TUMS.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-12-15T01:50:08.213966Z",
     "start_time": "2021-12-15T01:50:04.728098Z"
    }
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from pathlib import Path\n",
    "import torchvision\n",
    "import torchvision.transforms as transforms\n",
    "import random\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import torch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-12-15T01:50:13.102504Z",
     "start_time": "2021-12-15T01:50:09.024743Z"
    }
   },
   "outputs": [],
   "source": [
    "# load in train and test\n",
    "train_data_csv = pd.read_csv(\"./data/sign_mnist_train.csv\")\n",
    "test_data_csv = pd.read_csv(\"./data/sign_mnist_test.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-12-15T01:50:13.227985Z",
     "start_time": "2021-12-15T01:50:13.105302Z"
    }
   },
   "outputs": [],
   "source": [
    "# convert data to np arrays \n",
    "train_data = np.array(train_data_csv, dtype=\"float32\")\n",
    "test_data = np.array(test_data_csv, dtype=\"float32\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-12-15T01:50:13.243940Z",
     "start_time": "2021-12-15T01:50:13.229980Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(27455, 785)\n",
      "(7172, 785)\n"
     ]
    }
   ],
   "source": [
    "# 27k training cases; 7k test cases\n",
    "print(train_data.shape)\n",
    "print(test_data.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-12-15T01:50:15.342287Z",
     "start_time": "2021-12-15T01:50:15.310080Z"
    }
   },
   "outputs": [],
   "source": [
    "# Split data into input image data and the label, convert to a Tensor, and create a TensorDataset for each set\n",
    "train_tensor_set = torch.utils.data.TensorDataset(torch.Tensor(train_data[:, 1:].reshape(27455, 1, 28, 28)), torch.Tensor(train_data[:, 0]))\n",
    "test_tensor_set = torch.utils.data.TensorDataset(torch.Tensor(test_data[:, 1:].reshape(7172, 1, 28, 28)), torch.Tensor(test_data[:, 0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-12-15T01:50:17.657269Z",
     "start_time": "2021-12-15T01:50:17.648295Z"
    }
   },
   "outputs": [],
   "source": [
    "# Include J and Z technically, but there won't be any data associated with them\n",
    "classes =  ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J', 'K', 'L', 'M', 'N', 'O', 'P', 'Q', 'R', 'S', 'T', 'U', 'V', 'W', 'X', 'Y'] # not including the last letter for reduced computation. Not going to remove J for continuity in the indices, however"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-12-15T01:50:18.473077Z",
     "start_time": "2021-12-15T01:50:18.223259Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Label:  F\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Sample image display\n",
    "num = random.randint(1, train_data.shape[0])\n",
    "fig, ax = plt.subplots(figsize=(5, 5))\n",
    "plt.imshow(train_data[num, 1:].reshape((28, 28)))\n",
    "print(\"Label: \", classes[int(train_data[num, 0])])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-12-15T01:50:19.584240Z",
     "start_time": "2021-12-15T01:50:19.248033Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Label Distributions')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1152x1152 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Let's try something cool with the data\n",
    "fig = plt.figure(figsize = (16, 16))\n",
    "ax1 = fig.add_subplot(221)\n",
    "train_data_csv['label'].value_counts().plot(kind='bar', ax=ax1)\n",
    "# DISTRIBUTIONS of labels, look at x-axis for label indices\n",
    "ax1.set_ylabel('Count')\n",
    "ax1.set_title(\"Label Distributions\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-12-15T01:50:22.457815Z",
     "start_time": "2021-12-15T01:50:22.434875Z"
    }
   },
   "outputs": [],
   "source": [
    "# TODO Recalculate our means and standard deviations for the channels of our images, if necessary\n",
    "mnist_transform = transforms.Compose([\n",
    "    transforms.ToTensor(),\n",
    "    transforms.Normalize((0.5,0.5,0.5), (0.5, 0.5, 0.5))\n",
    "])\n",
    "# first set is for means, second is for standard deviations. Im pretty sure we have to calculate them, but let's see if it works okay without lol"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-12-15T01:50:23.053218Z",
     "start_time": "2021-12-15T01:50:23.047198Z"
    }
   },
   "outputs": [],
   "source": [
    "batch_size = 4 # Keep for now\n",
    "\n",
    "# Creates an iterable for training dataset\n",
    "trainloader = torch.utils.data.DataLoader(train_tensor_set, batch_size=batch_size, shuffle=True, num_workers=2)\n",
    "\n",
    "# Creates an iterable for testing dataset\n",
    "testloader = torch.utils.data.DataLoader(test_tensor_set, batch_size=batch_size, shuffle=False, num_workers=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-12-15T01:50:25.205667Z",
     "start_time": "2021-12-15T01:50:25.159536Z"
    }
   },
   "outputs": [],
   "source": [
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "class Net(nn.Module):\n",
    "    def __init__(self):\n",
    "        super().__init__()\n",
    "        # This is where a lot of the design process will happen since we have to decide layers and such\n",
    "        # The current values need to be adjusted; we don't have to immediately go for the most complex one, we should just try to get the program running\n",
    "        # with a simple model before we try to make it more complex.\n",
    "        self.conv1 = nn.Conv2d(1, 32, 3) # The first value is the number of input channels (1, since we are using greyscale images)\n",
