05-validation_set.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# The Validation partition of the dataset\n",
    "\n",
    "This notebook presents some of the common practices in training a classification model, namely:\n",
    "\n",
    "- use of a validation set\n",
    "- early stopping\n",
    "- learning rate scheduler"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Auxiliary Function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "def evaluate(model, data_loader):\n",
    "\n",
    "    # sets the model in evaluation mode.\n",
    "    # although our model does not have layers which behave differently during training and evaluation\n",
    "    # this is a good practice as the models architecture may change in the future\n",
    "    model.eval()\n",
    "\n",
    "    correct = 0\n",
    "    \n",
    "    for i, (images, targets) in enumerate(data_loader):\n",
    "         \n",
    "        # forward pass, compute the output of the model for the current batch\n",
    "        outputs = model(images.to(device))\n",
    "\n",
    "        # \"max\" returns a namedtuple (values, indices) where values is the maximum \n",
    "        # value of each row of the input tensor in the given dimension dim; \n",
    "        # indices is the index location of each maximum value found (argmax).\n",
    "        # the argmax effectively provides the predicted class number        \n",
    "        _, preds = torch.max(outputs, dim=1)\n",
    "\n",
    "        correct += (preds.cpu() == targets).sum()\n",
    "\n",
    "    return (correct / len(data_loader.dataset)).item()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# The Dataset\n",
    "\n",
    "The dataset in this notebook is a subset of GTSRB, a traffic sign dataset. This subset was selected to make it more practical for classroom use. However, the concepts covered here apply to the full dataset as well.\n",
    "\n",
    "The full dataset can be found in [here](https://sid.erda.dk/public/archives/daaeac0d7ce1152aea9b61d9f1e19370/published-archive.html).\n",
    "\n",
    "GTSRB consists of 43 classes representing various traffic signs. In this subset, we focus exclusively on speed limit signs, which include 8 classes.\n",
    "\n",
    "Sample images from each class:\n",
    "\n",
    "![class 0](notebook_images/class0.png) ![class 1](notebook_images/class1.png) ![class 2](notebook_images/class2.png) ![class 3](notebook_images/class3.png) ![class 4](notebook_images/class4.png) ![class 5](notebook_images/class5.png) ![class 6](notebook_images/class6.png) ![class 7](notebook_images/class7.png)\n",
    "\n",
    "The images are organized into 8 folders, one for each class. The folder name serves as the class label. The folder structures is as follows:\n",
    "\n",
    "![folder structure](notebook_images/train_folder.png)\n",
    "\n",
    "Both training and test sets adhere to this format"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torchvision\n",
    "import torchvision.transforms as transforms\n",
    "\n",
    "import torchinfo\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import time\n",
    "\n",
    "# importing a module with utilities for displaying stats and data\n",
    "import sys\n",
    "sys.path.insert(1, 'util')\n",
    "import vcpi_util"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Dataset loading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cuda:0\n"
     ]
    }
   ],
   "source": [
    "device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')\n",
    "print(device)\n",
    "\n",
    "PATH_TRAINING_SET = 'd:/vcpi/gtsrb/train_full'\n",
    "PATH_TEST_SET = 'd:/vcpi/gtsrb/test'\n",
    "\n",
    "BATCH_SIZE = 32\n",
    "\n",
    "EPOCHS = 20"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As can be seen from the samples above, the image sizes vary. Since the model requires a fixed input size, we need to resize the images to a common dimension. Here, we are going to use 32x32."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "transform = transforms.Compose(\n",
    "    [transforms.Resize((32,32)), transforms.ToTensor()]) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_set = torchvision.datasets.ImageFolder(root=PATH_TRAINING_SET, transform = transform)\n",
    "train_loader = torch.utils.data.DataLoader(train_set, batch_size = BATCH_SIZE, shuffle=True)\n",
    "\n",
    "# No shuffle is required for the test set, also the batch size can be completely different\n",
    "# Shuffling is performed so that we can see images of various classes when inspecting the results\n",
    "test_set = torchvision.datasets.ImageFolder(root=PATH_TEST_SET, transform = transform)\n",
    "test_loader = torch.utils.data.DataLoader(test_set, batch_size = BATCH_SIZE, shuffle=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dataset info and a sample batch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Batch shape:  torch.Size([32, 3, 32, 32])\n",
      "tensor([7, 6, 2, 1, 1, 2, 5, 6, 2, 5, 1, 5, 3, 4, 6, 7, 4, 4, 7, 6, 7, 3, 3, 1,\n",
      "        5, 1, 1, 4, 4, 2, 2, 7])\n"
     ]
    }
   ],
   "source": [
    "images, targets = next(iter(train_loader))\n",
    "print(\"Batch shape: \", images.shape)\n",
    "\n",
    "print(targets)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1600x800 with 32 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "vcpi_util.show_images(4,8, images, targets, train_set.classes) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# The model\n",
    "\n",
