[3.1]AI-engineering-Deep-Networks-Project.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Fashion-MNIST Project"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: torch in c:\\users\\vytask\\anaconda3\\lib\\site-packages (2.5.1)\n",
      "Requirement already satisfied: filelock in c:\\users\\vytask\\anaconda3\\lib\\site-packages (from torch) (3.13.1)\n",
      "Requirement already satisfied: typing-extensions>=4.8.0 in c:\\users\\vytask\\anaconda3\\lib\\site-packages (from torch) (4.11.0)\n",
      "Requirement already satisfied: networkx in c:\\users\\vytask\\anaconda3\\lib\\site-packages (from torch) (3.2.1)\n",
      "Requirement already satisfied: jinja2 in c:\\users\\vytask\\anaconda3\\lib\\site-packages (from torch) (3.1.4)\n",
      "Requirement already satisfied: fsspec in c:\\users\\vytask\\anaconda3\\lib\\site-packages (from torch) (2024.3.1)\n",
      "Requirement already satisfied: setuptools in c:\\users\\vytask\\anaconda3\\lib\\site-packages (from torch) (69.5.1)\n",
      "Requirement already satisfied: sympy==1.13.1 in c:\\users\\vytask\\anaconda3\\lib\\site-packages (from torch) (1.13.1)\n",
      "Requirement already satisfied: mpmath<1.4,>=1.1.0 in c:\\users\\vytask\\anaconda3\\lib\\site-packages (from sympy==1.13.1->torch) (1.3.0)\n",
      "Requirement already satisfied: MarkupSafe>=2.0 in c:\\users\\vytask\\anaconda3\\lib\\site-packages (from jinja2->torch) (2.1.3)\n",
      "Note: you may need to restart the kernel to use updated packages.\n",
      "Collecting torchvisionNote: you may need to restart the kernel to use updated packages.\n",
      "\n",
      "  Downloading torchvision-0.20.1-cp312-cp312-win_amd64.whl.metadata (6.2 kB)\n",
      "Requirement already satisfied: numpy in c:\\users\\vytask\\anaconda3\\lib\\site-packages (from torchvision) (1.26.4)\n",
      "Requirement already satisfied: torch==2.5.1 in c:\\users\\vytask\\anaconda3\\lib\\site-packages (from torchvision) (2.5.1)\n",
      "Requirement already satisfied: pillow!=8.3.*,>=5.3.0 in c:\\users\\vytask\\anaconda3\\lib\\site-packages (from torchvision) (10.3.0)\n",
      "Requirement already satisfied: filelock in c:\\users\\vytask\\anaconda3\\lib\\site-packages (from torch==2.5.1->torchvision) (3.13.1)\n",
      "Requirement already satisfied: typing-extensions>=4.8.0 in c:\\users\\vytask\\anaconda3\\lib\\site-packages (from torch==2.5.1->torchvision) (4.11.0)\n",
      "Requirement already satisfied: networkx in c:\\users\\vytask\\anaconda3\\lib\\site-packages (from torch==2.5.1->torchvision) (3.2.1)\n",
      "Requirement already satisfied: jinja2 in c:\\users\\vytask\\anaconda3\\lib\\site-packages (from torch==2.5.1->torchvision) (3.1.4)\n",
      "Requirement already satisfied: fsspec in c:\\users\\vytask\\anaconda3\\lib\\site-packages (from torch==2.5.1->torchvision) (2024.3.1)\n",
      "Requirement already satisfied: setuptools in c:\\users\\vytask\\anaconda3\\lib\\site-packages (from torch==2.5.1->torchvision) (69.5.1)\n",
      "Requirement already satisfied: sympy==1.13.1 in c:\\users\\vytask\\anaconda3\\lib\\site-packages (from torch==2.5.1->torchvision) (1.13.1)\n",
      "Requirement already satisfied: mpmath<1.4,>=1.1.0 in c:\\users\\vytask\\anaconda3\\lib\\site-packages (from sympy==1.13.1->torch==2.5.1->torchvision) (1.3.0)\n",
      "Requirement already satisfied: MarkupSafe>=2.0 in c:\\users\\vytask\\anaconda3\\lib\\site-packages (from jinja2->torch==2.5.1->torchvision) (2.1.3)\n",
      "Downloading torchvision-0.20.1-cp312-cp312-win_amd64.whl (1.6 MB)\n",
      "   ---------------------------------------- 0.0/1.6 MB ? eta -:--:--\n",
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      "   ---------------------------------------- 1.6/1.6 MB 3.1 MB/s eta 0:00:00\n",
      "Installing collected packages: torchvision\n",
      "Successfully installed torchvision-0.20.1\n",
      "Requirement already satisfied: matplotlib in c:\\users\\vytask\\anaconda3\\lib\\site-packages (3.9.2)\n",
      "Requirement already satisfied: contourpy>=1.0.1 in c:\\users\\vytask\\anaconda3\\lib\\site-packages (from matplotlib) (1.2.0)\n",
      "Requirement already satisfied: cycler>=0.10 in c:\\users\\vytask\\anaconda3\\lib\\site-packages (from matplotlib) (0.11.0)\n",