    "        self.pool = nn.MaxPool2d(2, 2)\n",
    "        self.conv2 = nn.Conv2d(32, 64, 3)\n",
    "        self.fc1 = nn.Linear(1600, 420) # TODO Research transition from Conv2D to Linear layers\n",
    "        self.fc2 = nn.Linear(420, 69) \n",
    "        self.fc3 = nn.Linear(69, len(classes)) # the last int here has to be equal to the number of classes we are classifying for\n",
    "    # Onwards!\n",
    "    def forward(self, x):\n",
    "        x = self.pool(F.relu(self.conv1(x))) # relu cap the value between 0 and 1\n",
    "        x = self.pool(F.relu(self.conv2(x)))\n",
    "        x = torch.flatten(x, 1)\n",
    "        # print(x.size())\n",
    "        x = F.relu(self.fc1(x))\n",
    "        x = F.relu(self.fc2(x))\n",
    "        x = self.fc3(x)\n",
    "        return x\n",
    "net = Net()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-12-15T01:50:25.896598Z",
     "start_time": "2021-12-15T01:50:25.884636Z"
    }
   },
   "outputs": [],
   "source": [
    "# ONWARDS!\n",
    "# Initialize optimizer and loss function\n",
    "import torch.optim as optim\n",
    "\n",
    "criterion = nn.CrossEntropyLoss()\n",
    "optimizer = optim.SGD(net.parameters(), lr=0.003, momentum=0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-12-15T02:18:18.092727Z",
     "start_time": "2021-12-15T02:12:55.469261Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1,  2000] loss: 1.405\n",
      "[1,  4000] loss: 0.156\n",
      "[1,  6000] loss: 0.050\n",
      "[2,  2000] loss: 0.001\n",
      "[2,  4000] loss: 0.000\n",
      "[2,  6000] loss: 0.000\n",
      "[3,  2000] loss: 0.000\n",
      "[3,  4000] loss: 0.000\n",
      "[3,  6000] loss: 0.000\n",
      "[4,  2000] loss: 0.000\n",
      "[4,  4000] loss: 0.000\n",
      "[4,  6000] loss: 0.000\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_2908/3406593408.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     13\u001b[0m         \u001b[0mloss\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbackward\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     14\u001b[0m         \u001b[1;31m# Perform gradient descent step to modify parameters\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 15\u001b[1;33m         \u001b[0moptimizer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstep\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     16\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     17\u001b[0m         \u001b[1;31m# print statistics\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\.conda\\envs\\ACMProjFA21\\lib\\site-packages\\torch\\optim\\optimizer.py\u001b[0m in \u001b[0;36mwrapper\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m     86\u001b[0m                 \u001b[0mprofile_name\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m\"Optimizer.step#{}.step\"\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__class__\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__name__\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     87\u001b[0m                 \u001b[1;32mwith\u001b[0m \u001b[0mtorch\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mautograd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprofiler\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrecord_function\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mprofile_name\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 88\u001b[1;33m                     \u001b[1;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     89\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     90\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\.conda\\envs\\ACMProjFA21\\lib\\site-packages\\torch\\autograd\\grad_mode.py\u001b[0m in \u001b[0;36mdecorate_context\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m     26\u001b[0m         \u001b[1;32mdef\u001b[0m \u001b[0mdecorate_context\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     27\u001b[0m             \u001b[1;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__class__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 28\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     29\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mcast\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mF\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdecorate_context\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     30\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\.conda\\envs\\ACMProjFA21\\lib\\site-packages\\torch\\optim\\sgd.py\u001b[0m in \u001b[0;36mstep\u001b[1;34m(self, closure)\u001b[0m\n\u001b[0;32m    134\u001b[0m                         \u001b[0mmomentum_buffer_list\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mstate\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'momentum_buffer'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    135\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 136\u001b[1;33m             F.sgd(params_with_grad,\n\u001b[0m\u001b[0;32m    137\u001b[0m                   \u001b[0md_p_list\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    138\u001b[0m                   \u001b[0mmomentum_buffer_list\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\.conda\\envs\\ACMProjFA21\\lib\\site-packages\\torch\\optim\\_functional.py\u001b[0m in \u001b[0;36msgd\u001b[1;34m(params, d_p_list, momentum_buffer_list, weight_decay, momentum, lr, dampening, nesterov)\u001b[0m\n\u001b[0;32m    171\u001b[0m                 \u001b[0mmomentum_buffer_list\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mbuf\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    172\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 173\u001b[1;33m                 \u001b[0mbuf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmul_\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmomentum\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0madd_\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0md_p\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0malpha\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m1\u001b[0m \u001b[1;33m-\u001b[0m \u001b[0mdampening\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    174\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    175\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mnesterov\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "for epoch in range(5):\n",