    "The *Conv* class defines a convolutional neural network for image classification, processing 32x32 RGB images through convolutional, batch normalization, ReLU, and pooling layers, followed by a fully connected layer for final classification. The output corresponds to the number of classes in the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Conv(\n",
      "  (conv1): Conv2d(3, 16, kernel_size=(3, 3), stride=(1, 1))\n",
      "  (bn1): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "  (relu1): ReLU()\n",
      "  (conv2): Conv2d(16, 32, kernel_size=(3, 3), stride=(1, 1))\n",
      "  (bn2): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "  (relu2): ReLU()\n",
      "  (maxpool1): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      "  (conv3): Conv2d(32, 48, kernel_size=(3, 3), stride=(1, 1))\n",
      "  (bn3): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "  (relu3): ReLU()\n",
      "  (conv4): Conv2d(48, 48, kernel_size=(3, 3), stride=(1, 1))\n",
      "  (bn4): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "  (relu4): ReLU()\n",
      "  (maxpool2): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      "  (fc1): Linear(in_features=1200, out_features=8, bias=True)\n",
      ")\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "==========================================================================================\n",
       "Layer (type:depth-idx)                   Output Shape              Param #\n",
       "==========================================================================================\n",
       "Conv                                     [32, 8]                   --\n",
       "├─Conv2d: 1-1                            [32, 16, 30, 30]          448\n",
       "├─BatchNorm2d: 1-2                       [32, 16, 30, 30]          32\n",
       "├─ReLU: 1-3                              [32, 16, 30, 30]          --\n",
       "├─Conv2d: 1-4                            [32, 32, 28, 28]          4,640\n",
       "├─BatchNorm2d: 1-5                       [32, 32, 28, 28]          64\n",
       "├─ReLU: 1-6                              [32, 32, 28, 28]          --\n",
       "├─MaxPool2d: 1-7                         [32, 32, 14, 14]          --\n",
       "├─Conv2d: 1-8                            [32, 48, 12, 12]          13,872\n",
       "├─BatchNorm2d: 1-9                       [32, 48, 12, 12]          96\n",
       "├─ReLU: 1-10                             [32, 48, 12, 12]          --\n",
       "├─Conv2d: 1-11                           [32, 48, 10, 10]          20,784\n",
       "├─BatchNorm2d: 1-12                      [32, 48, 10, 10]          96\n",
       "├─ReLU: 1-13                             [32, 48, 10, 10]          --\n",
       "├─MaxPool2d: 1-14                        [32, 48, 5, 5]            --\n",
       "├─Linear: 1-15                           [32, 8]                   9,608\n",
       "==========================================================================================\n",
       "Total params: 49,640\n",
       "Trainable params: 49,640\n",
       "Non-trainable params: 0\n",
       "Total mult-adds (Units.MEGABYTES): 260.06\n",
       "==========================================================================================\n",
       "Input size (MB): 0.39\n",
       "Forward/backward pass size (MB): 26.22\n",
       "Params size (MB): 0.20\n",
       "Estimated Total Size (MB): 26.81\n",
       "=========================================================================================="
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "class Conv(torch.nn.Module):\n",
    "\n",
    "    def __init__(self, num_classes):\n",
    "        super().__init__()\n",
    "        self.conv1 = torch.nn.Conv2d(3, 16, 3)\n",
    "        self.bn1 = torch.nn.BatchNorm2d(16)\n",
    "        self.relu1 = torch.nn.ReLU()\n",
    "\n",
    "        self.conv2 = torch.nn.Conv2d(16, 32, 3)\n",
    "        self.bn2 = torch.nn.BatchNorm2d(32)\n",
    "        self.relu2 = torch.nn.ReLU()\n",
    "\n",
    "        self.maxpool1 = torch.nn.MaxPool2d(2)\n",
    "\n",
    "\n",
    "        self.conv3 = torch.nn.Conv2d(32, 48, 3)\n",
    "        self.bn3 = torch.nn.BatchNorm2d(48)\n",
    "        self.relu3 = torch.nn.ReLU()\n",
    "\n",
    "        self.conv4 = torch.nn.Conv2d(48, 48, 3)\n",
    "        self.bn4 = torch.nn.BatchNorm2d(48)\n",
    "        self.relu4 = torch.nn.ReLU()\n",
    "\n",
    "        self.maxpool2 = torch.nn.MaxPool2d(2)\n",
    "\n",
    "        self.fc1 = torch.nn.Linear(1200, num_classes)\n",
    "        \n",
    "\n",
    "    def forward(self, x):    \n",
    "        \n",
    "        # input = (bs, 3, 32, 32)\n",
    "        x = self.conv1(x) # -> (bs, 16, 26, 26)\n",
    "        x = self.bn1(x)\n",
    "        x = self.relu1(x)\n",
    "        x = self.conv2(x) # -> (bs, 32, 24, 24)\n",
    "        x = self.bn2(x)\n",
    "        x = self.relu2(x)\n",
    "        x = self.maxpool1(x)\n",
    "        \n",
    "        x = self.conv3(x) # -> (bs, 48, 12, 12)\n",
    "        x = self.bn3(x)\n",
    "        x = self.relu3(x)\n",
    "        x = self.conv4(x) # -> (bs, 48, 10, 10)\n",
    "        x = self.bn4(x)\n",
    "        x = self.relu4(x)\n",
    "        x = self.maxpool2(x)\n",
    "        \n",
    "        x = torch.flatten(x,1) # -> (bs, 48 * 5 * 5 = 1200)\n",
    "        x = self.fc1(x)        # -> (bs, 10)\n",
    "\n",
    "        return(x)\n",
    "\n",
    "\n",
    "model_Conv_I = Conv(len(train_set.classes))\n",
    "model_Conv_I.to(device)\n",
    "print(model_Conv_I)\n",
    "\n",
    "torchinfo.summary(model_Conv_I, input_size=(BATCH_SIZE, 3, 32, 32))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def train(model, train_loader, test_loader, epochs, loss_fn, optimizer):\n",
