Solution_Exercise_6.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "WodkhD5cjIdk"
   },
   "source": [
    "<h1 style=\"color:rgb(0,120,170)\">OEAW AI Summer School 2021 - Day 1: </h1>\n",
    "<h2 style=\"color:rgb(0,120,170)\">Introduction to Deep Learning - My first Neural Network</h2>\n",
    "\n",
    "This notebook serves as a sanity check whether everything works on your computer and is also the basis and starting point for the exercises of today. Press Crtl/cmd + Enter to run a cell."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "executionInfo": {
     "elapsed": 5079,
     "status": "ok",
     "timestamp": 1631536622868,
     "user": {
      "displayName": "daniel haider",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s64",
      "userId": "05844593966666692581"
     },
     "user_tz": -120
    },
    "id": "qgtqg1oJjIdp"
   },
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "from torch.autograd import Variable\n",
    "import torch.optim as optim\n",
    "from torchvision import datasets, transforms\n",
    "from types import SimpleNamespace\n",
    "import matplotlib.pyplot as plt\n",
    "import os\n",
    "import numpy as np  # added in exercise 1\n",
    "from scipy.signal import savgol_filter  # added in exercise 1\n",
    "%config InlineBackend.figure_formats = ['svg']  # added in exercise 1\n",
    "from sklearn.metrics import confusion_matrix  # added in exercise 2\n",
    "import seaborn as sns  # added in exercise 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "LWJuryq3k2fc"
   },
   "source": [
    "# Hyperparameters for the Training Procedure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "executionInfo": {
     "elapsed": 210,
     "status": "ok",
     "timestamp": 1631536696454,
     "user": {
      "displayName": "daniel haider",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s64",
      "userId": "05844593966666692581"
     },
     "user_tz": -120
    },
    "id": "uYPxCvQmjIdr"
   },
   "outputs": [],
   "source": [
    "args = SimpleNamespace(batch_size=8, test_batch_size=1000, epochs=1,\n",
    "                       lr=0.005, momentum=0.5, seed=1, log_interval=100)  # values changed in exercise 3\n",
    "torch.manual_seed(args.seed)\n",
    "use_cuda = torch.cuda.is_available()\n",
    "device = torch.device('cuda' if use_cuda else 'cpu')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "5sBMjO9qk9yw"
   },
   "source": [
    "# Data Loader"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 471,
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    },
    "executionInfo": {
     "elapsed": 5820,
     "status": "ok",
     "timestamp": 1631536628679,
     "user": {
      "displayName": "daniel haider",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s64",
      "userId": "05844593966666692581"
     },
     "user_tz": -120
    },
    "id": "yEeNcvEgjIdr",
    "outputId": "bcae628f-ec8d-44df-d801-0b24690d6e8f"
   },
   "outputs": [],
   "source": [
    "# 0.1307 and 0.3081 are the mean and the std computed on the training set\n",
    "kwargs = {'num_workers': 1, 'pin_memory': True} if use_cuda else {}\n",
    "train_loader = torch.utils.data.DataLoader(\n",
    "    datasets.FashionMNIST(os.path.join('.','..','data'), train=True, download=True, # dataset name changed in exercise 5\n",
    "                   transform=transforms.Compose([\n",
    "                       transforms.ToTensor(),\n",
    "                       transforms.Normalize((0.1307,), (0.3081,))\n",
    "                   ])),\n",
    "    batch_size=args.batch_size, shuffle=True, **kwargs)\n",
    "test_loader = torch.utils.data.DataLoader(\n",
    "    datasets.FashionMNIST(os.path.join('.','..','data'), train=False, transform=transforms.Compose([ # dataset name changed in exercise 5\n",
    "                       transforms.ToTensor(),\n",
    "                       transforms.Normalize((0.1307,), (0.3081,))\n",
