H4P2T2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Build a DNN for ECG Signal Classification (5 classes)  - Pytorch\n",
    "This is the implementation of an MLP for classifying the ECG signals. <br>\n",
    "Your task is to design new DNNs for ECG signal classification <br>\n",
    "You can use this file as a template"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as nnF\n",
    "from torch.utils.data import DataLoader as torch_dataloader\n",
    "from torch.utils.data import Dataset as torch_dataset\n",
    "import torch.optim as optim"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The Neural Network: CNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "class CNN(nn.Module):\n",
    "    def __init__(self):\n",
    "        super(CNN, self).__init__()\n",
    "        self.conv1 = nn.Sequential(\n",
    "            nn.Conv1d(1, 64, kernel_size=3, padding=1),\n",
    "            nn.ReLU(),\n",
    "        )\n",
    "        self.conv2 = nn.Sequential(\n",
    "            nn.Conv1d(64, 64, kernel_size=3, padding=1),\n",
    "            nn.ReLU(),\n",
    "        )\n",
    "\n",
    "        self.pool1 = nn.AvgPool1d(kernel_size=2, stride=2)\n",
    "        self.norm3 = nn.BatchNorm1d(64)\n",
    "        self.conv3 = nn.Sequential(\n",
    "            nn.Conv1d(64, 64, kernel_size=3, padding=1),\n",
    "            nn.ReLU(),\n",
    "        )\n",
    "\n",
    "        self.pool2 = nn.AvgPool1d(kernel_size=2, stride=2)\n",
    "        self.norm4 = nn.BatchNorm1d(64)\n",
    "        self.conv4 = nn.Sequential(\n",
    "            nn.Conv1d(64, 64, kernel_size=3, padding=1),\n",
    "            nn.ReLU(),\n",
    "        )\n",
    "\n",
    "        self.pool3 = nn.AvgPool1d(kernel_size=2, stride=2)\n",
    "        self.norm5 = nn.BatchNorm1d(64)\n",
    "        self.conv5 = nn.Sequential(\n",
    "            nn.Conv1d(64, 64, kernel_size=3, padding=1),\n",
    "            nn.ReLU(),\n",
    "        )\n",
    "\n",
    "        feature_size = (187 // 8) * 64\n",
    "\n",
    "        self.fc1 = nn.Sequential(\n",
    "            nn.Linear(feature_size, 1024),\n",
    "            nn.ReLU(),\n",
    "        )\n",
    "        self.fc2 = nn.Linear(1024, 5)\n",
    "\n",
    "    def forward(self, x):\n",
    "        x1 = self.conv1(x)\n",
    "        x1a = self.pool1(x1)\n",
    "\n",
    "        x2c = self.conv2(x1a)\n",
    "        x2 = x2c + x1a\n",
    "        x2a = self.pool2(x2)\n",
    "\n",
    "        x3c = self.conv3(self.norm3(x2a))\n",
    "        x3 = x3c + x2a\n",
    "        x3a = self.pool3(x3)\n",
    "\n",
    "        x4c = self.conv4(self.norm4(x3a))\n",
    "        x4 = x4c + x3a\n",
    "\n",
    "        x5 = self.conv5(self.norm5(x4))\n",
    "\n",
    "        x5r = x5.view(x5.size(0), -1)\n",
    "\n",
    "        x6 = self.fc1(x5r)\n",
    "        z = self.fc2(x6)\n",
    "        #y=nnF.softmax(z, dim=1)\n",
    "        return z"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[W NNPACK.cpp:61] Could not initialize NNPACK! Reason: Unsupported hardware.\n"
     ]
    }
   ],
   "source": [
    "#debug your network here\n",
    "#make sure it works \n",
    "model=CNN()\n",
    "x=torch.rand(10,1,187) # since network is CNN\n",
    "z=model(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def save_checkpoint(filename, model, optimizer, result, epoch):\n",
    "    torch.save({'epoch': epoch,\n",
    "                'model_state_dict': model.state_dict(),\n",
    "                'optimizer_state_dict': optimizer.state_dict(),\n",
    "                'result':result},\n",
    "               filename)\n",
    "    print('saved:', filename)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def cal_accuracy(confusion):\n",
    "    #input: confusion is the confusion matrix\n",
    "    #output: acc is the standard classification accuracy\n",
    "    M=confusion.copy().astype('float32')\n",
    "    acc = M.diagonal().sum()/M.sum()    \n",
    "    sens=np.zeros(M.shape[0])\n",
    "    prec=np.zeros(M.shape[0]) \n",
    "    for n in range(0, M.shape[0]):\n",
    "        TP=M[n,n]\n",
    "        FN=np.sum(M[n,:])-TP\n",
    "        FP=np.sum(M[:,n])-TP\n",
    "        sens[n]=TP/(TP+FN)\n",
    "        prec[n]=TP/(TP+FP)       \n",
    "    return acc, sens, prec"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The function to train the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def train(model, device, optimizer, dataloader, epoch):    \n",