    "\n",
    "    history = {}\n",
    "    history['accuracy'] = []\n",
    "    history['test_acc'] = []\n",
    "    history['loss'] = []\n",
    "\n",
    "    for epoch in range(epochs):  # loop over the dataset multiple times\n",
    "\n",
    "        model.train()\n",
    "        start_time = time.time() \n",
    "        correct = 0\n",
    "        running_loss = 0.0\n",
    "        for i, (inputs, targets) in enumerate(train_loader, 0):\n",
    "            \n",
    "            # send data to device, hopefully the GPU\n",
    "            inputs = inputs.to(device)\n",
    "            targets = targets.to(device)\n",
    "\n",
    "            # forward pass, compute the output of the model for the current batch\n",
    "            outputs = model(inputs)\n",
    "\n",
    "\n",
    "            _, predicted = torch.max(outputs, 1)\n",
    "\n",
    "            #compute the loss between the model's outputs and the labels, or targets.\n",
    "            loss = loss_fn(outputs, targets)\n",
    "            \n",
    "            # zero the parameter gradients\n",
    "            # this function must be called before calling \"backward\" on the losses, otherwise the gradients get accumulated with previous batches.\n",
    "            optimizer.zero_grad()\n",
    "\n",
    "            # perform backpropagation\n",
    "            loss.backward()\n",
    "            #update the model's parameters\n",
    "            optimizer.step()\n",
    "\n",
    "            running_loss += loss\n",
    "            correct += (predicted == targets).sum()\n",
    "\n",
    "        model.eval()\n",
    "        t_correct = 0\n",
    "        with torch.no_grad():\n",
    "            for i,t in test_loader:\n",
    "                i = i.to(device)\n",
    "                t = t.to(device)\n",
    "                o = model(i)\n",
    "                _,p = torch.max(o,1)\n",
    "\n",
    "                t_correct += (p == t).sum()\n",
    "\n",
    "        epoch_loss = running_loss / len(train_loader.dataset)\n",
    "        accuracy = 100 * correct / len(train_loader.dataset)\n",
    "        t_accuracy = 100 * t_correct / len(test_loader.dataset)\n",
    "        stop_time = time.time()\n",
    "        print(f'Epoch: {epoch:03d}; Loss: {epoch_loss:0.4f}; Accuracy: {accuracy:0.4f}; Test_Acc: {t_accuracy:0.4f}; Elapsed time: {(stop_time - start_time):0.4f}')\n",
    "        history['accuracy'].append(accuracy.cpu().numpy())\n",
    "        history['test_acc'].append(t_accuracy.cpu().numpy())\n",
    "        history['loss'].append(running_loss.cpu().detach().numpy())\n",
    "        \n",
    "    print('Finished Training')\n",
    "\n",
    "    return(history)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 000; Loss: 0.0137; Accuracy: 85.8764; Test_Acc: 96.1391; Elapsed time: 141.2946\n",
      "Epoch: 001; Loss: 0.0013; Accuracy: 99.0532; Test_Acc: 97.2662; Elapsed time: 8.6491\n",
      "Epoch: 002; Loss: 0.0006; Accuracy: 99.6479; Test_Acc: 96.9544; Elapsed time: 8.7366\n",
      "Epoch: 003; Loss: 0.0003; Accuracy: 99.8044; Test_Acc: 98.1055; Elapsed time: 8.9221\n",
      "Epoch: 004; Loss: 0.0001; Accuracy: 99.9765; Test_Acc: 98.0096; Elapsed time: 9.0073\n",
      "Epoch: 005; Loss: 0.0000; Accuracy: 100.0000; Test_Acc: 98.1055; Elapsed time: 8.8203\n",
      "Epoch: 006; Loss: 0.0008; Accuracy: 99.2332; Test_Acc: 93.5971; Elapsed time: 8.8099\n",
      "Epoch: 007; Loss: 0.0007; Accuracy: 99.3114; Test_Acc: 97.5300; Elapsed time: 8.8114\n",
      "Epoch: 008; Loss: 0.0002; Accuracy: 99.8200; Test_Acc: 98.2734; Elapsed time: 9.0857\n",
      "Epoch: 009; Loss: 0.0003; Accuracy: 99.7966; Test_Acc: 98.4412; Elapsed time: 8.7221\n",
      "Epoch: 010; Loss: 0.0001; Accuracy: 99.9531; Test_Acc: 98.3933; Elapsed time: 8.5972\n",
      "Epoch: 011; Loss: 0.0000; Accuracy: 100.0000; Test_Acc: 98.6331; Elapsed time: 8.7321\n",
      "Epoch: 012; Loss: 0.0000; Accuracy: 100.0000; Test_Acc: 98.8010; Elapsed time: 8.7640\n",
      "Epoch: 013; Loss: 0.0000; Accuracy: 100.0000; Test_Acc: 98.7530; Elapsed time: 8.6166\n",
      "Epoch: 014; Loss: 0.0000; Accuracy: 100.0000; Test_Acc: 98.7290; Elapsed time: 8.6670\n",
      "Epoch: 015; Loss: 0.0000; Accuracy: 100.0000; Test_Acc: 98.8010; Elapsed time: 8.8631\n",
      "Epoch: 016; Loss: 0.0000; Accuracy: 100.0000; Test_Acc: 98.7050; Elapsed time: 8.8615\n",
      "Epoch: 017; Loss: 0.0000; Accuracy: 100.0000; Test_Acc: 98.8249; Elapsed time: 8.7311\n",
      "Epoch: 018; Loss: 0.0000; Accuracy: 100.0000; Test_Acc: 98.7530; Elapsed time: 8.7013\n",
      "Epoch: 019; Loss: 0.0000; Accuracy: 100.0000; Test_Acc: 98.7050; Elapsed time: 8.6723\n",
      "Finished Training\n"
     ]
    }
   ],
   "source": [
    "optimizer_I = torch.optim.Adam(model_Conv_I.parameters())\n",
    "loss_fn = torch.nn.CrossEntropyLoss()\n",
    "\n",
    "history_I = train(model_Conv_I, train_loader, test_loader, EPOCHS, loss_fn, optimizer_I)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "torch.save({\n",
    "        'model':model_Conv_I.state_dict(),\n",
    "        'optimizer': optimizer_I.state_dict()\n",
    "        },\n",
    "        f'model_gtsrb.pt')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Question: Why is the test accuracy higher than the training accuracy in the initial epochs?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Evaluation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Although they may align, the epoch with the highest training accuracy is not always the one that yields the best test data accuracy.\n",
