MNISTCLASSIFICATION.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "ebcb76c5",
   "metadata": {},
   "source": [
    "# MNIST DATASET CNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "356a951f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import torch\n",
    "import torchvision\n",
    "from torch import nn\n",
    "from torchvision import datasets\n",
    "from torchvision import transforms\n",
    "from torchvision.transforms import ToTensor\n",
    "import  matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "2cb7aafa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torchvision.datasets.mnist.MNIST"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "torchvision.datasets.MNIST"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "5cecc928",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data= datasets.MNIST(\n",
    "    root=\"data\", #where to download the data\n",
    "    train=True,\n",
    "    download=True,\n",
    "    transform=torchvision.transforms.ToTensor(),\n",
    "    target_transform=None\n",
    ")\n",
    "test_data= datasets.MNIST(\n",
    "    root=\"data\", #where to download the data\n",
    "    train=False,\n",
    "    download=True,\n",
    "    transform=torchvision.transforms.ToTensor(),\n",
    "    target_transform=None\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "8d4c5d5a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Dataset MNIST\n",
       "    Number of datapoints: 60000\n",
       "    Root location: data\n",
       "    Split: Train\n",
       "    StandardTransform\n",
       "Transform: ToTensor()"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "b90d87ea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Dataset MNIST\n",
       "    Number of datapoints: 10000\n",
       "    Root location: data\n",
       "    Split: Test\n",
       "    StandardTransform\n",
       "Transform: ToTensor()"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "c4563a14",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "60000"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(train_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "ed652926",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10000"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(test_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "61c2680e",
   "metadata": {},
   "outputs": [],
   "source": [
    "image,label=train_data[0]\n",
    "# image,label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "32a15904",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['0 - zero',\n",
       " '1 - one',\n",
       " '2 - two',\n",
       " '3 - three',\n",
       " '4 - four',\n",
       " '5 - five',\n",
       " '6 - six',\n",
       " '7 - seven',\n",
       " '8 - eight',\n",
       " '9 - nine']"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "class_names= train_data.classes\n",
    "class_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "4f632c32",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'0 - zero': 0,\n",
       " '1 - one': 1,\n",
       " '2 - two': 2,\n",
       " '3 - three': 3,\n",
       " '4 - four': 4,\n",
       " '5 - five': 5,\n",
       " '6 - six': 6,\n",
       " '7 - seven': 7,\n",
       " '8 - eight': 8,\n",
       " '9 - nine': 9}"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "class_to_idx=train_data.class_to_idx\n",
    "class_to_idx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "41777287",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "60000"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(train_data.targets)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "2045e2c1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([1, 28, 28])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "image.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "8cfa3b88",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([1, 28, 28])\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, '5 - five')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "image,lable=train_data[0]\n",
    "print(image.shape)\n",