    "    model.train()#set model to training mode\n",
    "    loss_train=0\n",
    "    acc_train =0 \n",
    "    sample_count=0\n",
    "    for batch_idx, (X, Y) in enumerate(dataloader):\n",
    "        X, Y = X.to(device), Y.to(device)\n",
    "        optimizer.zero_grad()#clear grad of each parameter\n",
    "        Z = model(X)#forward pass\n",
    "        loss = nnF.cross_entropy(Z, Y)\n",
    "        loss.backward()#backward pass\n",
    "        optimizer.step()#update parameters\n",
    "        loss_train+=loss.item()\n",
    "        #do not need softmax\n",
    "        Yp = Z.data.max(dim=1)[1]  # get the index of the max               \n",
    "        acc_train+= torch.sum(Yp==Y).item()\n",
    "        sample_count+=X.size(0)\n",
    "        if batch_idx % 100 == 0:\n",
    "            print('Train Epoch: {} [{:.0f}%]\\tLoss: {:.6f}'.format(\n",
    "                    epoch, 100. * batch_idx / len(dataloader), loss.item()))\n",
    "    loss_train/=len(dataloader)\n",
    "    #due to upsampling, len(dataloader.dataset) != sample_count\n",
    "    #acc_train/=len(dataloader.dataset) \n",
    "    acc_train/=sample_count    \n",
    "    return loss_train, acc_train"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The Function to test the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def test(model, device, dataloader):\n",
    "    model.eval()#set model to evaluation mode\n",
    "    acc_test =0\n",
    "    confusion=np.zeros((5,5))\n",
    "    with torch.no_grad(): # tell Pytorch not to build graph in the with section\n",
    "        for batch_idx, (X, Y) in enumerate(dataloader):\n",
    "            X, Y = X.to(device), Y.to(device)\n",
    "            Z = model(X)#forward pass\n",
    "            #do not need softmax\n",
    "            Yp = Z.data.max(dim=1)[1]  # get the index of the max \n",
    "            acc_test+= torch.sum(Yp==Y).item()\n",
    "            for i in range(0, 5):\n",
    "                for j in range(0, 5):\n",
    "                    confusion[i,j]+=torch.sum((Y==i)&(Yp==j)).item()\n",
    "    acc, sens, prec=cal_accuracy(confusion)\n",
    "    return acc, (confusion, sens, prec)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load data and create dataloaders \n",
    "### You may need to edit MyDataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "class MyDataset(torch_dataset):\n",
    "    def __init__(self, X, Y):\n",
    "        self.X=X\n",
    "        self.Y=Y\n",
    "    def __len__(self):\n",
    "        #return the number of data points\n",
    "        return self.X.shape[0]\n",
    "    def __getitem__(self, idx):        \n",
    "        #we can use DatasetName[idx] to get a data point (x,y) with index idx\n",
    "        x=torch.tensor(self.X[idx], dtype=torch.float32)\n",
    "        y=torch.tensor(self.Y[idx], dtype=torch.int64)\n",
    "        x=x.reshape(1,-1) # if network is CNN\n",
    "        return x, y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Attention: this dataset does not have class-imbalance issue."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "X=pd.read_csv('ECG_dataX.csv')\n",
    "Y=pd.read_csv('ECG_dataY.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3841, 187)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#convert dataframe to numpy array\n",
    "X=X.values\n",
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3841, 1)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#convert dataframe to numpy array\n",
    "Y=Y.values\n",
    "Y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3841,)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#reshape Y into a 1D array\n",
    "Y=Y.reshape(-1)\n",
    "Y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([800.,   0., 800.,   0.,   0., 800.,   0., 641.,   0., 800.]),\n",
       " array([0. , 0.4, 0.8, 1.2, 1.6, 2. , 2.4, 2.8, 3.2, 3.6, 4. ]),\n",
       " <BarContainer object of 10 artists>)"
      ]
     },
     "execution_count": 13,
     "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": [
    "plt.hist(Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1000 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fs=125  # sampling frequency\n",
    "Ts=1/fs # sampling interval\n",
    "N=187 # the number of timepoints\n",
    "Duration=N*Ts # duration of a signal\n",
    "t=np.linspace(0, Duration-Ts, N) # array of timepoints\n",