      "Requirement already satisfied: fonttools>=4.22.0 in c:\\users\\vytask\\anaconda3\\lib\\site-packages (from matplotlib) (4.51.0)\n",
      "Requirement already satisfied: kiwisolver>=1.3.1 in c:\\users\\vytask\\anaconda3\\lib\\site-packages (from matplotlib) (1.4.4)\n",
      "Requirement already satisfied: numpy>=1.23 in c:\\users\\vytask\\anaconda3\\lib\\site-packages (from matplotlib) (1.26.4)\n",
      "Requirement already satisfied: packaging>=20.0 in c:\\users\\vytask\\anaconda3\\lib\\site-packages (from matplotlib) (23.2)\n",
      "Requirement already satisfied: pillow>=8 in c:\\users\\vytask\\anaconda3\\lib\\site-packages (from matplotlib) (10.3.0)\n",
      "Requirement already satisfied: pyparsing>=2.3.1 in c:\\users\\vytask\\anaconda3\\lib\\site-packages (from matplotlib) (3.0.9)\n",
      "Requirement already satisfied: python-dateutil>=2.7 in c:\\users\\vytask\\anaconda3\\lib\\site-packages (from matplotlib) (2.9.0.post0)\n",
      "Requirement already satisfied: six>=1.5 in c:\\users\\vytask\\anaconda3\\lib\\site-packages (from python-dateutil>=2.7->matplotlib) (1.16.0)\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "%pip install torch\n",
    "%pip install torchvision\n",
    "%pip install matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# PyTorch Modules you need for this lab\n",
    "\n",
    "from torch.utils.data import Dataset, DataLoader\n",
    "\n",
    "from torchvision import transforms\n",
    "import torch \n",
    "import torch.nn as nn\n",
    "import torchvision.transforms as transforms\n",
    "import torchvision.datasets as dsets\n",
    "torch.manual_seed(0)\n",
    "\n",
    "# Other non-PyTorch Modules\n",
    "from matplotlib.pyplot import imshow\n",
    "import matplotlib.pylab as plt\n",
    "\n",
    "from PIL import Image"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Helper functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def show_data(data_sample):\n",
    "    plt.imshow(data_sample[0].numpy().reshape(IMAGE_SIZE, IMAGE_SIZE), cmap='gray')\n",
    "    plt.title('y = '+ str(data_sample[1]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Questions 1: Create a Dataset Class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "IMAGE_SIZE = 16\n",
    "\n",
    "transforms.Resize((IMAGE_SIZE, IMAGE_SIZE)),\n",
    "transforms.ToTensor()\n",
    "composed = transforms.Compose([transforms.Resize((IMAGE_SIZE, IMAGE_SIZE)), transforms.ToTensor()])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-images-idx3-ubyte.gz\n",
      "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-images-idx3-ubyte.gz to .fashion/data\\FashionMNIST\\raw\\train-images-idx3-ubyte.gz\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 26.4M/26.4M [00:06<00:00, 4.07MB/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting .fashion/data\\FashionMNIST\\raw\\train-images-idx3-ubyte.gz to .fashion/data\\FashionMNIST\\raw\n",
      "\n",
      "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-labels-idx1-ubyte.gz\n",
      "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-labels-idx1-ubyte.gz to .fashion/data\\FashionMNIST\\raw\\train-labels-idx1-ubyte.gz\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 29.5k/29.5k [00:00<00:00, 445kB/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting .fashion/data\\FashionMNIST\\raw\\train-labels-idx1-ubyte.gz to .fashion/data\\FashionMNIST\\raw\n",
      "\n",
      "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-images-idx3-ubyte.gz\n",
      "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-images-idx3-ubyte.gz to .fashion/data\\FashionMNIST\\raw\\t10k-images-idx3-ubyte.gz\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 4.42M/4.42M [00:01<00:00, 3.25MB/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting .fashion/data\\FashionMNIST\\raw\\t10k-images-idx3-ubyte.gz to .fashion/data\\FashionMNIST\\raw\n",
      "\n",
      "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-labels-idx1-ubyte.gz\n",