    "    running_loss = 0.0\n",
    "    for i, data in enumerate(trainloader, 0):\n",
    "        # get the inputs; data is a list of [inputs, labels]\n",
    "        inputs, labels = data \n",
    "        # zero the parameter gradients\n",
    "        optimizer.zero_grad()\n",
    "        # forward + backward + optimize\n",
    "        outputs = net(inputs)\n",
    "        # compute loss\n",
    "        loss = criterion(outputs,labels.long())\n",
    "        # compute gradients\n",
    "        loss.backward()\n",
    "        # Perform gradient descent step to modify parameters\n",
    "        optimizer.step()\n",
    "\n",
    "        # print statistics\n",
    "        running_loss += loss.item()\n",
    "        if i % 2000 == 1999: # print every 2000 mini-batches\n",
    "            print('[%d, %5d] loss: %.3f' % (epoch + 1, i + 1, running_loss / 2000))\n",
    "            running_loss = 0.0\n",
    "\n",
    "print('Ready for Tums!')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "f7774b32",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of the network on the 10000 test images: 91 %\n"
     ]
    }
   ],
   "source": [
    "correct = 0\n",
    "total = 0\n",
    "# since we're not training, we don't need to calculate the gradients for our outputs\n",
    "with torch.no_grad():\n",
    "    for data in testloader:\n",
    "        images, labels = data\n",
    "        # calculate outputs by running images through the network\n",
    "        outputs = net(images)  \n",
    "        # the class with the highest energy is what we choose as prediction\n",
    "        _, predicted = torch.max(outputs.data, 1)\n",
    "        total += labels.size(0)\n",
    "        correct += (predicted == labels).sum().item()\n",
    "\n",
    "print('Accuracy of the network on the 10000 test images: %d %%' % (\n",
    "    100 * correct / total))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "PATH = './tums_net.pth'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save the weights if they are looking good\n",
    "torch.save(net.state_dict(), PATH)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "b6680393",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<All keys matched successfully>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "net = Net()\n",
    "net.load_state_dict(torch.load(PATH))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "ename": "RuntimeError",
     "evalue": "output with shape [1, 28, 28] doesn't match the broadcast shape [3, 28, 28]",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_14112/1209686214.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      6\u001b[0m     \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mimshow\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mimage\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      7\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 8\u001b[1;33m     \u001b[0mprediction\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnet\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmnist_transform\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mimage\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      9\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     10\u001b[0m     \u001b[0mpredicted\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0margmax\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mprediction\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\.conda\\envs\\ACMProjFA21\\lib\\site-packages\\torchvision\\transforms\\transforms.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, img)\u001b[0m\n\u001b[0;32m     59\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m__call__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mimg\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     60\u001b[0m         \u001b[1;32mfor\u001b[0m \u001b[0mt\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtransforms\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 61\u001b[1;33m             \u001b[0mimg\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mt\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mimg\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     62\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mimg\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     63\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\.conda\\envs\\ACMProjFA21\\lib\\site-packages\\torch\\nn\\modules\\module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[1;34m(self, *input, **kwargs)\u001b[0m\n\u001b[0;32m   1100\u001b[0m         if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks\n\u001b[0;32m   1101\u001b[0m                 or _global_forward_hooks or _global_forward_pre_hooks):\n\u001b[1;32m-> 1102\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mforward_call\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0minput\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1103\u001b[0m         \u001b[1;31m# Do not call functions when jit is used\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1104\u001b[0m         \u001b[0mfull_backward_hooks\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnon_full_backward_hooks\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\.conda\\envs\\ACMProjFA21\\lib\\site-packages\\torchvision\\transforms\\transforms.py\u001b[0m in \u001b[0;36mforward\u001b[1;34m(self, tensor)\u001b[0m\n\u001b[0;32m    224\u001b[0m             \u001b[0mTensor\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mNormalized\u001b[0m \u001b[0mTensor\u001b[0m \u001b[0mimage\u001b[0m\u001b[1;33m.