    "\n",
    "The issue arises because training accuracy does not always reflect the model's ability to generalize to the test set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best epoch for train accuracy: : 5  Best epoch for test accuracy: : 17\n",
      "Test accuracy at epoch  5  : 98.105515 Test accuracy at epoch  17  : 98.82494\n"
     ]
    }
   ],
   "source": [
    "best_train = np.asarray(history_I['accuracy']).argmax()\n",
    "best_test = np.asarray(history_I['test_acc']).argmax()\n",
    "\n",
    "print('Best epoch for train accuracy: :', best_train,' Best epoch for test accuracy: :',  best_test)\n",
    "print('Test accuracy at epoch ',best_train, ' :', history_I['test_acc'][best_train], 'Test accuracy at epoch ',best_test, ' :', history_I['test_acc'][best_test])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "preds = []\n",
    "ground_truth = []\n",
    "\n",
    "for images, targets in test_loader:\n",
    "\n",
    "    logits = model_Conv_I(images.to(device))\n",
    "    preds_sparse = [np.argmax(x) for x in logits.cpu().detach().numpy()]\n",
    "    preds.extend(preds_sparse)\n",
    "    ground_truth.extend(targets.numpy())\n",
    "\n",
    "vcpi_util.show_confusion_matrix(ground_truth, preds, len(train_set.class_to_idx))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The confusion matrix, derived from predictions on the test set, shows varying accuracy across classes. The histogram below indicates that some classes are underrepresented, which could explain their poorer performance.\n",
    "\n",
    "- Is there a relationship between class representativeness and accuracy?\n",
    "- Or is the training set less representative of certain classes?\n",
    "\n",
    "Both of these factors could potentially explain this behavior."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dataset histogram\n",
    "\n",
    "This dataset is heavily unbalanced, we'll deal with this in later classes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Counter({2: 2250, 1: 2220, 4: 1980, 5: 1860, 6: 1440, 3: 1410, 7: 1410, 0: 210})\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "target_list = []\n",
    "for images, targets in train_loader:\n",
    "    target_list.extend(targets)\n",
    "\n",
    "vcpi_util.show_histogram(target_list, train_set.classes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x2000 with 60 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "images, targets = next(iter(test_loader))\n",
    "logits = model_Conv_I(images.to(device))\n",
    "\n",
    "predictions = torch.nn.functional.softmax(logits, dim=1).cpu().detach().numpy()\n",
    "vcpi_util.plot_predictions(images, predictions, targets, train_set.classes, 10, 3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# The Validation Set\n",
    "\n",
    "Why do we need a third dataset?\n",
    "\n",
    "- Fine tunning a model to provide a good answer on the test set directly can lead to a model biased towards that dataset.\n",
    "\n",
    "- Without a separate dataset, we have no way to evaluate how the model will perform on unseen data.\n",
    "\n",
    "The validation dataset aims at avoiding the training to be solely focused on the training set. This is achieved by having metrics on validation set influence the training process.\n",
    "\n",
    "This can be achieved without using any of the images of the validation set to compute gradients. Instead, the validation set can be used to guide which model to save, or help adapt hyper parameters such as the learning rate. This is done trough monitoring certain metrics on the validation set, such as the loss.\n",
    "\n",
    "A good validation set gives an indication of how the model will perform when faced with unseen data. The test dataset can then be used to corroborate this expectation.\n",
    "\n",
    "## Building the Validation Dataset\n",
    "\n",
    "One approach to building a validation set is to take a percentage of samples from the train set, i.e. to split the train into two subsets: one for training, and one for validation.\n",
    "\n",
    "However, having a validation dataset is a compromise, as we are splitting the training data into two subsets: the train and validation datasets. This approach reduces the amount of data available for training the model.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_sub, val_sub = torch.utils.data.random_split(train_set, [10200, 2580])\n",
    "\n",
    "train_sub_loader = torch.utils.data.DataLoader(train_sub, batch_size = BATCH_SIZE, shuffle=True)\n",
    "val_sub_loader = torch.utils.data.DataLoader(val_sub, batch_size = BATCH_SIZE)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A small cosmetic change to the train function, replacing 'test' with 'val' where appropriate."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "def train_II(model, train_loader, val_loader, epochs, loss_fn, optimizer):\n",
    "\n",
    "    \n",
    "\n",
    "    history = {}\n",
    "    history['accuracy'] = []\n",
    "    history['val_acc'] = []\n",
    "    history['loss'] = []\n",
    "\n",
    "    for epoch in range(epochs):  # loop over the dataset multiple times\n",
    "\n",
    "        model.train()\n",
    "        start_time = time.time() \n",
    "        correct = 0\n",
    "        running_loss = 0.0\n",
    "        for i, (inputs, targets) in enumerate(train_loader, 0):\n",
    "            \n",
    "            # send data to device, hopefully the GPU\n",
    "            inputs = inputs.to(device)\n",
    "            targets = targets.to(device)\n",
    "\n",