    "                   ])),\n",
    "        batch_size=args.test_batch_size, shuffle=True, **kwargs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "6Hb4as4klGrF"
   },
   "source": [
    "# How does the Data look like?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 309
    },
    "executionInfo": {
     "elapsed": 1212,
     "status": "ok",
     "timestamp": 1631536629877,
     "user": {
      "displayName": "daniel haider",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s64",
      "userId": "05844593966666692581"
     },
     "user_tz": -120
    },
    "id": "AvvHQ-lpjIds",
    "outputId": "34ad418c-1fb6-4e7d-bf1e-b6410158c8a9"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 60000 samples for training and 10000 samples for testing.\n",
      "The format of the data is torch.Size([60000, 28, 28])\n",
      "Input dimension as vector is 784.\n"
     ]
    },
    {
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      "text/plain": [
       "<Figure size 1080x216 with 20 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print('There are', train_loader.dataset.targets.shape[0], 'samples for training and', test_loader.dataset.targets.shape[0], 'samples for testing.')\n",
    "print('The format of the data is', train_loader.dataset.data.shape)\n",
    "input_dim = train_loader.dataset.data.shape[1]*train_loader.dataset.data.shape[2]\n",
    "print(\"Input dimension as vector is {}.\".format(input_dim))\n",
    "output_dim = 10\n",
    "\n",
    "# Plot example images\n",
    "fig=plt.figure(figsize=(15,3))\n",
    "for image in range(20):\n",
    "    show_img = train_loader.dataset.data[image].numpy().reshape(28, 28)\n",
    "    fig.add_subplot(2,10,image+1)\n",
    "    plt.xticks([]),plt.yticks([])\n",
    "    plt.imshow(show_img, cmap='gray')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "B10wrG9WjIdt"
   },
   "source": [
    "# A first architecture\n",
    "This is a basic neural network with one input layer, one hidden layer and one output layer. You can play around with number of layers, number of units, weight initialization, dropout, batch normalization, activation functions..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "executionInfo": {
     "elapsed": 30,
     "status": "ok",
     "timestamp": 1631536629878,
     "user": {
      "displayName": "daniel haider",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s64",
      "userId": "05844593966666692581"
     },
     "user_tz": -120
    },
    "id": "0uSwnJcIjIdu"
   },
   "outputs": [],
   "source": [
    "class NeuralNet(nn.Module):\n",
    "    def __init__(self):\n",
    "        super(NeuralNet, self).__init__()\n",
    "        # input layer\n",
    "        self.linear1 = nn.Linear(784, 1024)\n",
    "        #hidden layers\n",
    "        self.linear2 = nn.Linear(1024, 2048)\n",
    "        self.linear3 = nn.Linear(2048, 1024)\n",
    "        self.linear4 = nn.Linear(1024, 512)\n",
    "        #output layer\n",
    "        self.linear5 = nn.Linear(512, 10)\n",
    "        \n",
    "        self.dropout1 = nn.Dropout(p=0.2)  # values changed in exercise 3\n",
    "        self.dropout2 = nn.Dropout(p=0.1)\n",
    "        self.dropout3 = nn.Dropout(p=0.1)\n",
    "        self.dropout4 = nn.Dropout(p=0.1)\n",
    "\n",
    "        self.bn1 = nn.BatchNorm1d(1024)\n",
    "        self.bn2 = nn.BatchNorm1d(2048)\n",
    "        self.bn3 = nn.BatchNorm1d(1024)\n",
    "\n",
    "    def forward(self, x):\n",
    "        x = self.linear1(x)\n",
    "        x = self.dropout1(x)\n",
    "        x = F.relu(x)\n",
    "        #x = self.bn1(x)\n",
    "        \n",
    "        x = self.linear2(x)\n",
    "        x = self.dropout2(x)\n",
    "        x = F.relu(x)\n",
    "        #x = self.bn2(x)\n",
    "        \n",
    "        x = self.linear3(x)\n",
    "        x = self.dropout3(x)\n",
    "        x = F.relu(x)\n",
    "        #x = self.bn3(x)\n",