    "plt.imshow(image.squeeze())\n",
    "plt.title(class_names[label])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "11318d1e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-0.5, 27.5, 27.5, -0.5)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(image.squeeze(),cmap=\"gray\")\n",
    "plt.axis(False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "19648e8a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 900x900 with 16 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "torch.rand(42)\n",
    "\n",
    "fig=plt.figure(figsize=(9,9))\n",
    "rows,cols=4,4\n",
    "for i in range(1, rows*cols+1):\n",
    "    random_idx=torch.randint(0,len(train_data),size=[1]).item()\n",
    "    img,lable=train_data[random_idx]\n",
    "    fig.add_subplot(rows,cols,i)\n",
    "    plt.imshow(img.squeeze(), cmap=\"gray\")\n",
    "    plt.title(class_names[lable])\n",
    "    plt.axis(False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "2ef2d063",
   "metadata": {},
   "outputs": [],
   "source": [
    "from torch.utils.data import DataLoader\n",
    "BATCH_SIZE=32\n",
    "#turn datasets into batches\n",
    "train_dataloader=DataLoader(dataset=train_data,\n",
    "                           batch_size=BATCH_SIZE,\n",
    "                           shuffle=True)\n",
    "test_dataloader=DataLoader(dataset=test_data,\n",
    "                           batch_size=BATCH_SIZE,\n",
    "                           shuffle=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "a85c47b8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1875, 313)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(train_dataloader), len(test_dataloader)\n",
    "#1875 is one batch , so 1875*32 is 60000 (length of train data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "22d845bf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(torch.Size([32, 1, 28, 28]), torch.Size([32]))"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#whats happening in training datloader\n",
    "train_features_batch, train_labels_batch=next(iter(train_dataloader))\n",
    "train_features_batch.shape,train_labels_batch.shape\n",
    "#batchsize, height,width,color channels\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "4cc94ac2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-0.5, 27.5, 27.5, -0.5)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#show a sample\n",
    "torch.manual_seed(42)\n",
    "random_idx=torch.randint(0,len(train_features_batch), size=[1]).item()\n",
    "img,label= train_features_batch[random_idx], train_labels_batch[random_idx]\n",
    "plt.imshow(img.squeeze(),cmap=\"gray\")\n",
    "plt.title(class_names[label])\n",
    "plt.axis(False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "10da022e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "before flattening:\n",
      "torch.Size([1, 28, 28])\n",
      "after flattening:\n",
      "torch.Size([1, 784])\n"
     ]
    }
   ],
   "source": [
    "#creat a flatten layer\n",
    "flatten_model=nn.Flatten()\n",
    "#get a single sample\n",
    "x= train_features_batch[0]\n",
    "x.shape\n",
    "#flatten the sample\n",
    "output=flatten_model(x) #perform what happened\n",
    "print('before flattening:')\n",
    "print(x.shape)#color channels, height, width\n",
    "print('after flattening:')\n",
    "print(output.shape)#color channels, height*width"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "426a1f00",
   "metadata": {},
   "outputs": [],
   "source": [
    "class MNISTModelTinyVGG(nn.Module):\n",
    "    \"\"\"\n",
    "    Model architecture that replicates the TinyVGG\n",
    "    model from CNN explainer website\n",
    "    \"\"\"\n",
    "    \n",
    "    def __init__(self,input_shape: int, hidden_units: int, output_shape: int):\n",
    "        super().__init__()\n",
    "        #block1: conv,relu- conv,relu,maxpool\n",
    "        #block2: conv,relu- conv,relu,maxpool\n",
    "        self.conv_block_1=nn.Sequential(\n",
    "            nn.Conv2d(in_channels= input_shape,\n",
    "                      out_channels= hidden_units,\n",