    "            # forward pass, compute the output of the model for the current batch\n",
    "            outputs = model(inputs)\n",
    "\n",
    "\n",
    "            _, predicted = torch.max(outputs, 1)\n",
    "\n",
    "            #compute the loss between the model's outputs and the labels, or targets.\n",
    "            loss = loss_fn(outputs, targets)\n",
    "            \n",
    "            # zero the parameter gradients\n",
    "            # this function must be called before calling \"backward\" on the losses, otherwise the gradients get accumulated with previous batches.\n",
    "            optimizer.zero_grad()\n",
    "\n",
    "            # perform backpropagation\n",
    "            loss.backward()\n",
    "            #update the model's parameters\n",
    "            optimizer.step()\n",
    "\n",
    "            running_loss += loss\n",
    "            correct += (predicted == targets).sum()\n",
    "\n",
    "        model.eval()\n",
    "        with torch.no_grad():\n",
    "            v_correct = 0\n",
    "            for i,t in val_loader:\n",
    "                i = i.to(device)\n",
    "                t = t.to(device)\n",
    "                o = model(i)\n",
    "                _,p = torch.max(o,1)\n",
    "\n",
    "                v_correct += (p == t).sum()\n",
    "\n",
    "        epoch_loss = running_loss / len(train_loader.dataset)\n",
    "        accuracy = 100 * correct / len(train_loader.dataset)\n",
    "        v_accuracy = 100 * v_correct / len(val_loader.dataset)\n",
    "        stop_time = time.time()\n",
    "        print(f'Epoch: {epoch:03d}; Loss: {epoch_loss:0.4f}; Accuracy: {accuracy:0.4f}; Val Acc: {v_accuracy:0.4f}; Elapsed time: {(stop_time - start_time):0.4f}')\n",
    "        history['accuracy'].append(accuracy.cpu().numpy())\n",
    "        history['val_acc'].append(v_accuracy.cpu().numpy())\n",
    "        history['loss'].append(running_loss.cpu().detach().numpy())\n",
    "        \n",
    "    print('Finished Training')\n",
    "\n",
    "    return(history)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training and evaluating"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_Conv_II = Conv(len(train_set.classes))\n",
    "model_Conv_II.to(device)\n",
    "\n",
    "optimizer_II = torch.optim.Adam(model_Conv_II.parameters())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 000; Loss: 0.0154; Accuracy: 83.9020; Val Acc: 96.5504; Elapsed time: 6.6279\n",
      "Epoch: 001; Loss: 0.0013; Accuracy: 99.1667; Val Acc: 98.6047; Elapsed time: 6.5940\n",
      "Epoch: 002; Loss: 0.0007; Accuracy: 99.5196; Val Acc: 98.8372; Elapsed time: 6.6818\n",
      "Epoch: 003; Loss: 0.0003; Accuracy: 99.8039; Val Acc: 99.3411; Elapsed time: 6.6322\n",
      "Epoch: 004; Loss: 0.0002; Accuracy: 99.9412; Val Acc: 98.4884; Elapsed time: 6.6658\n",
      "Epoch: 005; Loss: 0.0005; Accuracy: 99.6274; Val Acc: 97.5194; Elapsed time: 6.6952\n",
      "Epoch: 006; Loss: 0.0003; Accuracy: 99.7647; Val Acc: 99.1473; Elapsed time: 6.6958\n",
      "Epoch: 007; Loss: 0.0001; Accuracy: 99.9510; Val Acc: 99.4186; Elapsed time: 6.6730\n",
      "Epoch: 008; Loss: 0.0000; Accuracy: 100.0000; Val Acc: 99.6124; Elapsed time: 6.6496\n",
      "Epoch: 009; Loss: 0.0000; Accuracy: 100.0000; Val Acc: 99.5736; Elapsed time: 6.6618\n",
      "Epoch: 010; Loss: 0.0000; Accuracy: 100.0000; Val Acc: 99.5736; Elapsed time: 6.6932\n",
      "Epoch: 011; Loss: 0.0000; Accuracy: 100.0000; Val Acc: 99.5736; Elapsed time: 6.7038\n",
      "Epoch: 012; Loss: 0.0000; Accuracy: 100.0000; Val Acc: 99.6124; Elapsed time: 6.7056\n",
      "Epoch: 013; Loss: 0.0000; Accuracy: 100.0000; Val Acc: 99.6124; Elapsed time: 6.6467\n",
      "Epoch: 014; Loss: 0.0000; Accuracy: 100.0000; Val Acc: 99.5736; Elapsed time: 6.7177\n",
      "Epoch: 015; Loss: 0.0000; Accuracy: 100.0000; Val Acc: 99.6124; Elapsed time: 6.7496\n",
      "Epoch: 016; Loss: 0.0000; Accuracy: 100.0000; Val Acc: 99.6512; Elapsed time: 6.5364\n",
      "Epoch: 017; Loss: 0.0000; Accuracy: 100.0000; Val Acc: 99.5349; Elapsed time: 6.5636\n",
      "Epoch: 018; Loss: 0.0000; Accuracy: 100.0000; Val Acc: 99.6124; Elapsed time: 6.6706\n",
      "Epoch: 019; Loss: 0.0000; Accuracy: 100.0000; Val Acc: 99.6512; Elapsed time: 6.5373\n",
      "Finished Training\n"
     ]
    }
   ],
   "source": [
    "history_II = train_II(model_Conv_II, train_sub_loader, val_sub_loader, EPOCHS, loss_fn, optimizer_II)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "torch.save({\n",
    "        'model':model_Conv_II.state_dict(),\n",
    "        'optimizer': optimizer_II.state_dict()\n",
    "        },\n",
    "        f'model_gtsrb_val_I.pt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.986810564994812"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "evaluate(model_Conv_II, test_loader)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As can be seen from the losses of the test and validation sets, we have not achieved our goal. The validation loss closely mirrors the training loss, and it is not a reliable predictor of the test loss.\n",
    "\n",
    "This suggests that we should examine the data to see how we are splitting the original training set, as it may be influencing the observed results."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Inspecting the dataset\n",
    "\n",
    "The GTSRB training data consist of video sequences, each with 30 frames, leading to strong image correlation within each sequence.\n",
    "\n",