      "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-labels-idx1-ubyte.gz to .fashion/data\\FashionMNIST\\raw\\t10k-labels-idx1-ubyte.gz\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 5.15k/5.15k [00:00<?, ?B/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting .fashion/data\\FashionMNIST\\raw\\t10k-labels-idx1-ubyte.gz to .fashion/data\\FashionMNIST\\raw\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "dataset_train = dsets.FashionMNIST(root= '.fashion/data', train=True, transform=composed,  download=True)\n",
    "dataset_val = dsets.FashionMNIST(root= '.fashion/data', train=False, transform=composed,  download=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for n,data_sample in enumerate(dataset_val):\n",
    "\n",
    "    show_data(data_sample)\n",
    "    plt.show()\n",
    "    if n==2:\n",
    "        break"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Questions 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "class CNN_batch(nn.Module):\n",
    "    \n",
    "    # Contructor\n",
    "    def __init__(self, out_1=16, out_2=32, number_of_classes=10):\n",
    "        super(CNN_batch, self).__init__()\n",
    "        self.cnn1 = nn.Conv2d(in_channels=1, out_channels=out_1, kernel_size=5, padding=2)\n",
    "        self.conv1_bn = nn.BatchNorm2d(out_1)\n",
    "\n",
    "        self.maxpool1=nn.MaxPool2d(kernel_size=2)\n",
    "        \n",
    "        self.cnn2 = nn.Conv2d(in_channels=out_1, out_channels=out_2, kernel_size=5, stride=1, padding=2)\n",
    "        self.conv2_bn = nn.BatchNorm2d(out_2)\n",
    "\n",
    "        self.maxpool2=nn.MaxPool2d(kernel_size=2)\n",
    "        self.fc1 = nn.Linear(out_2 * 4 * 4, number_of_classes)\n",
    "        self.bn_fc1 = nn.BatchNorm1d(10)\n",
    "    \n",
    "    # Prediction\n",
    "    def forward(self, x):\n",
    "        x = self.cnn1(x)\n",
    "        x=self.conv1_bn(x)\n",
    "        x = torch.relu(x)\n",
    "        x = self.maxpool1(x)\n",
    "        x = self.cnn2(x)\n",
    "        x=self.conv2_bn(x)\n",
    "        x = torch.relu(x)\n",
    "        x = self.maxpool2(x)\n",
    "        x = x.view(x.size(0), -1)\n",
    "        x = self.fc1(x)\n",
    "        x=self.bn_fc1(x)\n",
    "        return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_loader = torch.utils.data.DataLoader(dataset=dataset_train, batch_size=100)\n",
    "test_loader = torch.utils.data.DataLoader(dataset=dataset_val, batch_size=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = CNN_batch(out_1=16, out_2=32, number_of_classes=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "criterion = nn.CrossEntropyLoss()\n",
    "optimizer = torch.optim.SGD(model.parameters(), lr=0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "import time\n",
    "start_time = time.time()\n",
    "\n",
    "cost_list=[]\n",
    "accuracy_list=[]\n",
    "N_test=len(dataset_val)\n",
    "n_epochs=10\n",
    "for epoch in range(n_epochs):\n",
    "    cost=0\n",
    "    model.train()\n",
    "    for x, y in train_loader:\n",
    "        optimizer.zero_grad()\n",
    "        z = model(x)\n",
    "        loss = criterion(z, y)\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "        cost+=loss.item()\n",
    "    correct=0\n",
    "    #perform a prediction on the validation  data \n",
    "    model.eval()\n",
    "    for x_test, y_test in test_loader:\n",
    "        z = model(x_test)\n",
    "        _, yhat = torch.max(z.data, 1)\n",
    "        correct += (yhat == y_test).sum().item()\n",
    "    accuracy = correct / N_test\n",
    "    accuracy_list.append(accuracy)\n",
    "    cost_list.append(cost)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax1 = plt.subplots()\n",
    "color = 'tab:red'\n",
    "ax1.plot(cost_list, color=color)\n",
    "ax1.set_xlabel('epoch', color=color)\n",
    "ax1.set_ylabel('Cost', color=color)\n",
    "ax1.tick_params(axis='y', color=color)\n",
    "    \n",
    "ax2 = ax1.twinx()  \n",
    "color = 'tab:blue'\n",
    "ax2.set_ylabel('accuracy', color=color) \n",
    "ax2.set_xlabel('epoch', color=color)\n",
    "ax2.plot( accuracy_list, color=color)\n",
    "ax2.tick_params(axis='y', color=color)\n",
    "fig.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "base",
   "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": "3.12.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}