    "        \n",
    "        x = self.linear4(x)\n",
    "        x = self.dropout4(x)\n",
    "        x = F.selu(x)\n",
    "        \n",
    "        x = self.linear5(x)\n",
    "        x = F.log_softmax(x, dim=1)\n",
    "        return x\n",
    "\n",
    "\n",
    "# CNN architecture (two conv layers followed by three fully connected layers)\n",
    "class ConvNet(nn.Module):\n",
    "    def __init__(self):\n",
    "        super(ConvNet, self).__init__()\n",
    "        self.conv1 = nn.Conv2d(1, 32, 3)\n",
    "        self.conv2 = nn.Conv2d(32, 64, 3)\n",
    "        #self.conv3 = nn.Conv2d(96, 80, 3)\n",
    "        #self.conv4 = nn.Conv2d(80, 96, 3)\n",
    "        self.fc1 = nn.Linear(64*5*5, 1024)\n",
    "        self.fc2 = nn.Linear(1024, 512)\n",
    "        self.fc3 = nn.Linear(512, 10)\n",
    "\n",
    "        self.dropout1 = nn.Dropout(p=0.2)\n",
    "        self.dropout2 = nn.Dropout2d(0.1)\n",
    "\n",
    "    def forward(self, x):\n",
    "        x = F.relu(self.conv1(x))\n",
    "        x = self.dropout2(x)\n",
    "        x = F.max_pool2d(x, 2, 2)\n",
    "\n",
    "        x = F.relu(self.conv2(x))\n",
    "        x = self.dropout2(x)\n",
    "        x = F.max_pool2d(x, 2, 2)\n",
    "\n",
    "        #x = F.relu(self.conv3(x))\n",
    "        #x = self.dropout2(x)\n",
    "\n",
    "        #x = F.relu(self.conv4(x))\n",
    "        #x = self.dropoutc(x)\n",
    "\n",
    "        #print(x.shape)\n",
    "\n",
    "        x = x.view(-1, 64*5*5)\n",
    "        x = F.relu(self.fc1(x))\n",
    "        x = self.dropout1(x)\n",
    "        x = F.relu(self.fc2(x))\n",
    "        x = F.relu(self.fc3(x))\n",
    "        x = F.log_softmax(x, dim=1)\n",
    "        return x"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "gcbJSojjoV0H"
   },
   "source": [
    "# This function trains the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "executionInfo": {
     "elapsed": 25,
     "status": "ok",
     "timestamp": 1631536629879,
     "user": {
      "displayName": "daniel haider",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s64",
      "userId": "05844593966666692581"
     },
     "user_tz": -120
    },
    "id": "yKYRHYbajIdv"
   },
   "outputs": [],
   "source": [
    "def train(args, model, device, train_loader, optimizer, epoch, \n",
    "          loss_curve,  # added in exercise 1\n",
    "          ):\n",
    "    model.train()\n",
    "    for batch_idx, (data, target) in enumerate(train_loader):\n",
    "        #data = Variable(data.view(-1, input_dim))    #needs to be removed in exercise 6\n",
    "        data, target = data.to(device), target.to(device)\n",
    "        optimizer.zero_grad()\n",
    "        output = model(data)\n",
    "        loss = F.nll_loss(output, target)\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "        if batch_idx % args.log_interval == 0:\n",
    "            print('Train Epoch: {} [{}/{} ({:.0f}%)]\\tLoss: {:.6f}'.format(\n",
    "                epoch, batch_idx * len(data), len(train_loader.dataset),\n",
    "                100. * batch_idx / len(train_loader), loss.item()))\n",
    "            loss_curve.append(loss.item())  # added in exercise 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "wpjygw_poj7M"
   },
   "source": [
    "# This function evaluates the model on the test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "executionInfo": {
     "elapsed": 5,
     "status": "ok",
     "timestamp": 1631536631484,
     "user": {
      "displayName": "daniel haider",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s64",
      "userId": "05844593966666692581"
     },
     "user_tz": -120
    },
    "id": "McBMI3RbjIdv"
   },
   "outputs": [],
   "source": [
    "def test(args, model, device, test_loader, confusion_matrix):\n",
    "    model.eval()\n",
    "    test_loss = 0\n",
    "    correct = 0\n",
    "    with torch.no_grad():\n",
