week5.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Intro Neural Networks\n",
    "\n",
    "#### 3Blue1Brown Videos\n",
    "\n",
    "[What are Neural Nets](https://www.youtube.com/watch?v=aircAruvnKk)\n",
    "\n",
    "[How Neural Nets Learn](https://www.youtube.com/watch?v=IHZwWFHWa-w)\n",
    "\n",
    "[What is Backprop](https://www.youtube.com/watch?v=Ilg3gGewQ5U)\n",
    "\n",
    "[Math behind Backprop](https://www.youtube.com/watch?v=tIeHLnjs5U8)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Intro Project"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[project based off](https://towardsdatascience.com/handwritten-digit-mnist-pytorch-977b5338e627)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.10/site-packages/tqdm/auto.py:22: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import torch\n",
    "import torchvision\n",
    "import matplotlib.pyplot as plt\n",
    "from time import time\n",
    "from torchvision import datasets, transforms\n",
    "from torch import nn, optim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz\n",
      "Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz to Training_Data/MNIST/raw/train-images-idx3-ubyte.gz\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 12%|█▏        | 1146880/9912422 [01:20<27:03, 5400.81it/s] "
     ]
    }
   ],
   "source": [
    "transform = transforms.Compose([transforms.ToTensor(),\n",
    "                              transforms.Normalize((0.5,), (0.5,)),\n",
    "                              ])\n",
    "\n",
    "\n",
    "trainset = datasets.MNIST('Training_Data', download=True, train=True, transform=transform)\n",
    "valset = datasets.MNIST('Testing_Data', download=True, train=False, transform=transform)\n",
    "\n",
    "trainloader = torch.utils.data.DataLoader(trainset, batch_size=64, shuffle=True)\n",
    "valloader = torch.utils.data.DataLoader(valset, batch_size=64, shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([64, 1, 28, 28])\n",
      "torch.Size([64])\n"
     ]
    }
   ],
   "source": [
    "dataiter = iter(trainloader)\n",
    "images, labels = next(dataiter)\n",
    "\n",
    "print(images.shape)\n",
    "print(labels.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor(9)"
      ]
     },
     "execution_count": 14,
     "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.imshow(images[1].numpy().squeeze(), cmap='gray_r');\n",
    "labels[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([9, 1, 9, 6, 8, 8, 5, 2, 4, 4, 7, 7, 1, 4, 7, 2, 3, 3, 6, 0, 6, 7, 5, 9,\n",
      "        7, 3, 7, 2, 9, 8, 8, 7, 1, 3, 3, 0, 8, 1, 0, 1, 6, 1, 5, 6, 8, 1, 5, 8,\n",
      "        2, 8, 7, 8, 3, 5, 9, 6, 1, 5, 5, 1, 8, 2, 5, 5])\n"
     ]
    }
   ],
   "source": [
    "print(labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 64 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "figure = plt.figure()\n",
    "num_of_images = 64\n",
    "for index in range(len(images)):\n",
    "    plt.subplot(8, 8, index + 1)\n",
    "    plt.axis('off')\n",
    "    plt.imshow(images[index].numpy().squeeze(), cmap='gray_r')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "\n",
    "\n",
    "class Net(nn.Module):\n",
    "    def __init__(self):\n",
    "        super().__init__()\n",
    "        self.fc1 = nn.Linear(28*28, 128)\n",
    "        self.fc2 = nn.Linear(128, 64)\n",
    "        self.fc3 = nn.Linear(64, 10)\n",
    "\n",
    "    def forward(self, x):\n",
    "        x = torch.flatten(x,1)\n",
    "\n",
    "        x = self.fc1(x)\n",
    "        x = F.relu(x)\n",
    "        x = self.fc2(x)\n",
    "        x = F.relu(x)\n",
    "        x = self.fc3(x)\n",
    "        x = F.log_softmax(x, dim=1)\n",
    "        return x\n",
    "\n",
    "\n",
    "model = Net()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 0 - Training loss: 0.6272492635367649\n",
      "Epoch 1 - Training loss: 0.2814668390963441\n",
      "Epoch 2 - Training loss: 0.21929898187279828\n",
      "Epoch 3 - Training loss: 0.17549290658751213\n",
      "Epoch 4 - Training loss: 0.1464396201904172\n",
      "Epoch 5 - Training loss: 0.12520822098871856\n",
      "Epoch 6 - Training loss: 0.10858830430114003\n",
      "Epoch 7 - Training loss: 0.09690638882979782\n",
      "Epoch 8 - Training loss: 0.08731873615432395\n",
      "Epoch 9 - Training loss: 0.07782375173363239\n",
      "Epoch 10 - Training loss: 0.07207398523818821\n",
      "Epoch 11 - Training loss: 0.06677345681919662\n",
      "Epoch 12 - Training loss: 0.061214422762666004\n",
      "Epoch 13 - Training loss: 0.05561702125675992\n",
      "Epoch 14 - Training loss: 0.05122477287448434\n",
      "\n",
      "Training Time (in minutes) = 2.744000514348348\n"
     ]
    }
   ],
   "source": [
    "criterion = nn.NLLLoss()\n",
    "optimizer = optim.SGD(model.parameters(), lr=0.003, momentum=0.9)\n",
    "\n",
    "time0 = time()\n",
    "epochs = 15\n",
    "for e in range(epochs):\n",
    "    running_loss = 0\n",
    "    for images, labels in trainloader:\n",
    "        \n",
    "        # Training pass\n",
    "        optimizer.zero_grad()\n",
    "        \n",
    "        output = model(images)\n",
    "        loss = criterion(output, labels)\n",
    "        \n",
    "        #This is where the model learns by backpropagating\n",
    "        loss.backward()\n",
    "        \n",
    "        #And optimizes its weights here\n",
    "        optimizer.step()\n",
    "        \n",
    "        running_loss += loss.item()\n",
    "    else:\n",
    "        print(\"Epoch {} - Training loss: {}\".format(e, running_loss/len(trainloader)))\n",
    "  \n",
    "print(\"\\nTraining Time (in minutes) =\",(time()-time0)/60)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number Of Images Tested = 10000\n",
      "\n",
      "Model Accuracy = 0.9775\n"
     ]
    }
   ],
   "source": [
    "correct_count, all_count = 0, 0\n",
    "for images,labels in valloader:\n",
    "  for i in range(len(labels)):\n",
    "    img = images[i].view(1, 784)\n",
    "    with torch.no_grad():\n",
    "        logps = model(img)\n",
    "\n",
    "    \n",
    "    ps = torch.exp(logps)\n",
    "    probab = list(ps.numpy()[0])\n",
    "    pred_label = probab.index(max(probab))\n",
    "    true_label = labels.numpy()[i]\n",
    "    if(true_label == pred_label):\n",
    "      correct_count += 1\n",
    "    all_count += 1\n",
    "\n",
    "print(\"Number Of Images Tested =\", all_count)\n",
    "print(\"\\nModel Accuracy =\", (correct_count/all_count))"
   ]
  }
 ],
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