    "                      kernel_size= 3,\n",
    "                      stride= 1,\n",
    "                      padding=1),\n",
    "            nn.ReLU(),\n",
    "            nn.Conv2d(in_channels= hidden_units,\n",
    "                      out_channels= hidden_units,\n",
    "                      kernel_size= 3,\n",
    "                      stride= 1,\n",
    "                      padding=1),\n",
    "            nn.ReLU(),\n",
    "            nn.MaxPool2d(kernel_size=2,\n",
    "                         stride=2)\n",
    "        )\n",
    "        self.conv_block_2=nn.Sequential(\n",
    "            nn.Conv2d(in_channels= hidden_units,\n",
    "                      out_channels= hidden_units,\n",
    "                      kernel_size= 3,\n",
    "                      stride= 1,\n",
    "                      padding=1),\n",
    "            nn.ReLU(),\n",
    "            nn.Conv2d(in_channels= hidden_units,\n",
    "                      out_channels= hidden_units,\n",
    "                      kernel_size= 3,\n",
    "                      stride= 1,\n",
    "                      padding=1),\n",
    "            nn.ReLU(),\n",
    "            nn.MaxPool2d(kernel_size=2,\n",
    "                         stride=2)\n",
    "        )\n",
    "        \n",
    "        self.classifier = nn.Sequential(\n",
    "            nn.Flatten(),\n",
    "            nn.Linear(in_features=hidden_units*7*7,\n",
    "                      out_features= output_shape)\n",
    "        )\n",
    "    \n",
    "    #forward method\n",
    "    \n",
    "    def forward(self,x):\n",
    "        x = self.conv_block_1(x)\n",
    "        #print(x.shape)\n",
    "        x= self.conv_block_2(x)\n",
    "        #print(x.shape)\n",
    "        x= self.classifier(x)\n",
    "        return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "2357700b",
   "metadata": {},
   "outputs": [],
   "source": [
    "class_names= train_data.classes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "1b554a9b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MNISTModelTinyVGG(\n",
       "  (conv_block_1): Sequential(\n",
       "    (0): Conv2d(1, 10, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (1): ReLU()\n",
       "    (2): Conv2d(10, 10, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (3): ReLU()\n",
       "    (4): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
       "  )\n",
       "  (conv_block_2): Sequential(\n",
       "    (0): Conv2d(10, 10, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (1): ReLU()\n",
       "    (2): Conv2d(10, 10, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (3): ReLU()\n",
       "    (4): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
       "  )\n",
       "  (classifier): Sequential(\n",
       "    (0): Flatten(start_dim=1, end_dim=-1)\n",
       "    (1): Linear(in_features=490, out_features=10, bias=True)\n",
       "  )\n",
       ")"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "torch.manual_seed(42)\n",
    "mnist_model= MNISTModelTinyVGG(input_shape=1,\n",
    "                           hidden_units=10,\n",
    "                           output_shape= len(class_names))\n",
    "mnist_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "d40495df",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "image shape torch.Size([32, 3, 64, 64])\n",
      "test image shape torch.Size([3, 64, 64])\n"
     ]
    }
   ],
   "source": [
    "#nn. Conv2d\n",
    "torch.manual_seed(42)\n",
    "#creating a batch of images\n",
    "images = torch.randn(size=(32,3,64,64))\n",
    "test_image= images[0]\n",
    "print('image shape',images.shape)\n",
    "print('test image shape',test_image.shape)\n",
    "# print(test_image)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "bcb6d355",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f9138245f60>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(image.squeeze(), cmap=\"gray\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "32e1f087",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "exists, skipping download....\n"