    "By randomly sampling the original training set to gather images for the validation set, we are, in average, taking a small percentage of images in each sequence. This implies that the training set may have very similar images to those in the validation set, causing the reported losses and accuracies to be closely aligned.\n",
    "\n",
    "An approach to address this issue is to build the validation set by gathering entire sequences from the training set. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Build the new validation dataset\n",
    "\n",
    "Taking into account what we said before, the new validation set will be build using full sequences from the training set, so instead of taking 20% of samples ramdomly, we built the validation dataset with approximately 20% of the sequences."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "PATH_VAL_SET = 'd:/vcpi/gtsrb/val'\n",
    "\n",
    "val_set = torchvision.datasets.ImageFolder(root=PATH_VAL_SET, transform = transform)\n",
    "val_loader = torch.utils.data.DataLoader(val_set, batch_size = BATCH_SIZE)\n",
    "\n",
    "PATH_TRAIN_PARTIAL_SET = 'd:/vcpi/gtsrb/train'\n",
    "\n",
    "train_partial = torchvision.datasets.ImageFolder(root=PATH_TRAIN_PARTIAL_SET, transform = transform)\n",
    "train_partial_loader = torch.utils.data.DataLoader(train_partial, batch_size = BATCH_SIZE,shuffle=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train and evaluate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 000; Loss: 0.0152; Accuracy: 84.2941; Val Acc: 88.1008; Elapsed time: 102.3136\n",
      "Epoch: 001; Loss: 0.0013; Accuracy: 99.1274; Val Acc: 93.1395; Elapsed time: 6.6446\n",
      "Epoch: 002; Loss: 0.0003; Accuracy: 99.9216; Val Acc: 96.6279; Elapsed time: 6.6207\n",
      "Epoch: 003; Loss: 0.0001; Accuracy: 99.9804; Val Acc: 96.7442; Elapsed time: 6.6218\n",
      "Epoch: 004; Loss: 0.0001; Accuracy: 99.9902; Val Acc: 97.0543; Elapsed time: 6.6048\n",
      "Epoch: 005; Loss: 0.0000; Accuracy: 100.0000; Val Acc: 96.7829; Elapsed time: 6.5319\n",
      "Epoch: 006; Loss: 0.0000; Accuracy: 100.0000; Val Acc: 96.9380; Elapsed time: 6.5232\n",
      "Epoch: 007; Loss: 0.0000; Accuracy: 100.0000; Val Acc: 96.8992; Elapsed time: 6.5715\n",
      "Epoch: 008; Loss: 0.0000; Accuracy: 100.0000; Val Acc: 96.9380; Elapsed time: 6.7008\n",
      "Epoch: 009; Loss: 0.0000; Accuracy: 100.0000; Val Acc: 96.8217; Elapsed time: 6.4614\n",
      "Epoch: 010; Loss: 0.0000; Accuracy: 100.0000; Val Acc: 96.9380; Elapsed time: 6.6914\n",
      "Epoch: 011; Loss: 0.0000; Accuracy: 100.0000; Val Acc: 97.0155; Elapsed time: 6.4111\n",
      "Epoch: 012; Loss: 0.0018; Accuracy: 98.3529; Val Acc: 86.7054; Elapsed time: 6.5557\n",
      "Epoch: 013; Loss: 0.0016; Accuracy: 98.3922; Val Acc: 91.5891; Elapsed time: 6.4973\n",
      "Epoch: 014; Loss: 0.0002; Accuracy: 99.8529; Val Acc: 97.4419; Elapsed time: 6.4554\n",
      "Epoch: 015; Loss: 0.0000; Accuracy: 99.9706; Val Acc: 98.0233; Elapsed time: 6.4773\n",
      "Epoch: 016; Loss: 0.0000; Accuracy: 100.0000; Val Acc: 97.7519; Elapsed time: 6.5148\n",
      "Epoch: 017; Loss: 0.0000; Accuracy: 100.0000; Val Acc: 97.8295; Elapsed time: 6.7136\n",
      "Epoch: 018; Loss: 0.0000; Accuracy: 100.0000; Val Acc: 97.6744; Elapsed time: 6.8094\n",
      "Epoch: 019; Loss: 0.0000; Accuracy: 100.0000; Val Acc: 97.7132; Elapsed time: 6.9159\n",
      "Finished Training\n"
     ]
    }
   ],
   "source": [
    "model_Conv_III = Conv(len(train_set.classes))\n",
    "model_Conv_III.to(device)\n",
    "\n",
    "optimizer_III = torch.optim.Adam(model_Conv_III.parameters())\n",
    "\n",
    "history_III = train_II(model_Conv_III, train_partial_loader, val_loader, EPOCHS, loss_fn, optimizer_III)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "torch.save({\n",
    "        'model':model_Conv_III.state_dict(),\n",
    "        'optimizer': optimizer_III.state_dict()\n",
    "        },\n",
    "        f'model_gtsrb_val_II.pt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9851319193840027"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "evaluate(model_Conv_III, test_loader)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As can be seen, the validation and test accuracies are now much closer, and the validation metrics serve as a more reliable indicator of the test metrics.\n",
    "\n",
    "Thus, we have successfully achieved our goal with the validation set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "vcpi_util.show_history_plus(history_III, ['accuracy', 'val_acc'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Early Stopping and Learning Rate Scheduler"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Early Stopping\n",
    "\n",
    "The early stopping criterion allows us to set a higher number of epochs without the concern of wasting training time. If the model learns quickly, training will stop early; if the model takes longer to train, it won't be stopped prematurely due to a limited number of epochs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Early_Stopping():\n",
    "\n",
    "    def __init__(self, patience = 3, min_delta = 0.00001):\n",
    "\n",
    "        self.patience = patience \n",
    "        self.min_delta = min_delta\n",
    "\n",
    "        self.min_delta\n",
    "        self.min_val_loss = float('inf')\n",
    "\n",
    "    def __call__(self, val_loss):\n",
    "\n",
    "        # improvement\n",
    "        if val_loss + self.min_delta < self.min_val_loss:\n",
    "            self.min_val_loss = val_loss\n",
    "            self.counter = 0\n",
    "\n",
    "        # no improvement            \n",
    "        else:\n",
    "            self.counter += 1\n",