    "fig, ax = plt.subplots(5,1,constrained_layout=True, figsize=(10,10))\n",
    "for c in range(0, 5):   \n",
    "    for n in range(0, 3):\n",
    "        idx=np.random.randint(0,10)\n",
    "        ax[c].plot(t, X[Y==c][idx])        \n",
    "        ax[c].set_xlabel('time t [seconds]', fontsize=16)\n",
    "        ax[c].set_ylabel('amplitude', fontsize=16)\n",
    "    ax[c].set_title('class '+str(c), fontsize=16)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2, random_state=0)\n",
    "X_train, X_val, Y_train, Y_val = train_test_split(X_train, Y_train, test_size=0.1, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset_train=MyDataset(X_train, Y_train)\n",
    "dataset_val=MyDataset(X_val, Y_val)\n",
    "dataset_test=MyDataset(X_test, Y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "loader_train = torch_dataloader(dataset_train, batch_size=128, shuffle=True, num_workers=0)\n",
    "loader_val = torch_dataloader(dataset_val, batch_size=128, shuffle=False, num_workers=0) \n",
    "loader_test = torch_dataloader(dataset_test, batch_size=128, shuffle=False, num_workers=0) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create a model, and start the traning-validation-testing process"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
    "model=CNN()\n",
    "model.to(device)\n",
    "x=dataset_train[0][0]\n",
    "x=x.view(1, 1, 187).to(device) #change it to x=x.view(1,1,187).to(device) for CNN\n",
    "z=model(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[0.2234, 0.1862, 0.2065, 0.1861, 0.1978]], grad_fn=<SoftmaxBackward0>)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_hat=nnF.softmax(z, dim=1)\n",
    "y_hat"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "use stochastic gradient descent as the optimization method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "optimizer = optim.Adamax(model.parameters(), lr=0.001, weight_decay=1e-4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "loss_train_list=[]\n",
    "acc_train_list=[]\n",
    "acc_val_list=[]\n",
    "epoch_save=-1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "train/val/test over many epochs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Epoch: 0 [0%]\tLoss: 1.610683\n",
      "epoch 0 training loss: 0.799447010863911 acc: 0.7051374819102749\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/xs/czdjg8q54cz21bqm4kfr5zz80000gn/T/ipykernel_2181/3076453557.py:13: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  prec[n]=TP/(TP+FP)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch 0 validation acc: 0.19155844\n",
      "saved: ECG_MLP_Pytorch_epoch0.pt\n",
      "Train Epoch: 1 [0%]\tLoss: 0.467122\n",
      "epoch 1 training loss: 0.4449270963668823 acc: 0.8382778581765558\n",
      "epoch 1 validation acc: 0.5064935\n",
      "saved: ECG_MLP_Pytorch_epoch1.pt\n",
      "Train Epoch: 2 [0%]\tLoss: 0.347181\n",
      "epoch 2 training loss: 0.34770258787003433 acc: 0.8726483357452967\n",
      "epoch 2 validation acc: 0.7987013\n",
      "saved: ECG_MLP_Pytorch_epoch2.pt\n",
      "Train Epoch: 3 [0%]\tLoss: 0.365459\n",
      "epoch 3 training loss: 0.2941947430372238 acc: 0.8932706222865412\n",
      "epoch 3 validation acc: 0.84415585\n",
      "saved: ECG_MLP_Pytorch_epoch3.pt\n",
      "Train Epoch: 4 [0%]\tLoss: 0.181635\n",
      "epoch 4 training loss: 0.24824860285628925 acc: 0.9106367583212736\n",
      "epoch 4 validation acc: 0.84415585\n",
      "saved: ECG_MLP_Pytorch_epoch4.pt\n",
      "Train Epoch: 5 [0%]\tLoss: 0.257212\n",
      "epoch 5 training loss: 0.20610239627686414 acc: 0.9254703328509407\n",
      "epoch 5 validation acc: 0.8798701\n",
      "saved: ECG_MLP_Pytorch_epoch5.pt\n",
      "Train Epoch: 6 [0%]\tLoss: 0.171226\n",
      "epoch 6 training loss: 0.17604400725527244 acc: 0.9413892908827786\n",
      "epoch 6 validation acc: 0.8668831\n",
      "saved: ECG_MLP_Pytorch_epoch6.pt\n",
      "Train Epoch: 7 [0%]\tLoss: 0.176600\n",
      "epoch 7 training loss: 0.1453516994687644 acc: 0.9522431259044862\n",
      "epoch 7 validation acc: 0.8084416\n",
      "saved: ECG_MLP_Pytorch_epoch7.pt\n",
      "Train Epoch: 8 [0%]\tLoss: 0.090257\n",
      "epoch 8 training loss: 0.12954753095453436 acc: 0.9587554269175108\n",
      "epoch 8 validation acc: 0.8863636\n",
      "saved: ECG_MLP_Pytorch_epoch8.pt\n",
      "Train Epoch: 9 [0%]\tLoss: 0.164172\n",
      "epoch 9 training loss: 0.10589142922650684 acc: 0.967438494934877\n",