     ]
    }
   ],
   "source": [
    "import requests\n",
    "from pathlib import Path\n",
    "#download\n",
    "if Path(\"helper_functions.py\").is_file():\n",
    "    print('exists, skipping download....')\n",
    "else:\n",
    "    print('downloading..')\n",
    "    request=requests.get(\"https://raw.githubusercontent.com/mrdbourke/pytorch-deep-learning/main/helper_functions.py\")\n",
    "    with open('helper_functions.py','wb') as f:\n",
    "        f.write(request.content)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "2459619c",
   "metadata": {},
   "outputs": [],
   "source": [
    "from helper_functions import accuracy_fn\n",
    "\n",
    "loss_fn = nn.CrossEntropyLoss()\n",
    "optimizer= torch.optim.SGD(params=mnist_model.parameters(),\n",
    "                          lr=0.1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7e67f92e",
   "metadata": {},
   "source": [
    "# training and testing loops"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "e1b15b7d",
   "metadata": {},
   "outputs": [],
   "source": [
    "def train_step(model: torch.nn.Module,\n",
    "              data_loader: torch.utils.data.DataLoader,\n",
    "              loss_fn: torch.nn.Module,\n",
    "              optimizer: torch.optim.Optimizer,\n",
    "              accuracy_fn):\n",
    "    train_loss, train_acc = 0,0\n",
    "    #put model in training mode\n",
    "    model.train()\n",
    "\n",
    "    #training\n",
    "\n",
    "    # add a loop to loop through the training batches\n",
    "    for batch, (X,y) in enumerate(data_loader):\n",
    "        #1 forward pass\n",
    "        y_pred= model(X)\n",
    "        \n",
    "        loss= loss_fn(y_pred,y)\n",
    "        \n",
    "        train_loss += loss #accumulate train loss\n",
    "        train_acc +=accuracy_fn(y_true=y,\n",
    "                               y_pred=y_pred.argmax(dim=1))\n",
    "        \n",
    "        optimizer.zero_grad()\n",
    "        \n",
    "        #loss backward\n",
    "        loss.backward()\n",
    "        \n",
    "        #optimizer step\n",
    "        optimizer.step()\n",
    "        \n",
    "        #print out whats happening\n",
    "        if batch %400==0:\n",
    "            print(f\"Looked at {batch*len(X)}/{len(train_dataloader.dataset)} samples.\")\n",
    "            \n",
    "    #divide total train loss and acc by length of train dataloader\n",
    "    train_loss /= len(data_loader)\n",
    "    train_acc /= len(data_loader)\n",
    "    #print out whats happening\n",
    "    print(f\"Train loss: {train_loss:0.5f} | Train_acc: {train_acc: 0.2f}%\")\n",
    "\n",
    "test_loss=0\n",
    "def testing_step(model: torch.nn.Module,\n",
    "              data_loader: torch.utils.data.DataLoader,\n",
    "              loss_fn: torch.nn.Module,\n",
    "              accuracy_fn):\n",
    "    test_loss, test_acc=0,0\n",
    "    model.eval()\n",
    "    with torch.inference_mode():\n",
    "        for X,y in test_dataloader:\n",
    "            #forward pass\n",
    "            test_pred= model(X)\n",
    "            \n",
    "            #calculate the loss\n",
    "            test_loss +=loss_fn(test_pred,y)\n",
    "            \n",
    "            #calculate the accuracy\n",
    "            test_acc +=accuracy_fn(y_true=y, \n",
    "                                   y_pred=test_pred.argmax(dim=1))\n",
    "            \n",
    "        #calculate the test loss average per batch\n",
    "        test_loss /= len(data_loader)\n",
    "        \n",
    "        #calculate the test acc average per batch\n",
    "        test_acc /= len(data_loader)\n",
    "    \n",
    "    #print out whats happening\n",
    "    print(f\"Test_loss: {test_loss:0.5f} | Test_acc: {test_loss: 0.2f}, Test acc: {test_acc: 4f}\")\n",
    "\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "74a28eac",
   "metadata": {},
   "outputs": [],
   "source": [
    "from timeit import default_timer as timer\n",
    "def print_train_time(start: float,\n",