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    225\u001b[0m         \"\"\"\n\u001b[1;32m--> 226\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mF\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnormalize\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtensor\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstd\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minplace\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    227\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    228\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m__repr__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\.conda\\envs\\ACMProjFA21\\lib\\site-packages\\torchvision\\transforms\\functional.py\u001b[0m in \u001b[0;36mnormalize\u001b[1;34m(tensor, mean, std, inplace)\u001b[0m\n\u001b[0;32m    349\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mstd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    350\u001b[0m         \u001b[0mstd\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mstd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mview\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 351\u001b[1;33m     \u001b[0mtensor\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msub_\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmean\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdiv_\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mstd\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    352\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0mtensor\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    353\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mRuntimeError\u001b[0m: output with shape [1, 28, 28] doesn't match the broadcast shape [3, 28, 28]"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Sample image prediction\n",
    "with torch.no_grad():\n",
    "    num = random.randint(1, test_data.shape[0])\n",
    "    fig, ax = plt.subplots(figsize=(5, 5))\n",
    "    image = test_data[num, 1:].reshape((28, 28))\n",
    "    plt.imshow(image)\n",
    "\n",
    "    prediction = net(mnist_transform(image))\n",
    "\n",
    "    predicted = np.argmax(prediction)\n",
    "    \n",
    "    image = image.reshape(28, 28, 1)\n",
    "\n",
    "    print(\"Predicted Label: \", classes[predicted])\n",
    "    print(\"Actual Label: \", classes[int(test_data[num, 0])])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x168a94a9250>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Importing Image class from PIL module\n",
    "from PIL import Image\n",
    "\n",
    "# Opens a image in RGB mode\n",
    "im = Image.open(r\"data\\SampleA.jpg\")\n",
    "\n",
    "# Size of the image in pixels (size of original image)\n",
    "# (This is not mandatory)\n",
    "width, height = im.size\n",
    "\n",
    "# Setting the points for cropped image\n",
    "left = 381\n",
    "top = 135\n",
    "right = left + 527\n",
    "bottom = top + 527\n",
    "\n",
    "# Cropped image of above dimension\n",
    "# (It will not change original image)\n",
    "im1 = im.crop((left, top, right, bottom))\n",
    "newsize = (28, 28)\n",
    "im1 = im1.resize(newsize)\n",
    "# Shows the image in image viewer\n",
    "plt.imshow(im1)\n",
    "\n",
    "# credit to https://www.geeksforgeeks.org/python-pil-image-resize-method/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "ename": "RuntimeError",
     "evalue": "Expected 4-dimensional input for 4-dimensional weight [32, 1, 3, 3], but got 3-dimensional input of size [3, 28, 28] instead",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_14112/919493299.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      4\u001b[0m     \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mimshow\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mim1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 6\u001b[1;33m     \u001b[0mprediction\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnet\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmnist_transform\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mim1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      7\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      8\u001b[0m     \u001b[0mpredicted\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0margmax\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mprediction\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\.conda\\envs\\ACMProjFA21\\lib\\site-packages\\torch\\nn\\modules\\module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[1;34m(self, *input, **kwargs)\u001b[0m\n\u001b[0;32m   1100\u001b[0m         if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks\n\u001b[0;32m   1101\u001b[0m                 or _global_forward_hooks or _global_forward_pre_hooks):\n\u001b[1;32m-> 1102\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mforward_call\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0minput\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1103\u001b[0m         \u001b[1;31m# Do not call functions when jit is used\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1104\u001b[0m         \u001b[0mfull_backward_hooks\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnon_full_backward_hooks\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_14112/2126427385.py\u001b[0m in \u001b[0;36mforward\u001b[1;34m(self, x)\u001b[0m\n\u001b[0;32m     15\u001b[0m     \u001b[1;31m# Onwards!