      "epoch 9 validation acc: 0.8896104\n",
      "saved: ECG_MLP_Pytorch_epoch9.pt\n",
      "Train Epoch: 10 [0%]\tLoss: 0.103189\n",
      "epoch 10 training loss: 0.09958100132644176 acc: 0.9685238784370478\n",
      "epoch 10 validation acc: 0.8636364\n",
      "saved: ECG_MLP_Pytorch_epoch10.pt\n",
      "Train Epoch: 11 [0%]\tLoss: 0.052127\n",
      "epoch 11 training loss: 0.08113905872133645 acc: 0.9768451519536903\n",
      "epoch 11 validation acc: 0.8896104\n",
      "saved: ECG_MLP_Pytorch_epoch11.pt\n",
      "Train Epoch: 12 [0%]\tLoss: 0.103162\n",
      "epoch 12 training loss: 0.07707613198594614 acc: 0.9775687409551375\n",
      "epoch 12 validation acc: 0.8831169\n",
      "saved: ECG_MLP_Pytorch_epoch12.pt\n",
      "Train Epoch: 13 [0%]\tLoss: 0.090000\n",
      "epoch 13 training loss: 0.06596378643404353 acc: 0.9819102749638206\n",
      "epoch 13 validation acc: 0.8668831\n",
      "saved: ECG_MLP_Pytorch_epoch13.pt\n",
      "Train Epoch: 14 [0%]\tLoss: 0.088052\n",
      "epoch 14 training loss: 0.06206105107610876 acc: 0.9829956584659914\n",
      "epoch 14 validation acc: 0.8733766\n",
      "saved: ECG_MLP_Pytorch_epoch14.pt\n",
      "Train Epoch: 15 [0%]\tLoss: 0.018883\n",
      "epoch 15 training loss: 0.05244269636882977 acc: 0.9884225759768451\n",
      "epoch 15 validation acc: 0.85064936\n",
      "saved: ECG_MLP_Pytorch_epoch15.pt\n",
      "Train Epoch: 16 [0%]\tLoss: 0.081586\n",
      "epoch 16 training loss: 0.04924793050370433 acc: 0.9866136034732272\n",
      "epoch 16 validation acc: 0.8766234\n",
      "saved: ECG_MLP_Pytorch_epoch16.pt\n",
      "Train Epoch: 17 [0%]\tLoss: 0.026916\n",
      "epoch 17 training loss: 0.04087883115492084 acc: 0.9902315484804631\n",
      "epoch 17 validation acc: 0.8636364\n",
      "saved: ECG_MLP_Pytorch_epoch17.pt\n",
      "Train Epoch: 18 [0%]\tLoss: 0.042860\n",
      "epoch 18 training loss: 0.03417209760201248 acc: 0.993849493487699\n",
      "epoch 18 validation acc: 0.8668831\n",
      "saved: ECG_MLP_Pytorch_epoch18.pt\n",
      "Train Epoch: 19 [0%]\tLoss: 0.015403\n",
      "epoch 19 training loss: 0.03913264835930683 acc: 0.9891461649782923\n",
      "epoch 19 validation acc: 0.8603896\n",
      "saved: ECG_MLP_Pytorch_epoch19.pt\n",
      "Train Epoch: 20 [0%]\tLoss: 0.035105\n",
      "epoch 20 training loss: 0.03734612816267393 acc: 0.9887843704775687\n",
      "epoch 20 validation acc: 0.8766234\n",
      "saved: ECG_MLP_Pytorch_epoch20.pt\n",
      "Train Epoch: 21 [0%]\tLoss: 0.047078\n",
      "epoch 21 training loss: 0.031102611471644857 acc: 0.992040520984081\n",
      "epoch 21 validation acc: 0.8636364\n",
      "saved: ECG_MLP_Pytorch_epoch21.pt\n",
      "Train Epoch: 22 [0%]\tLoss: 0.007509\n",
      "epoch 22 training loss: 0.021073719634759156 acc: 0.9963820549927641\n",
      "epoch 22 validation acc: 0.8831169\n",
      "saved: ECG_MLP_Pytorch_epoch22.pt\n",
      "Train Epoch: 23 [0%]\tLoss: 0.015343\n",
      "epoch 23 training loss: 0.01955226935784925 acc: 0.9952966714905933\n",
      "epoch 23 validation acc: 0.8766234\n",
      "saved: ECG_MLP_Pytorch_epoch23.pt\n",
      "Train Epoch: 24 [0%]\tLoss: 0.006577\n",
      "epoch 24 training loss: 0.01818334704942324 acc: 0.9960202604920405\n",
      "epoch 24 validation acc: 0.8733766\n",
      "saved: ECG_MLP_Pytorch_epoch24.pt\n",
      "Train Epoch: 25 [0%]\tLoss: 0.013218\n",
      "epoch 25 training loss: 0.01743292563002218 acc: 0.9956584659913169\n",
      "epoch 25 validation acc: 0.8733766\n",
      "saved: ECG_MLP_Pytorch_epoch25.pt\n",
      "Train Epoch: 26 [0%]\tLoss: 0.008807\n",
      "epoch 26 training loss: 0.01603961663997986 acc: 0.9963820549927641\n",
      "epoch 26 validation acc: 0.8733766\n",
      "saved: ECG_MLP_Pytorch_epoch26.pt\n",
      "Train Epoch: 27 [0%]\tLoss: 0.007335\n",
      "epoch 27 training loss: 0.015641661106862804 acc: 0.9963820549927641\n",
      "epoch 27 validation acc: 0.8831169\n",
      "saved: ECG_MLP_Pytorch_epoch27.pt\n",
      "Train Epoch: 28 [0%]\tLoss: 0.015744\n",
      "epoch 28 training loss: 0.015404135920107365 acc: 0.9967438494934877\n",
      "epoch 28 validation acc: 0.8766234\n",
      "saved: ECG_MLP_Pytorch_epoch28.pt\n",
      "Train Epoch: 29 [0%]\tLoss: 0.025233\n",
      "epoch 29 training loss: 0.014771026474508371 acc: 0.9952966714905933\n",
      "epoch 29 validation acc: 0.8766234\n",
      "saved: ECG_MLP_Pytorch_epoch29.pt\n",