    "                    end: float):\n",
    "    \"\"\"Prints difference between start and end time \"\"\"\n",
    "    total_time= end-start\n",
    "    return (\"train time\",total_time)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "56271445",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f8199bb5eb76430d8a6a79220d90761e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/3 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 0\n",
      "----\n",
      "Looked at 0/60000 samples.\n",
      "Looked at 12800/60000 samples.\n",
      "Looked at 25600/60000 samples.\n",
      "Looked at 38400/60000 samples.\n",
      "Looked at 51200/60000 samples.\n",
      "Train loss: 0.33442 | Train_acc:  88.81%\n",
      "Test_loss: 0.07836 | Test_acc:  0.08, Test acc:  97.603834\n",
      "Epoch: 1\n",
      "----\n",
      "Looked at 0/60000 samples.\n",
      "Looked at 12800/60000 samples.\n",
      "Looked at 25600/60000 samples.\n",
      "Looked at 38400/60000 samples.\n",
      "Looked at 51200/60000 samples.\n",
      "Train loss: 0.08719 | Train_acc:  97.26%\n",
      "Test_loss: 0.07802 | Test_acc:  0.08, Test acc:  97.563898\n",
      "Epoch: 2\n",
      "----\n",
      "Looked at 0/60000 samples.\n",
      "Looked at 12800/60000 samples.\n",
      "Looked at 25600/60000 samples.\n",
      "Looked at 38400/60000 samples.\n",
      "Looked at 51200/60000 samples.\n",
      "Train loss: 0.06807 | Train_acc:  97.92%\n",
      "Test_loss: 0.05946 | Test_acc:  0.06, Test acc:  98.063099\n"
     ]
    }
   ],
   "source": [
    "torch.manual_seed(42)\n",
    "from tqdm.auto import tqdm\n",
    "\n",
    "from timeit import default_timer as timer\n",
    "train_time_start_mnist_model = timer()\n",
    "\n",
    "epochs=3\n",
    "for epoch in tqdm(range(epochs)):\n",
    "    print(f\"Epoch: {epoch}\\n----\")\n",
    "    train_step(model=mnist_model,\n",
    "               data_loader= train_dataloader,\n",
    "               loss_fn= loss_fn,\n",
    "               optimizer= optimizer,\n",
    "               accuracy_fn= accuracy_fn)\n",
    "    \n",
    "    testing_step(model=mnist_model,\n",
    "              data_loader= test_dataloader,\n",
    "              loss_fn= loss_fn,\n",
    "              accuracy_fn= accuracy_fn\n",
    "              )\n",
    "train_time_end_mnist_model= timer()\n",
    "total_train_time_mnist_model= print_train_time(start=train_time_start_mnist_model,\n",
    "                                           end=train_time_end_mnist_model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "6860a8ee",
   "metadata": {},
   "outputs": [],
   "source": [
    "# torch.manual_seed(42)\n",
    "def eval_model(model: torch.nn.Module,\n",
    "              data_loader: torch.utils.data.DataLoader,\n",
    "              loss_fn: torch.nn.Module,\n",
    "              accuracy_fn):\n",
    "    \"\"\"Returns a dictionary containng the results of model predicting on data Loader\"\"\"\n",
    "    loss, acc=0,0\n",
    "    model.eval()\n",
    "    with torch.inference_mode():\n",
    "        for X,y in data_loader:\n",
    "            #make predictions\n",
    "            y_pred= model(X)\n",
    "            \n",
    "            \n",
    "            #accumulate the loss and accuracy balues per batch\n",
    "            loss += loss_fn(y_pred,y)\n",
    "            acc += accuracy_fn(y_true=y,\n",
    "                              y_pred= y_pred.argmax(dim=1))\n",
    "            \n",
    "        #scale loss and acc to find the average loss/acc per batch\n",
    "        loss /= len(data_loader)\n",
    "        acc /= len(data_loader)\n",
    "    \n",
    "    return {\"model_name\": model.__class__.__name__,\n",
    "           \"model_loss\": loss.item(),\n",
    "           \"model_acc\": acc}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "390a4c75",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'model_name': 'MNISTModelTinyVGG',\n",
       " 'model_loss': 0.059458471834659576,\n",
       " 'model_acc': 98.06309904153355}"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get results\n",
