.7828829535122, 2003.1829486715383, 1972.3091399094153, 1992.5373301012762, 2003.410506939066, 1997.6876788961476, 2000.3623991341426, 1995.1401430327317, 2004.8254499764278, 1988.202484920107, 1994.0473561780207, 2007.2000493016735, 1982.1878809435614, 1998.645808252795, 1985.3536521648539, 1984.9316511483028, 1999.386670342807, 1988.5681518028523, 1984.048385093952, 1988.7952423095703, 1989.08431296513, 1998.1692949492356, 1988.6335772810312, 1976.9390966481176, 1999.555404663086, 1995.5443617722083, 1992.5217987586711, 2000.317012129159, 2005.8639657908473, 1981.7990509559368, 1986.2976261007375, 1971.951253167514, 1990.774154268462, 2025.6206123088969, 1986.7531709342168, 1979.922994153253, 1995.7263699235587, 1998.1356777322703, 2000.1554431257578, 1995.879764162261, 1978.3641283758755, 1995.8167429956898]\n"
     ]
    }
   ],
   "source": [
    "# Load the list from the file\n",
    "losses_path = os.path.join(result_dir,'losses.pkl')\n",
    "with open(losses_path, 'rb') as file:\n",
    "    losses = pickle.load(file)\n",
    "print(losses)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "saveLossDiagram(num_epochs,losses,result_dir)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Batch Size 50 , Epochs 50 , Learning Rate 0.010000 , Loss 1970.753693\n",
      "MSE Loss: 43.7102\n",
      "result/teacher\\results.csv\n"
     ]
    }
   ],
   "source": [
    "# Specify the path to the saved model checkpoint\n",
    "model_checkpoint_path = os.path.join(result_dir,'teacher.pth')\n",
    "# Load the saved model state_dict\n",
    "model.load_state_dict(torch.load(model_checkpoint_path))\n",
    "\n",
    "outputss = []\n",
    "targetss = []\n",
    "\n",
    "# choose loss to evaluate with \n",
    "if loss == 0:\n",
    "    criterion = nn.MSELoss()\n",
    "else:\n",
    "    def RMSELoss(yhat,y):\n",
    "        return torch.sqrt(torch.mean((yhat-y)**2))\n",
    "    criterion = RMSELoss\n",
    "\n",
    "# Evaluate the model on the validation data\n",
    "with torch.no_grad():\n",
    "    total_loss = 0\n",
    "    for inputs, targets in val_loader:\n",
    "        inputs = inputs.to(device)\n",
    "        targets = targets.to(device)\n",
    "        outputs = model(inputs)\n",
    "        outputss.append(outputs[:,0].tolist())\n",
    "        targetss.append(targets.tolist())\n",
    "        loss = criterion(outputs[:,0], targets.float())\n",
    "        total_loss += loss.item()\n",
    "    mean_loss = total_loss / len(val_loader)\n",
    "    if loss == 0:\n",
    "        print(\"Batch Size %d , Epochs %d , Learning Rate %f , Loss %f\"%(batch_size, num_epochs, lr, lowest_loss))\n",
    "        print(f'MSE Loss: {mean_loss:.4f}')\n",
    "        SaveResults(result_dir, batch_size, num_epochs, lr, mean_loss, lowest_loss)\n",
    "    else:\n",
    "        print(\"Batch Size %d , Epochs %d , Learning Rate %f , Loss %f\"%(batch_size, num_epochs, lr, lowest_loss))\n",
    "        print(f'MSE Loss: {mean_loss:.4f}')\n",
    "        SaveResults(result_dir, batch_size, num_epochs, lr,lowest_loss, mean_loss)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "outputs = []\n",
    "for i in outputss:\n",
    "    for j in i:\n",
    "        outputs.append(j)\n",
    "\n",
    "targets = []\n",
    "for i in targetss:\n",
    "    for j in i:\n",
    "        targets.append(j)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 4000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "observedvsPredicted(result_dir, targets, outputs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Train and Shut Down"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import os\n",
    "# os.system(\"shutdown /s /t 1\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "pytorch",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.5"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}