      "Train Epoch: 30 [0%]\tLoss: 0.006892\n",
      "epoch 30 training loss: 0.013414294353093614 acc: 0.9971056439942113\n",
      "epoch 30 validation acc: 0.8733766\n",
      "saved: ECG_MLP_Pytorch_epoch30.pt\n",
      "Train Epoch: 31 [0%]\tLoss: 0.016283\n",
      "epoch 31 training loss: 0.017063058732839472 acc: 0.9956584659913169\n",
      "epoch 31 validation acc: 0.8701299\n",
      "saved: ECG_MLP_Pytorch_epoch31.pt\n",
      "Train Epoch: 32 [0%]\tLoss: 0.006555\n",
      "epoch 32 training loss: 0.016686590134420178 acc: 0.9949348769898697\n",
      "epoch 32 validation acc: 0.8798701\n",
      "saved: ECG_MLP_Pytorch_epoch32.pt\n",
      "Train Epoch: 33 [0%]\tLoss: 0.009818\n",
      "epoch 33 training loss: 0.014339509716426784 acc: 0.9960202604920405\n",
      "epoch 33 validation acc: 0.8831169\n",
      "saved: ECG_MLP_Pytorch_epoch33.pt\n",
      "Train Epoch: 34 [0%]\tLoss: 0.004134\n",
      "epoch 34 training loss: 0.014129704859277064 acc: 0.9960202604920405\n",
      "epoch 34 validation acc: 0.84090906\n",
      "saved: ECG_MLP_Pytorch_epoch34.pt\n",
      "Train Epoch: 35 [0%]\tLoss: 0.004362\n",
      "epoch 35 training loss: 0.014498016363094475 acc: 0.9960202604920405\n",
      "epoch 35 validation acc: 0.8766234\n",
      "saved: ECG_MLP_Pytorch_epoch35.pt\n",
      "Train Epoch: 36 [0%]\tLoss: 0.007553\n",
      "epoch 36 training loss: 0.011304552582177248 acc: 0.9971056439942113\n",
      "epoch 36 validation acc: 0.8701299\n",
      "saved: ECG_MLP_Pytorch_epoch36.pt\n",
      "Train Epoch: 37 [0%]\tLoss: 0.003338\n",
      "epoch 37 training loss: 0.008646726872856643 acc: 0.9974674384949349\n",
      "epoch 37 validation acc: 0.8798701\n",
      "saved: ECG_MLP_Pytorch_epoch37.pt\n",
      "Train Epoch: 38 [0%]\tLoss: 0.008158\n",
      "epoch 38 training loss: 0.01736657841088758 acc: 0.9949348769898697\n",
      "epoch 38 validation acc: 0.8668831\n",
      "saved: ECG_MLP_Pytorch_epoch38.pt\n",
      "Train Epoch: 39 [0%]\tLoss: 0.009642\n",
      "epoch 39 training loss: 0.01862962406382642 acc: 0.9949348769898697\n",
      "epoch 39 validation acc: 0.8733766\n",
      "saved: ECG_MLP_Pytorch_epoch39.pt\n",
      "Train Epoch: 40 [0%]\tLoss: 0.007138\n",
      "epoch 40 training loss: 0.007950966182926839 acc: 0.9989146164978292\n",
      "epoch 40 validation acc: 0.8733766\n",
      "saved: ECG_MLP_Pytorch_epoch40.pt\n",
      "Train Epoch: 41 [0%]\tLoss: 0.012806\n",
      "epoch 41 training loss: 0.00680153238565915 acc: 0.9985528219971056\n",
      "epoch 41 validation acc: 0.8636364\n",
      "saved: ECG_MLP_Pytorch_epoch41.pt\n",
      "Train Epoch: 42 [0%]\tLoss: 0.021165\n",
      "epoch 42 training loss: 0.006786829522091218 acc: 0.9974674384949349\n",
      "epoch 42 validation acc: 0.8831169\n",
      "saved: ECG_MLP_Pytorch_epoch42.pt\n",
      "Train Epoch: 43 [0%]\tLoss: 0.002665\n",
      "epoch 43 training loss: 0.004325663115278902 acc: 0.9992764109985528\n",
      "epoch 43 validation acc: 0.8766234\n",
      "saved: ECG_MLP_Pytorch_epoch43.pt\n",
      "Train Epoch: 44 [0%]\tLoss: 0.003110\n",
      "epoch 44 training loss: 0.005920983263587748 acc: 0.9985528219971056\n",
      "epoch 44 validation acc: 0.8831169\n",
      "saved: ECG_MLP_Pytorch_epoch44.pt\n",
      "Train Epoch: 45 [0%]\tLoss: 0.009506\n",
      "epoch 45 training loss: 0.005980487717103891 acc: 0.9985528219971056\n",
      "epoch 45 validation acc: 0.8668831\n",
      "saved: ECG_MLP_Pytorch_epoch45.pt\n",
      "Train Epoch: 46 [0%]\tLoss: 0.005046\n",
      "epoch 46 training loss: 0.005305259514981034 acc: 0.9996382054992764\n",
      "epoch 46 validation acc: 0.8798701\n",
      "saved: ECG_MLP_Pytorch_epoch46.pt\n",
      "Train Epoch: 47 [0%]\tLoss: 0.004141\n",
      "epoch 47 training loss: 0.0033936030707660725 acc: 0.9996382054992764\n",
      "epoch 47 validation acc: 0.8798701\n",
      "saved: ECG_MLP_Pytorch_epoch47.pt\n",
      "Train Epoch: 48 [0%]\tLoss: 0.003538\n",
      "epoch 48 training loss: 0.003058962185274471 acc: 1.0\n",
      "epoch 48 validation acc: 0.8831169\n",
      "saved: ECG_MLP_Pytorch_epoch48.pt\n",
      "Train Epoch: 49 [0%]\tLoss: 0.003550\n",
      "epoch 49 training loss: 0.002371821952031248 acc: 0.9996382054992764\n",
      "epoch 49 validation acc: 0.8831169\n",
      "saved: ECG_MLP_Pytorch_epoch49.pt\n",
      "Train Epoch: 50 [0%]\tLoss: 0.001123\n",
      "epoch 50 training loss: 0.0023818307619711213 acc: 0.9996382054992764\n",