    "mnist_model_results =  eval_model( model= mnist_model,\n",
    "                              data_loader= test_dataloader,\n",
    "                              loss_fn= loss_fn,\n",
    "                              accuracy_fn= accuracy_fn)\n",
    "mnist_model_results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "3f8eaf1e",
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_predictions(model: torch.nn.Module,\n",
    "                     data: list):\n",
    "    pred_probs=[]\n",
    "    model.eval()\n",
    "    with torch.inference_mode():\n",
    "        for sample in data:\n",
    "            #prepare the sample\n",
    "            sample= torch.unsqueeze(sample, dim=0)\n",
    "            \n",
    "            #forward pass\n",
    "            pred_logit = model(sample)\n",
    "            \n",
    "            #get prediction probablity\n",
    "            pred_prob = torch.softmax(pred_logit.squeeze(), dim=0)\n",
    "            \n",
    "            #get pred probs off the GPU\n",
    "            pred_probs.append(pred_prob)\n",
    "    \n",
    "    # stack the pred_probs to turn a list into a tensor\n",
    "    return torch.stack(pred_probs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "a270828f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([1, 28, 28])\n",
      "torch.Size([1, 28, 28])\n",
      "torch.Size([1, 28, 28])\n",
      "torch.Size([1, 28, 28])\n",
      "torch.Size([1, 28, 28])\n",
      "torch.Size([1, 28, 28])\n",
      "torch.Size([1, 28, 28])\n",
      "torch.Size([1, 28, 28])\n",
      "torch.Size([1, 28, 28])\n"
     ]
    }
   ],
   "source": [
    "import random\n",
    "random.seed(42)\n",
    "test_samples= []\n",
    "test_lables= []\n",
    "for sample, label in random.sample(list(test_data), k=9):\n",
    "    test_samples.append(sample)\n",
    "    test_lables.append(label)\n",
    "    \n",
    "    print(test_samples[0].shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "d15cfbf8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, '2 - two')"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(test_samples[0].squeeze(), cmap=\"gray\")\n",
    "plt.title(class_names[test_lables[0]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "86c20ca0",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[W NNPACK.cpp:53] Could not initialize NNPACK! Reason: Unsupported hardware.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "tensor([[9.1950e-11, 1.5202e-06, 1.0000e+00, 3.4008e-08, 4.9504e-14, 2.1632e-14,\n",
       "         3.7700e-14, 4.2789e-09, 1.8015e-06, 6.3871e-11],\n",
       "        [1.9299e-07, 9.9503e-01, 3.9883e-06, 5.5190e-08, 3.5612e-06, 1.3554e-03,\n",
       "         3.9629e-04, 2.9952e-06, 3.1831e-03, 2.7266e-05]])"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "# pd.set_option('display.float_format',lambda x: '%.3f'%x)\n",
    "# pd.options.display.float_format = '{:.2f}'.format\n",
    "pred_probs = make_predictions(model= mnist_model,\n",
    "                             data= test_samples)\n",
    "# view first two prediction probablities\n",
    "pred_probs[:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "64f35861",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([2, 1, 2, 4, 6, 6, 4, 9, 1])"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# convert pred probs to labels\n",
    "pred_classes= pred_probs.argmax(dim=1)\n",
    "pred_classes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "60f35b3f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 900x900 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "random.seed(42)\n",
    "# plot predtions\n",
    "plt.figure(figsize=(9,9))\n",
    "nrows= 3\n",
    "ncols=3\n",
    "for i , sample in enumerate(test_samples):\n",
    "    #create subplot\n",
    "    plt.subplot(nrows,ncols, i+1)\n",
    "    \n",
    "    # plot the target image\n",
    "    plt.imshow(sample.squeeze(), cmap= 'gray')\n",
    "    \n",
    "    # find pred label in text form\n",
    "    pred_label= class_names[pred_classes[i]]\n",