    "            if self.counter > self.patience:\n",
    "                return True\n",
    "            \n",
    "        return False\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Learning Rate Scheduler\n",
    "\n",
    "A learning rate scheduler adjusts the learning rate during training. \n",
    "\n",
    "It usually depends only on the number of epochs or batches. Some optimizers start with a large learning rate, and gradually decrease it over time, while others use a warmup period, increasing the learning rate faster in the beginning, and then decreasing it over a longer period of time. \n",
    "\n",
    "Alternatively, the learning rate can be adjusted based on the progress of a specific metric, such as the loss. For example, we can reduce the learning rate when the model is no longer making progress after a number of epochs. This is known as the \"reduce on plateau\" approach.\n",
    "\n",
    "The list of schedulers implemented in torch can be found in the [Documentation](https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Python311\\Lib\\site-packages\\torch\\optim\\lr_scheduler.py:143: 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"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1d063896f10>]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "optimizer_aux = torch.optim.Adam(model_Conv_III.parameters(), lr=0.001)\n",
    "\n",
    "#scheduler = torch.optim.lr_scheduler.LinearLR(optimizer_aux, start_factor=1, end_factor=0.1, total_iters =20)\n",
    "scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer_aux, 10, 0.00001)\n",
    "\n",
    "lrs = []\n",
    "for epoch in range(50):\n",
    "    scheduler.step()\n",
    "    lrs.append(scheduler.get_last_lr())\n",
    "\n",
    "plt.plot(lrs)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "def train_III(model, train_loader, val_loader, epochs, loss_fn, optimizer, scheduler, early_stopper, save_prefix = 'model'):\n",
    "\n",
    "    history = {}\n",
    "    history['accuracy'] = []\n",
    "    history['val_acc'] = []\n",
    "    history['val_loss'] = []\n",
    "    history['loss'] = []\n",
    "    best_val_loss = np.inf\n",
    "\n",
    "    for epoch in range(epochs):  # loop over the dataset multiple times\n",
    "\n",
    "        model.train()\n",
    "        start_time = time.time() \n",
    "        correct = 0\n",
    "        running_loss = 0.0\n",
    "        for i, (inputs, targets) in enumerate(train_loader, 0):\n",
    "            \n",
    "            inputs = inputs.to(device)\n",
    "            targets = targets.to(device)\n",
    "\n",
    "            outputs = model(inputs)\n",
    "            _, predicted = torch.max(outputs, 1)\n",
    "\n",
    "            loss = loss_fn(outputs, targets)\n",
    "            \n",
    "            optimizer.zero_grad()\n",
    "            loss.backward()\n",
    "            optimizer.step()\n",
    "\n",
    "            running_loss += loss\n",
    "            correct += (predicted == targets).sum()\n",
    "\n",
    "        model.eval()\n",
    "        with torch.no_grad():\n",
    "            v_correct = 0\n",
    "            val_loss = 0.0\n",
    "            for i,t in val_loader:\n",
    "                i = i.to(device)\n",
    "                t = t.to(device)\n",
    "                o = model(i)\n",
    "                _,p = torch.max(o,1)\n",
    "                \n",
    "                val_loss += loss_fn(o, t)\n",
    "\n",
    "                v_correct += (p == t).sum()\n",
    "\n",
    "        old_lr = optimizer.param_groups[0]['lr']\n",
    "        scheduler.step(val_loss)\n",
    "        new_lr = optimizer.param_groups[0]['lr']\n",
    "        \n",
    "        if old_lr != new_lr:\n",
    "            print('==> Learning rate updated: ', old_lr, ' -> ', new_lr)\n",
    "\n",
    "        epoch_loss = running_loss / len(train_loader.dataset)\n",
    "        accuracy = 100 * correct / len(train_loader.dataset)\n",
    "        v_accuracy = 100 * v_correct / len(val_loader.dataset)\n",
    "        val_loss = val_loss / len(val_loader.dataset)\n",
    "        stop_time = time.time()\n",
    "        print(f'Epoch: {epoch:03d}; Loss: {epoch_loss:0.6f}; Accuracy: {accuracy:0.4f}; Val Loss: {val_loss:0.6f}; Val Acc: {v_accuracy:0.4f}; Elapsed time: {(stop_time - start_time):0.4f}')\n",
    "        history['accuracy'].append(accuracy.cpu().numpy())\n",
    "        history['val_acc'].append(v_accuracy.cpu().numpy())\n",
    "        history['val_loss'].append(val_loss.cpu().detach().numpy())\n",
    "        history['loss'].append(epoch_loss.cpu().detach().numpy())\n",
    " \n",
    "        ###### Saving ######\n",
    "        if val_loss < best_val_loss:\n",
    "            best_val_loss = val_loss\n",
    "            torch.save({\n",
    "                'epoch': epoch,\n",
    "                'model':model.state_dict(),\n",
    "                'optimizer': optimizer.state_dict(),\n",
    "                'scheduler': scheduler.state_dict()\n",
    "                },\n",
    "                f'{save_prefix}_{epoch}.pt')\n",
    "\n",
    "        if early_stopper(val_loss):\n",
    "            print('Early stopping!')\n",
    "            break\n",
    "        \n",
    "    print('Finished Training')\n",
    "\n",
    "    return(history)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 000; Loss: 0.015527; Accuracy: 83.9510; Val Loss: 0.008105; Val Acc: 90.8915; Elapsed time: 6.7549\n",
      "Epoch: 001; Loss: 0.001155; Accuracy: 99.2451; Val Loss: 0.007766; Val Acc: 90.6589; Elapsed time: 6.7563\n",