      "epoch 50 validation acc: 0.8798701\n",
      "saved: ECG_MLP_Pytorch_epoch50.pt\n",
      "Train Epoch: 51 [0%]\tLoss: 0.005194\n",
      "epoch 51 training loss: 0.0026670217656911436 acc: 0.9996382054992764\n",
      "epoch 51 validation acc: 0.8798701\n",
      "saved: ECG_MLP_Pytorch_epoch51.pt\n",
      "Train Epoch: 52 [0%]\tLoss: 0.001918\n",
      "epoch 52 training loss: 0.0026006273269145327 acc: 0.9992764109985528\n",
      "epoch 52 validation acc: 0.8896104\n",
      "saved: ECG_MLP_Pytorch_epoch52.pt\n",
      "Train Epoch: 53 [0%]\tLoss: 0.016032\n",
      "epoch 53 training loss: 0.0036694079224782235 acc: 0.9992764109985528\n",
      "epoch 53 validation acc: 0.8896104\n",
      "saved: ECG_MLP_Pytorch_epoch53.pt\n",
      "Train Epoch: 54 [0%]\tLoss: 0.003866\n",
      "epoch 54 training loss: 0.003747279713438316 acc: 0.9989146164978292\n",
      "epoch 54 validation acc: 0.8766234\n",
      "saved: ECG_MLP_Pytorch_epoch54.pt\n",
      "Train Epoch: 55 [0%]\tLoss: 0.001541\n",
      "epoch 55 training loss: 0.002801300201099366 acc: 0.9996382054992764\n",
      "epoch 55 validation acc: 0.8798701\n",
      "saved: ECG_MLP_Pytorch_epoch55.pt\n",
      "Train Epoch: 56 [0%]\tLoss: 0.010300\n",
      "epoch 56 training loss: 0.003706770152679052 acc: 0.9992764109985528\n",
      "epoch 56 validation acc: 0.8766234\n",
      "saved: ECG_MLP_Pytorch_epoch56.pt\n",
      "Train Epoch: 57 [0%]\tLoss: 0.003597\n",
      "epoch 57 training loss: 0.0036193512443622403 acc: 1.0\n",
      "epoch 57 validation acc: 0.8766234\n",
      "saved: ECG_MLP_Pytorch_epoch57.pt\n",
      "Train Epoch: 58 [0%]\tLoss: 0.002907\n",
      "epoch 58 training loss: 0.008805854404768483 acc: 0.9974674384949349\n",
      "epoch 58 validation acc: 0.8831169\n",
      "saved: ECG_MLP_Pytorch_epoch58.pt\n",
      "Train Epoch: 59 [0%]\tLoss: 0.004057\n",
      "epoch 59 training loss: 0.0042124385045926 acc: 0.9996382054992764\n",
      "epoch 59 validation acc: 0.8766234\n",
      "saved: ECG_MLP_Pytorch_epoch59.pt\n",
      "Train Epoch: 60 [0%]\tLoss: 0.001798\n",
      "epoch 60 training loss: 0.002948860193348744 acc: 1.0\n",
      "epoch 60 validation acc: 0.8798701\n",
      "saved: ECG_MLP_Pytorch_epoch60.pt\n",
      "Train Epoch: 61 [0%]\tLoss: 0.002207\n",
      "epoch 61 training loss: 0.0049178112478164785 acc: 0.998191027496382\n",
      "epoch 61 validation acc: 0.8766234\n",
      "saved: ECG_MLP_Pytorch_epoch61.pt\n",
      "Train Epoch: 62 [0%]\tLoss: 0.001163\n",
      "epoch 62 training loss: 0.003452349877492948 acc: 1.0\n",
      "epoch 62 validation acc: 0.8831169\n",
      "saved: ECG_MLP_Pytorch_epoch62.pt\n",
      "Train Epoch: 63 [0%]\tLoss: 0.002760\n",
      "epoch 63 training loss: 0.002142608631402254 acc: 1.0\n",
      "epoch 63 validation acc: 0.8733766\n",
      "saved: ECG_MLP_Pytorch_epoch63.pt\n",
      "Train Epoch: 64 [0%]\tLoss: 0.001304\n",
      "epoch 64 training loss: 0.0030913746661760592 acc: 0.9996382054992764\n",
      "epoch 64 validation acc: 0.8636364\n",
      "saved: ECG_MLP_Pytorch_epoch64.pt\n",
      "Train Epoch: 65 [0%]\tLoss: 0.027675\n",
      "epoch 65 training loss: 0.006173124722078104 acc: 0.9985528219971056\n",
      "epoch 65 validation acc: 0.8863636\n",
      "saved: ECG_MLP_Pytorch_epoch65.pt\n",
      "Train Epoch: 66 [0%]\tLoss: 0.001408\n",
      "epoch 66 training loss: 0.008452822274359112 acc: 0.9974674384949349\n",
      "epoch 66 validation acc: 0.85064936\n",
      "saved: ECG_MLP_Pytorch_epoch66.pt\n",
      "Train Epoch: 67 [0%]\tLoss: 0.003218\n",
      "epoch 67 training loss: 0.00612730303758078 acc: 0.9989146164978292\n",
      "epoch 67 validation acc: 0.8603896\n",
      "saved: ECG_MLP_Pytorch_epoch67.pt\n",
      "Train Epoch: 68 [0%]\tLoss: 0.004074\n",
      "epoch 68 training loss: 0.007605246617458761 acc: 0.9978292329956585\n",
      "epoch 68 validation acc: 0.8701299\n",
      "saved: ECG_MLP_Pytorch_epoch68.pt\n",
      "Train Epoch: 69 [0%]\tLoss: 0.028922\n",
      "epoch 69 training loss: 0.009969139185903425 acc: 0.9971056439942113\n",
      "epoch 69 validation acc: 0.8636364\n",
      "saved: ECG_MLP_Pytorch_epoch69.pt\n",
      "Train Epoch: 70 [0%]\tLoss: 0.029484\n",
      "epoch 70 training loss: 0.011638250413604757 acc: 0.9960202604920405\n",
      "epoch 70 validation acc: 0.8798701\n",
      "saved: ECG_MLP_Pytorch_epoch70.pt\n",
      "Train Epoch: 71 [0%]\tLoss: 0.003182\n",