    "        for data, target in test_loader:\n",
    "            #data = Variable(data.view(-1, input_dim))    #needs to be removed in exercise 6\n",
    "            data, target = data.to(device), target.to(device)\n",
    "            output = model(data)\n",
    "            test_loss += F.nll_loss(output, target, reduction='sum').item() # sum up batch loss\n",
    "            pred = output.max(1, keepdim=True)[1] # get the index of the max log-probability\n",
    "            correct += pred.eq(target.view_as(pred)).sum().item()\n",
    "            for i, t in enumerate(target):  # added in exercise 2\n",
    "                confusion_matrix[t.item(), pred[i].item()] += 1\n",
    "    test_loss /= len(test_loader.dataset)\n",
    "\n",
    "    print('\\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.2f}%)\\n'.format(\n",
    "        test_loss, correct, len(test_loader.dataset),\n",
    "        100. * correct / len(test_loader.dataset)))\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "OWGiu9Oc2H4q"
   },
   "source": [
    "# This cell calls the model, starts the training and evaluates it on the test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 65955,
     "status": "ok",
     "timestamp": 1631536768509,
     "user": {
      "displayName": "daniel haider",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s64",
      "userId": "05844593966666692581"
     },
     "user_tz": -120
    },
    "id": "qjBotpPVjIdw",
    "outputId": "c1b23980-acfd-4d40-c78e-31d9b20b155a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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      "Train Epoch: 1 [0/60000 (0%)]\tLoss: 2.308565\n",
      "Train Epoch: 1 [800/60000 (1%)]\tLoss: 2.198024\n",
      "Train Epoch: 1 [1600/60000 (3%)]\tLoss: 1.795030\n",
      "Train Epoch: 1 [2400/60000 (4%)]\tLoss: 1.876771\n",
      "Train Epoch: 1 [3200/60000 (5%)]\tLoss: 1.343911\n",
      "Train Epoch: 1 [4000/60000 (7%)]\tLoss: 2.014274\n",
      "Train Epoch: 1 [4800/60000 (8%)]\tLoss: 0.730972\n",
      "Train Epoch: 1 [5600/60000 (9%)]\tLoss: 1.492775\n",
      "Train Epoch: 1 [6400/60000 (11%)]\tLoss: 1.201110\n",
      "Train Epoch: 1 [7200/60000 (12%)]\tLoss: 1.379237\n",
      "Train Epoch: 1 [8000/60000 (13%)]\tLoss: 1.857818\n",
      "Train Epoch: 1 [8800/60000 (15%)]\tLoss: 1.485381\n",
      "Train Epoch: 1 [9600/60000 (16%)]\tLoss: 1.537698\n",
      "Train Epoch: 1 [10400/60000 (17%)]\tLoss: 1.133993\n",
      "Train Epoch: 1 [11200/60000 (19%)]\tLoss: 1.038894\n",
      "Train Epoch: 1 [12000/60000 (20%)]\tLoss: 1.620812\n",
      "Train Epoch: 1 [12800/60000 (21%)]\tLoss: 1.465610\n",
      "Train Epoch: 1 [13600/60000 (23%)]\tLoss: 1.166596\n",
      "Train Epoch: 1 [14400/60000 (24%)]\tLoss: 0.506536\n",
      "Train Epoch: 1 [15200/60000 (25%)]\tLoss: 1.173354\n",
      "Train Epoch: 1 [16000/60000 (27%)]\tLoss: 0.659158\n",
      "Train Epoch: 1 [16800/60000 (28%)]\tLoss: 0.664053\n",
      "Train Epoch: 1 [17600/60000 (29%)]\tLoss: 0.921134\n",
      "Train Epoch: 1 [18400/60000 (31%)]\tLoss: 0.868578\n",
      "Train Epoch: 1 [19200/60000 (32%)]\tLoss: 1.312698\n",
      "Train Epoch: 1 [20000/60000 (33%)]\tLoss: 0.333078\n",
      "Train Epoch: 1 [20800/60000 (35%)]\tLoss: 0.738072\n",
      "Train Epoch: 1 [21600/60000 (36%)]\tLoss: 1.401177\n",
      "Train Epoch: 1 [22400/60000 (37%)]\tLoss: 0.730977\n",
      "Train Epoch: 1 [23200/60000 (39%)]\tLoss: 1.535098\n",
      "Train Epoch: 1 [24000/60000 (40%)]\tLoss: 0.928841\n",
      "Train Epoch: 1 [24800/60000 (41%)]\tLoss: 0.229678\n",
      "Train Epoch: 1 [25600/60000 (43%)]\tLoss: 0.723089\n",
      "Train Epoch: 1 [26400/60000 (44%)]\tLoss: 0.923622\n",
      "Train Epoch: 1 [27200/60000 (45%)]\tLoss: 0.799281\n",
      "Train Epoch: 1 [28000/60000 (47%)]\tLoss: 0.600893\n",
      "Train Epoch: 1 [28800/60000 (48%)]\tLoss: 0.802399\n",