    "    \n",
    "    #get truth label \n",
    "    truth_label = class_names[test_lables[i]]\n",
    "    \n",
    "    #create a title for the plot\n",
    "    title_text= f\"Pred: {pred_label} | Truth: {truth_label}\"\n",
    "    \n",
    "    \n",
    "    # check for equality between pred and truth and change color of title text\n",
    "    if pred_label == truth_label:\n",
    "        plt.title(title_text, fontsize=10, c='g') #green if pred is same as truth\n",
    "    else:\n",
    "        plt.title(title_text, fontsize=10, c='r')\n",
    "    \n",
    "    plt.axis(False);\n",
    "\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "c5ee99ed",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1aedeefdd3e44513b87e4e32f2a9f70e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "making predictions...:   0%|          | 0/313 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "tensor([7, 2, 1, 0, 8, 1, 8, 8, 8, 8])"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# import progress bar\n",
    "from tqdm.auto import tqdm\n",
    "\n",
    "# make predictions with trained model\n",
    "y_preds=[]\n",
    "mnist_model.eval()\n",
    "with torch.inference_mode():\n",
    "    for X,y in tqdm(test_dataloader, desc= \"making predictions...\"):\n",
    "        #forward pass\n",
    "        y_logit= mnist_model(X)\n",
    "        # turn predictions from logits to predictions\n",
    "        y_pred= torch.softmax(y_logit.squeeze(), dim=0).argmax(dim=1)\n",
    "        #put prediction on cpu for evaluation\n",
    "        y_preds.append(y_pred)\n",
    "\n",
    "# print(y_preds)\n",
    "y_pred_tensor= torch.cat(y_preds)\n",
    "y_pred_tensor[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "ccd6d0f5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import torchmetrics\n",
    "from torchmetrics import ConfusionMatrix\n",
    "from mlxtend.plotting import plot_confusion_matrix\n",
    "\n",
    "# setup confusion instance and compare predictions to targets\n",
    "confmat = ConfusionMatrix(num_classes= len(class_names))\n",
    "confmat_tensor= confmat(preds= y_pred_tensor,\n",
    "                        target = test_data.targets)\n",
    "\n",
    "# plot confusion matrix\n",
    "fig, ax = plot_confusion_matrix(\n",
    "    conf_mat=confmat_tensor.numpy(),\n",
    "    class_names= class_names,\n",
    "    figsize=(10,7))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "69a21a45",
   "metadata": {},
   "source": [
    "# predictive system"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "3c6f745b",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([1])\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import cv2\n",
    "import numpy as np\n",
    "\n",
    "\n",
    "# Load sample image\n",
    "file = r\"/Users/kush/Downloads/WhatsApp Image 2022-11-19 at 9.38.57 PM.jpeg\"\n",
    "test_image = cv2.imread(file)\n",
    "\n",
    "# Preview sample image\n",
    "plt.imshow(test_image, cmap='gray')\n",
    "\n",
    "\n",
    "gray = cv2.cvtColor(test_image, cv2.COLOR_BGR2GRAY)\n",
    "plt.imshow(gray, cmap='gray')\n",
    "\n",
    "\n",
    "# perform edge detection, find contours in the edge map, and sort the\n",
    "# resulting contours from left-to-right\n",
    "edged = cv2.Canny(gray, 50, 50)\n",
    "plt.imshow(edged, cmap='gray')\n",
    "\n",
    "\n",
    "# Format Image\n",
    "img_resized = cv2.resize(gray, (28, 28), interpolation=cv2.INTER_AREA)\n",
    "img_resized = cv2.bitwise_not(img_resized)\n",
    "\n",
    "kernel = np.array([[0, -1, 0],\n",
    "                   [-1, 5,-1],\n",
    "                   [0, -1, 0]])\n",
    "img_resized = cv2.filter2D(src=img_resized, ddepth=-1, kernel=kernel)\n",
    "#img_resized = cv2.Canny(img_resized, 50, 50)\n",
    "\n",
    "# Preview reformatted image\n",
    "plt.imshow(img_resized, cmap='gray')\n",
    "\n",
    "# convert image to tensor\n",
    "convert_tensor = transforms.ToTensor()\n",
    "l=convert_tensor(img_resized.squeeze())\n",
    "l.shape\n",
    "\n",
    "\n",