      "epoch 71 training loss: 0.005492680097549138 acc: 0.998191027496382\n",
      "epoch 71 validation acc: 0.8798701\n",
      "saved: ECG_MLP_Pytorch_epoch71.pt\n",
      "Train Epoch: 72 [0%]\tLoss: 0.002372\n",
      "epoch 72 training loss: 0.0039703982289541855 acc: 0.9989146164978292\n",
      "epoch 72 validation acc: 0.8831169\n",
      "saved: ECG_MLP_Pytorch_epoch72.pt\n",
      "Train Epoch: 73 [0%]\tLoss: 0.001053\n",
      "epoch 73 training loss: 0.0021643394522305407 acc: 0.9996382054992764\n",
      "epoch 73 validation acc: 0.89285713\n",
      "saved: ECG_MLP_Pytorch_epoch73.pt\n",
      "Train Epoch: 74 [0%]\tLoss: 0.000748\n",
      "epoch 74 training loss: 0.0016412125868638132 acc: 1.0\n",
      "epoch 74 validation acc: 0.8863636\n",
      "saved: ECG_MLP_Pytorch_epoch74.pt\n",
      "Train Epoch: 75 [0%]\tLoss: 0.000989\n",
      "epoch 75 training loss: 0.0011286659864708781 acc: 1.0\n",
      "epoch 75 validation acc: 0.8831169\n",
      "saved: ECG_MLP_Pytorch_epoch75.pt\n",
      "Train Epoch: 76 [0%]\tLoss: 0.000344\n",
      "epoch 76 training loss: 0.0007318221892505376 acc: 1.0\n",
      "epoch 76 validation acc: 0.8766234\n",
      "saved: ECG_MLP_Pytorch_epoch76.pt\n",
      "Train Epoch: 77 [0%]\tLoss: 0.000428\n",
      "epoch 77 training loss: 0.0007039274761072276 acc: 1.0\n",
      "epoch 77 validation acc: 0.8798701\n",
      "saved: ECG_MLP_Pytorch_epoch77.pt\n",
      "Train Epoch: 78 [0%]\tLoss: 0.000567\n",
      "epoch 78 training loss: 0.0006762996001105586 acc: 1.0\n",
      "epoch 78 validation acc: 0.8798701\n",
      "saved: ECG_MLP_Pytorch_epoch78.pt\n",
      "Train Epoch: 79 [0%]\tLoss: 0.000429\n",
      "epoch 79 training loss: 0.0005917683540081435 acc: 1.0\n",
      "epoch 79 validation acc: 0.8766234\n",
      "saved: ECG_MLP_Pytorch_epoch79.pt\n",
      "Train Epoch: 80 [0%]\tLoss: 0.000380\n",
      "epoch 80 training loss: 0.0006131791623457419 acc: 1.0\n",
      "epoch 80 validation acc: 0.8766234\n",
      "saved: ECG_MLP_Pytorch_epoch80.pt\n",
      "Train Epoch: 81 [0%]\tLoss: 0.000570\n",
      "epoch 81 training loss: 0.0005828410503454506 acc: 1.0\n",
      "epoch 81 validation acc: 0.8798701\n",
      "saved: ECG_MLP_Pytorch_epoch81.pt\n",
      "Train Epoch: 82 [0%]\tLoss: 0.001052\n",
      "epoch 82 training loss: 0.0006682250479405576 acc: 1.0\n",
      "epoch 82 validation acc: 0.8831169\n",
      "saved: ECG_MLP_Pytorch_epoch82.pt\n",
      "Train Epoch: 83 [0%]\tLoss: 0.000359\n",
      "epoch 83 training loss: 0.0005659322017560374 acc: 1.0\n",
      "epoch 83 validation acc: 0.8831169\n",
      "saved: ECG_MLP_Pytorch_epoch83.pt\n",
      "Train Epoch: 84 [0%]\tLoss: 0.000458\n",
      "epoch 84 training loss: 0.0005271404105852442 acc: 1.0\n",
      "epoch 84 validation acc: 0.8798701\n",
      "saved: ECG_MLP_Pytorch_epoch84.pt\n",
      "Train Epoch: 85 [0%]\tLoss: 0.000613\n",
      "epoch 85 training loss: 0.0005665188522967087 acc: 1.0\n",
      "epoch 85 validation acc: 0.8798701\n",
      "saved: ECG_MLP_Pytorch_epoch85.pt\n",
      "Train Epoch: 86 [0%]\tLoss: 0.000367\n",
      "epoch 86 training loss: 0.0005512469559272921 acc: 1.0\n",
      "epoch 86 validation acc: 0.8831169\n",
      "saved: ECG_MLP_Pytorch_epoch86.pt\n",
      "Train Epoch: 87 [0%]\tLoss: 0.000331\n",
      "epoch 87 training loss: 0.0005895517943744463 acc: 1.0\n",
      "epoch 87 validation acc: 0.8798701\n",
      "saved: ECG_MLP_Pytorch_epoch87.pt\n",
      "Train Epoch: 88 [0%]\tLoss: 0.000430\n",
      "epoch 88 training loss: 0.0005179216472738931 acc: 1.0\n",
      "epoch 88 validation acc: 0.8798701\n",
      "saved: ECG_MLP_Pytorch_epoch88.pt\n",
      "Train Epoch: 89 [0%]\tLoss: 0.000400\n",
      "epoch 89 training loss: 0.0004847277186027813 acc: 1.0\n",
      "epoch 89 validation acc: 0.8831169\n",
      "saved: ECG_MLP_Pytorch_epoch89.pt\n",
      "Train Epoch: 90 [0%]\tLoss: 0.000328\n",
      "epoch 90 training loss: 0.0005008431881750849 acc: 1.0\n",
      "epoch 90 validation acc: 0.8831169\n",
      "saved: ECG_MLP_Pytorch_epoch90.pt\n",
      "Train Epoch: 91 [0%]\tLoss: 0.000507\n",
      "epoch 91 training loss: 0.0005591597450389104 acc: 1.0\n",
      "epoch 91 validation acc: 0.8798701\n",
      "saved: ECG_MLP_Pytorch_epoch91.pt\n",
      "Train Epoch: 92 [0%]\tLoss: 0.000372\n",
      "epoch 92 training loss: 0.0007440914381401275 acc: 1.0\n",
      "epoch 92 validation acc: 0.8863636\n",