      "Train Epoch: 1 [29600/60000 (49%)]\tLoss: 1.284808\n",
      "Train Epoch: 1 [30400/60000 (51%)]\tLoss: 0.393505\n",
      "Train Epoch: 1 [31200/60000 (52%)]\tLoss: 0.545685\n",
      "Train Epoch: 1 [32000/60000 (53%)]\tLoss: 0.747871\n",
      "Train Epoch: 1 [32800/60000 (55%)]\tLoss: 0.826521\n",
      "Train Epoch: 1 [33600/60000 (56%)]\tLoss: 0.231461\n",
      "Train Epoch: 1 [34400/60000 (57%)]\tLoss: 0.454912\n",
      "Train Epoch: 1 [35200/60000 (59%)]\tLoss: 0.277152\n",
      "Train Epoch: 1 [36000/60000 (60%)]\tLoss: 0.849305\n",
      "Train Epoch: 1 [36800/60000 (61%)]\tLoss: 0.340363\n",
      "Train Epoch: 1 [37600/60000 (63%)]\tLoss: 0.950433\n",
      "Train Epoch: 1 [38400/60000 (64%)]\tLoss: 0.438013\n",
      "Train Epoch: 1 [39200/60000 (65%)]\tLoss: 0.875768\n",
      "Train Epoch: 1 [40000/60000 (67%)]\tLoss: 0.057042\n",
      "Train Epoch: 1 [40800/60000 (68%)]\tLoss: 0.602375\n",
      "Train Epoch: 1 [41600/60000 (69%)]\tLoss: 0.459175\n",
      "Train Epoch: 1 [42400/60000 (71%)]\tLoss: 0.769782\n",
      "Train Epoch: 1 [43200/60000 (72%)]\tLoss: 1.113883\n",
      "Train Epoch: 1 [44000/60000 (73%)]\tLoss: 1.588416\n",
      "Train Epoch: 1 [44800/60000 (75%)]\tLoss: 0.503614\n",
      "Train Epoch: 1 [45600/60000 (76%)]\tLoss: 0.364063\n",
      "Train Epoch: 1 [46400/60000 (77%)]\tLoss: 0.681594\n",
      "Train Epoch: 1 [47200/60000 (79%)]\tLoss: 0.474443\n",
      "Train Epoch: 1 [48000/60000 (80%)]\tLoss: 0.954999\n",
      "Train Epoch: 1 [48800/60000 (81%)]\tLoss: 0.130877\n",
      "Train Epoch: 1 [49600/60000 (83%)]\tLoss: 1.195090\n",
      "Train Epoch: 1 [50400/60000 (84%)]\tLoss: 1.257476\n",
      "Train Epoch: 1 [51200/60000 (85%)]\tLoss: 1.030967\n",
      "Train Epoch: 1 [52000/60000 (87%)]\tLoss: 1.116761\n",
      "Train Epoch: 1 [52800/60000 (88%)]\tLoss: 0.551056\n",
      "Train Epoch: 1 [53600/60000 (89%)]\tLoss: 0.752713\n",
      "Train Epoch: 1 [54400/60000 (91%)]\tLoss: 0.915094\n",
      "Train Epoch: 1 [55200/60000 (92%)]\tLoss: 0.831834\n",
      "Train Epoch: 1 [56000/60000 (93%)]\tLoss: 0.351298\n",
      "Train Epoch: 1 [56800/60000 (95%)]\tLoss: 0.675641\n",
      "Train Epoch: 1 [57600/60000 (96%)]\tLoss: 1.191231\n",
      "Train Epoch: 1 [58400/60000 (97%)]\tLoss: 0.729614\n",
      "Train Epoch: 1 [59200/60000 (99%)]\tLoss: 0.270330\n",
      "\n",
      "Test set: Average loss: 0.7341, Accuracy: 7257/10000 (72.57%)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Main\n",
    "model = ConvNet().to(device) # model name changed in exercise 6\n",
    "optimizer = optim.SGD(model.parameters(), lr=args.lr, \n",
    "                      momentum=args.momentum, weight_decay=0.0001)\n",
    "\n",
    "loss_curve = []  # added in exercise 1\n",
    "confusion_matrix = np.zeros([10,10], int)  # added in exercise 2\n",
    "\n",
    "for epoch in range(1, args.epochs + 1):\n",
    "    train(args, model, device, train_loader, optimizer, epoch, loss_curve)  # loss_curve added in exercise 1\n",
    "    test(args, model, device, test_loader, confusion_matrix)  # confusion_matrix added in exercise 2"
   ]
  },
  {
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   "execution_count": null,
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    },
    "id": "GDwdbORGA5Mm",
    "outputId": "24f00164-58b4-4985-8dfd-07227bcd8bc6"
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   "outputs": [
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   ],
   "source": [
    "# this cell was added in exercise 1\n",
    "\n",
    "loss_curve_avg = savgol_filter(loss_curve, 151, 3) \n",
    "\n",
    "\n",
    "plt.plot(args.log_interval*np.arange(len(loss_curve)),loss_curve, label='Original', linewidth=0.5, alpha=1)\n",
    "plt.plot(args.log_interval*np.arange(len(loss_curve)),loss_curve_avg, label='Smoothed', linewidth=2)\n",
    "plt.xlabel('Minibatch Iteration')\n",
    "plt.ylabel('Training Loss')\n",