      "Epoch: 002; Loss: 0.000479; Accuracy: 99.7157; Val Loss: 0.005515; Val Acc: 93.7209; Elapsed time: 6.8459\n",
      "Epoch: 003; Loss: 0.000327; Accuracy: 99.7941; Val Loss: 0.005244; Val Acc: 94.1085; Elapsed time: 6.9395\n",
      "Epoch: 004; Loss: 0.000130; Accuracy: 99.9608; Val Loss: 0.002958; Val Acc: 96.8605; Elapsed time: 6.9034\n",
      "Epoch: 005; Loss: 0.000026; Accuracy: 100.0000; Val Loss: 0.002281; Val Acc: 97.4419; Elapsed time: 6.7773\n",
      "Epoch: 006; Loss: 0.000013; Accuracy: 100.0000; Val Loss: 0.002289; Val Acc: 97.3256; Elapsed time: 6.6079\n",
      "Epoch: 007; Loss: 0.000009; Accuracy: 100.0000; Val Loss: 0.002330; Val Acc: 97.4419; Elapsed time: 6.8764\n",
      "Epoch: 008; Loss: 0.000007; Accuracy: 100.0000; Val Loss: 0.002145; Val Acc: 97.7132; Elapsed time: 6.8496\n",
      "Epoch: 009; Loss: 0.000005; Accuracy: 100.0000; Val Loss: 0.002236; Val Acc: 97.6357; Elapsed time: 6.8232\n",
      "Epoch: 010; Loss: 0.000004; Accuracy: 100.0000; Val Loss: 0.002152; Val Acc: 97.7907; Elapsed time: 6.7041\n",
      "Epoch: 011; Loss: 0.000003; Accuracy: 100.0000; Val Loss: 0.002216; Val Acc: 97.6357; Elapsed time: 6.6279\n",
      "==> Learning rate updated:  0.001  ->  0.0001\n",
      "Epoch: 012; Loss: 0.000003; Accuracy: 100.0000; Val Loss: 0.002250; Val Acc: 97.5969; Elapsed time: 6.8223\n",
      "Epoch: 013; Loss: 0.000002; Accuracy: 100.0000; Val Loss: 0.002192; Val Acc: 97.7907; Elapsed time: 6.9152\n",
      "Epoch: 014; Loss: 0.000002; Accuracy: 100.0000; Val Loss: 0.002122; Val Acc: 97.8682; Elapsed time: 6.8888\n",
      "Epoch: 015; Loss: 0.000002; Accuracy: 100.0000; Val Loss: 0.002136; Val Acc: 97.7907; Elapsed time: 6.9049\n",
      "Epoch: 016; Loss: 0.000002; Accuracy: 100.0000; Val Loss: 0.002174; Val Acc: 97.6744; Elapsed time: 6.9499\n",
      "Epoch: 017; Loss: 0.000002; Accuracy: 100.0000; Val Loss: 0.002276; Val Acc: 97.4806; Elapsed time: 7.3633\n",
      "==> Learning rate updated:  0.0001  ->  1e-05\n",
      "Epoch: 018; Loss: 0.000002; Accuracy: 100.0000; Val Loss: 0.002164; Val Acc: 97.6744; Elapsed time: 7.0193\n",
      "Epoch: 019; Loss: 0.000002; Accuracy: 100.0000; Val Loss: 0.002362; Val Acc: 97.5194; Elapsed time: 6.7643\n",
      "Epoch: 020; Loss: 0.000002; Accuracy: 100.0000; Val Loss: 0.002100; Val Acc: 97.9070; Elapsed time: 6.6180\n",
      "Epoch: 021; Loss: 0.000002; Accuracy: 100.0000; Val Loss: 0.002145; Val Acc: 97.7519; Elapsed time: 6.5134\n",
      "Epoch: 022; Loss: 0.000002; Accuracy: 100.0000; Val Loss: 0.002097; Val Acc: 97.7907; Elapsed time: 6.6497\n",
      "Epoch: 023; Loss: 0.000002; Accuracy: 100.0000; Val Loss: 0.002197; Val Acc: 97.5581; Elapsed time: 6.7113\n",
      "Epoch: 024; Loss: 0.000002; Accuracy: 100.0000; Val Loss: 0.002209; Val Acc: 97.5969; Elapsed time: 6.6272\n",
      "Epoch: 025; Loss: 0.000002; Accuracy: 100.0000; Val Loss: 0.002063; Val Acc: 97.9070; Elapsed time: 6.8714\n",
      "Epoch: 026; Loss: 0.000002; Accuracy: 100.0000; Val Loss: 0.002034; Val Acc: 98.0233; Elapsed time: 6.6330\n",
      "Epoch: 027; Loss: 0.000002; Accuracy: 100.0000; Val Loss: 0.002149; Val Acc: 97.7132; Elapsed time: 6.5568\n",
      "Epoch: 028; Loss: 0.000002; Accuracy: 100.0000; Val Loss: 0.002199; Val Acc: 97.5969; Elapsed time: 6.4627\n",
      "Epoch: 029; Loss: 0.000002; Accuracy: 100.0000; Val Loss: 0.002192; Val Acc: 97.6744; Elapsed time: 6.5302\n",
      "==> Learning rate updated:  1e-05  ->  1.0000000000000002e-06\n",
      "Epoch: 030; Loss: 0.000002; Accuracy: 100.0000; Val Loss: 0.002146; Val Acc: 97.7519; Elapsed time: 6.4421\n",
      "Epoch: 031; Loss: 0.000002; Accuracy: 100.0000; Val Loss: 0.002272; Val Acc: 97.5969; Elapsed time: 6.4573\n",
      "Epoch: 032; Loss: 0.000002; Accuracy: 100.0000; Val Loss: 0.002132; Val Acc: 97.7907; Elapsed time: 6.4577\n",
      "Epoch: 033; Loss: 0.000002; Accuracy: 100.0000; Val Loss: 0.002133; Val Acc: 97.7132; Elapsed time: 6.4444\n",
      "==> Learning rate updated:  1.0000000000000002e-06  ->  1.0000000000000002e-07\n",
      "Epoch: 034; Loss: 0.000002; Accuracy: 100.0000; Val Loss: 0.002152; Val Acc: 97.7519; Elapsed time: 6.4817\n",
      "Epoch: 035; Loss: 0.000002; Accuracy: 100.0000; Val Loss: 0.002228; Val Acc: 97.5969; Elapsed time: 6.4962\n",
      "Epoch: 036; Loss: 0.000002; Accuracy: 100.0000; Val Loss: 0.002269; Val Acc: 97.5194; Elapsed time: 6.4488\n",
      "Early stopping!\n",
      "Finished Training\n"
     ]
    }
   ],
   "source": [
    "model_Conv_IV = Conv(len(train_set.classes))\n",
    "model_Conv_IV.to(device)\n",
    "\n",
    "optimizer_IV = torch.optim.Adam(model_Conv_IV.parameters())\n",
    "scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer_IV, mode='min', factor = 0.1, patience=3)\n",
    "\n",
    "early_stop = Early_Stopping(9)\n",
    "\n",
    "history_IV = train_III(model_Conv_IV, train_partial_loader, val_loader, 100, loss_fn, optimizer_IV, scheduler, early_stop, 'model_gtsrb_best')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "26\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.9798561334609985"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reload = torch.load(f'model_gtsrb_best_26.pt')\n",
    "print(reload['epoch'])\n",
    "model_Conv_best = Conv(len(train_set.classes))\n",
    "model_Conv_best.to(device)\n",
    "model_Conv_best.load_state_dict(reload['model'])\n",
    "evaluate(model_Conv_best, test_loader)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "vcpi_util.show_histories([history_II, history_III, history_IV],['2','3','4'], 'val_acc')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.11.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}