\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     16\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mforward\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 17\u001b[1;33m         \u001b[0mx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpool\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mF\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrelu\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconv1\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;31m# relu cap the value between 0 and 1\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     18\u001b[0m         \u001b[0mx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpool\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mF\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrelu\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconv2\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     19\u001b[0m         \u001b[0mx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtorch\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mflatten\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\.conda\\envs\\ACMProjFA21\\lib\\site-packages\\torch\\nn\\modules\\module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[1;34m(self, *input, **kwargs)\u001b[0m\n\u001b[0;32m   1100\u001b[0m         if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks\n\u001b[0;32m   1101\u001b[0m                 or _global_forward_hooks or _global_forward_pre_hooks):\n\u001b[1;32m-> 1102\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mforward_call\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0minput\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1103\u001b[0m         \u001b[1;31m# Do not call functions when jit is used\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1104\u001b[0m         \u001b[0mfull_backward_hooks\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnon_full_backward_hooks\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\.conda\\envs\\ACMProjFA21\\lib\\site-packages\\torch\\nn\\modules\\conv.py\u001b[0m in \u001b[0;36mforward\u001b[1;34m(self, input)\u001b[0m\n\u001b[0;32m    444\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    445\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mforward\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minput\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mTensor\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m->\u001b[0m \u001b[0mTensor\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 446\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_conv_forward\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minput\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mweight\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbias\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    447\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    448\u001b[0m \u001b[1;32mclass\u001b[0m \u001b[0mConv3d\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0m_ConvNd\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\.conda\\envs\\ACMProjFA21\\lib\\site-packages\\torch\\nn\\modules\\conv.py\u001b[0m in \u001b[0;36m_conv_forward\u001b[1;34m(self, input, weight, bias)\u001b[0m\n\u001b[0;32m    440\u001b[0m                             \u001b[0mweight\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbias\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstride\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    441\u001b[0m                             _pair(0), self.dilation, self.groups)\n\u001b[1;32m--> 442\u001b[1;33m         return F.conv2d(input, weight, bias, self.stride,\n\u001b[0m\u001b[0;32m    443\u001b[0m                         self.padding, self.dilation, self.groups)\n\u001b[0;32m    444\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mRuntimeError\u001b[0m: Expected 4-dimensional input for 4-dimensional weight [32, 1, 3, 3], but got 3-dimensional input of size [3, 28, 28] instead"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Sample image prediction\n",
    "with torch.no_grad():\n",
    "\n",
    "    plt.imshow(im1)\n",
    "\n",
    "    prediction = net(mnist_transform(im1))\n",
    "\n",
    "    predicted = np.argmax(prediction)\n",
    "    \n",
    "    image = im1.reshape(28, 28, 1)\n",
    "\n",
    "    print(\"Predicted Label: \", classes[predicted])\n",
    "    print(\"Actual Label: A\")"
   ]
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "c0e4fcc3d75181d5baff9ac549e5c058ea351c3777f91bd28d8788ba40aecc8b"
  },
  "kernelspec": {
   "display_name": "Python [conda env:team-tums]",
   "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": "3.9.7"
  },
  "latex_envs": {
   "LaTeX_envs_menu_present": true,
   "autoclose": false,
   "autocomplete": true,
   "bibliofile": "biblio.bib",
   "cite_by": "apalike",
   "current_citInitial": 1,
   "eqLabelWithNumbers": true,
   "eqNumInitial": 1,
   "hotkeys": {
    "equation": "Ctrl-E",
    "itemize": "Ctrl-I"
   },
   "labels_anchors": false,
   "latex_user_defs": false,
   "report_style_numbering": false,
   "user_envs_cfg": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}