    "plt.title('Loss Curve')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
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     "base_uri": "https://localhost:8080/",
     "height": 371
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    "executionInfo": {
     "elapsed": 988,
     "status": "ok",
     "timestamp": 1631536777267,
     "user": {
      "displayName": "daniel haider",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s64",
      "userId": "05844593966666692581"
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     "user_tz": -120
    },
    "id": "T8x7UIajLJ56",
    "outputId": "51a2740d-79be-404c-e607-73f42acc9a06"
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   "outputs": [
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      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
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     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# this cell was added in exercise 2\n",
    "\n",
    "ax = sns.heatmap(confusion_matrix, annot=True, fmt='d', linewidths=.5)\n",
    "plt.xlabel(\"Label\") \n",
    "plt.ylabel(\"Prediction\") \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 25638,
     "status": "ok",
     "timestamp": 1630922214281,
     "user": {
      "displayName": "Felix Wagner",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GinKDf3B_--mFWytvwzeMaov5JcaiaV2Z68SfXW2g=s64",
      "userId": "11836224510007056212"
     },
     "user_tz": -120
    },
    "id": "HhUguPEVap5_",
    "outputId": "8e7ffe83-65c9-4ba3-bb6e-6cdde029bcb5"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mounted at /content/drive\n"
     ]
    }
   ],
   "source": [
    "# this cell was added in exercise 4\n",
    "\n",
    "from google.colab import drive\n",
    "drive.mount('/content/drive')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 1803,
     "status": "ok",
     "timestamp": 1630922222382,
     "user": {
      "displayName": "Felix Wagner",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GinKDf3B_--mFWytvwzeMaov5JcaiaV2Z68SfXW2g=s64",
      "userId": "11836224510007056212"
     },
     "user_tz": -120
    },
    "id": "9KAjMjQRZjRv",
    "outputId": "ecb523ea-a5bf-467f-a972-0816f4df48ee"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<All keys matched successfully>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# this cell was added in exercise 4\n",
    "# the path will be different for your drive!\n",
    "\n",
    "PATH = '/content/drive/MyDrive/work/hephy/oeawai21/exercises/solutions/trained_cnn.pt'\n",
    "\n",
    "torch.save(model.state_dict(), PATH)\n",
    "\n",
    "model = ConvNet()\n",
    "model.load_state_dict(torch.load(PATH))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 194,
     "status": "ok",
     "timestamp": 1630922224931,
     "user": {
      "displayName": "Felix Wagner",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GinKDf3B_--mFWytvwzeMaov5JcaiaV2Z68SfXW2g=s64",
      "userId": "11836224510007056212"
     },
     "user_tz": -120
    },
    "id": "TzcVxDwZaGYZ",
    "outputId": "d774bcec-5b77-43db-80ae-97d9239868aa"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ConvNet(\n",
      "  (conv1): Conv2d(1, 32, kernel_size=(3, 3), stride=(1, 1))\n",
      "  (conv2): Conv2d(32, 64, kernel_size=(3, 3), stride=(1, 1))\n",
      "  (fc1): Linear(in_features=1600, out_features=1024, bias=True)\n",
      "  (fc2): Linear(in_features=1024, out_features=512, bias=True)\n",
      "  (fc3): Linear(in_features=512, out_features=10, bias=True)\n",
      "  (dropout1): Dropout(p=0.2, inplace=False)\n",
      "  (dropout2): Dropout2d(p=0.1, inplace=False)\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "# this cell was added in exercise 4\n",
    "\n",
    "print(model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "3DRYDNOrH588"
   },
   "source": [
    "Done."
   ]
  }
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