      "saved: ECG_MLP_Pytorch_epoch92.pt\n",
      "Train Epoch: 93 [0%]\tLoss: 0.000691\n",
      "epoch 93 training loss: 0.0017995388585735452 acc: 0.9992764109985528\n",
      "epoch 93 validation acc: 0.8896104\n",
      "saved: ECG_MLP_Pytorch_epoch93.pt\n",
      "Train Epoch: 94 [0%]\tLoss: 0.015070\n",
      "epoch 94 training loss: 0.0022043107336768035 acc: 0.9992764109985528\n",
      "epoch 94 validation acc: 0.8896104\n",
      "saved: ECG_MLP_Pytorch_epoch94.pt\n",
      "Train Epoch: 95 [0%]\tLoss: 0.001614\n",
      "epoch 95 training loss: 0.0010898141838631338 acc: 1.0\n",
      "epoch 95 validation acc: 0.8831169\n",
      "saved: ECG_MLP_Pytorch_epoch95.pt\n",
      "Train Epoch: 96 [0%]\tLoss: 0.000976\n",
      "epoch 96 training loss: 0.0006952550915726038 acc: 1.0\n",
      "epoch 96 validation acc: 0.8798701\n",
      "saved: ECG_MLP_Pytorch_epoch96.pt\n",
      "Train Epoch: 97 [0%]\tLoss: 0.000426\n",
      "epoch 97 training loss: 0.0005639444623904473 acc: 1.0\n",
      "epoch 97 validation acc: 0.8863636\n",
      "saved: ECG_MLP_Pytorch_epoch97.pt\n",
      "Train Epoch: 98 [0%]\tLoss: 0.001524\n",
      "epoch 98 training loss: 0.0005638795740775425 acc: 1.0\n",
      "epoch 98 validation acc: 0.8831169\n",
      "saved: ECG_MLP_Pytorch_epoch98.pt\n",
      "Train Epoch: 99 [0%]\tLoss: 0.000634\n",
      "epoch 99 training loss: 0.0004905288859101182 acc: 1.0\n",
      "epoch 99 validation acc: 0.8831169\n",
      "saved: ECG_MLP_Pytorch_epoch99.pt\n"
     ]
    }
   ],
   "source": [
    "for epoch in range(epoch_save+1, 100): #change 100 to a larger number if necessary\n",
    "    #-------- training --------------------------------\n",
    "    loss_train, acc_train =train(model, device, optimizer, loader_train, epoch)    \n",
    "    loss_train_list.append(loss_train)\n",
    "    acc_train_list.append(acc_train)\n",
    "    print('epoch', epoch, 'training loss:', loss_train, 'acc:', acc_train)\n",
    "    #-------- validation --------------------------------\n",
    "    acc_val, other_val = test(model, device, loader_val)\n",
    "    acc_val_list.append(acc_val)\n",
    "    print('epoch', epoch, 'validation acc:', acc_val)\n",
    "    #--------save model-------------------------\n",
    "    result = (loss_train_list, acc_train_list, \n",
    "              acc_val_list, other_val)\n",
    "    #change the name of the file if necessary\n",
    "    save_checkpoint('ECG_MLP_Pytorch_epoch'+str(epoch)+'.pt', model, optimizer, result, epoch)\n",
    "    epoch_save=epoch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 2, figsize=(12,6))\n",
    "ax[0].set_title('loss v.s. epoch',fontsize=16)\n",
    "ax[0].plot(loss_train_list, '-b', label='training loss')\n",
    "ax[0].set_xlabel('epoch',fontsize=16)\n",
    "ax[0].legend(fontsize=16)\n",
    "ax[0].grid(True)\n",
    "ax[1].set_title('accuracy v.s. epoch',fontsize=16)\n",
    "ax[1].plot(acc_train_list, '-b', label='training accuracy')\n",
    "ax[1].plot(acc_val_list, '-g', label='validation accuracy')\n",
    "ax[1].set_xlabel('epoch',fontsize=16)\n",
    "ax[1].legend(fontsize=16)\n",
    "ax[1].grid(True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "load the best model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "73"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_epoch=np.argmax(acc_val_list)\n",
    "best_epoch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "checkpoint=torch.load('ECG_MLP_Pytorch_epoch'+str(best_epoch)+'.pt')\n",
    "model=CNN()\n",
    "model.load_state_dict(checkpoint['model_state_dict'])\n",
    "model.to(device);\n",
    "model.eval();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy (average) 0.91937584\n",
      "Sensitivity [0.85795456 0.91558444 0.92465752 0.95901638 0.95321637]\n",
      "Precision [0.86285716 0.86503065 0.95744681 0.93599999 0.9878788 ]\n",
      "Confusion \n",
      " [[151.  19.   2.   3.   1.]\n",
      " [ 12. 141.   1.   0.   0.]\n",
      " [  4.   1. 135.   5.   1.]\n",
      " [  4.   0.   1. 117.   0.]\n",
      " [  4.   2.   2.   0. 163.]]\n"
     ]
    }
   ],
   "source": [
    "acc, (confusion, sens, prec) = test(model, device, loader_test)\n",
    "print('Accuracy (average)', acc)\n",
    "print('Sensitivity', sens)\n",
    "print('Precision', prec)\n",
    "print('Confusion \\n', confusion)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.11.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}