    "# Logits -> Prediction probabilities -> Prediction labels\n",
    "model_pred_logits = mnist_model(l.unsqueeze(dim=1))\n",
    "model_pred_probs = torch.softmax(model_pred_logits, dim=1)\n",
    "model_pred_label = torch.argmax(model_pred_probs, dim=1)\n",
    "print(model_pred_label)\n",
    "\n",
    "# Plot the image and prediction\n",
    "\n",
    "# plt.figure()\n",
    "\n",
    "# plt.imshow(l.squeeze(), cmap=\"gray\")\n",
    "\n",
    "plt.title(f\" Pred: {model_pred_label.item()}\") \n",
    "# plt.axis(False);\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "55252424",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[[178, 178, 178,  ..., 195, 195, 195],\n",
       "         [178, 178, 178,  ..., 195, 195, 195],\n",
       "         [178, 178, 178,  ..., 195, 195, 195],\n",
       "         ...,\n",
       "         [175, 175, 175,  ..., 202, 202, 202],\n",
       "         [175, 175, 175,  ..., 202, 202, 202],\n",
       "         [175, 175, 174,  ..., 202, 202, 202]],\n",
       "\n",
       "        [[178, 178, 178,  ..., 195, 195, 195],\n",
       "         [178, 178, 178,  ..., 195, 195, 195],\n",
       "         [178, 178, 178,  ..., 195, 195, 195],\n",
       "         ...,\n",
       "         [179, 179, 179,  ..., 202, 202, 202],\n",
       "         [179, 179, 179,  ..., 202, 202, 202],\n",
       "         [179, 179, 178,  ..., 202, 202, 202]],\n",
       "\n",
       "        [[178, 178, 178,  ..., 195, 195, 195],\n",
       "         [178, 178, 178,  ..., 195, 195, 195],\n",
       "         [178, 178, 178,  ..., 195, 195, 195],\n",
       "         ...,\n",
       "         [180, 180, 180,  ..., 202, 202, 202],\n",
       "         [180, 180, 180,  ..., 202, 202, 202],\n",
       "         [180, 180, 179,  ..., 202, 202, 202]]], dtype=torch.uint8)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import torchvision\n",
    "file = r\"/Users/kush/Downloads/WhatsApp Image 2022-11-19 at 9.38.57 PM (2).jpeg\"\n",
    "# read custom image\n",
    "custom_image_uint8= torchvision.io.read_image(str(file)) #io means input output\n",
    "custom_image_uint8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "a6f68f15",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f9151fdcbb0>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(custom_image_uint8.permute(1,2,0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "a1541afe",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "custom image tensor tensor([[[178, 178, 178,  ..., 195, 195, 195],\n",
      "         [178, 178, 178,  ..., 195, 195, 195],\n",
      "         [178, 178, 178,  ..., 195, 195, 195],\n",
      "         ...,\n",
      "         [175, 175, 175,  ..., 202, 202, 202],\n",
      "         [175, 175, 175,  ..., 202, 202, 202],\n",
      "         [175, 175, 174,  ..., 202, 202, 202]],\n",
      "\n",
      "        [[178, 178, 178,  ..., 195, 195, 195],\n",
      "         [178, 178, 178,  ..., 195, 195, 195],\n",
      "         [178, 178, 178,  ..., 195, 195, 195],\n",
      "         ...,\n",
      "         [179, 179, 179,  ..., 202, 202, 202],\n",
      "         [179, 179, 179,  ..., 202, 202, 202],\n",
      "         [179, 179, 178,  ..., 202, 202, 202]],\n",
      "\n",
      "        [[178, 178, 178,  ..., 195, 195, 195],\n",
      "         [178, 178, 178,  ..., 195, 195, 195],\n",
      "         [178, 178, 178,  ..., 195, 195, 195],\n",
      "         ...,\n",
      "         [180, 180, 180,  ..., 202, 202, 202],\n",
      "         [180, 180, 180,  ..., 202, 202, 202],\n",
      "         [180, 180, 179,  ..., 202, 202, 202]]], dtype=torch.uint8)\n",
      "custom image shape torch.Size([3, 1280, 1280])\n",
      "custom image datatype torch.uint8\n"
     ]
    }
   ],
   "source": [
    "print('custom image tensor',custom_image_uint8)\n",
    "print('custom image shape',custom_image_uint8.shape)\n",
    "print('custom image datatype',custom_image_uint8.dtype)#%%"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "11cb1790",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[[0.6980, 0.6980, 0.6980,  ..., 0.7647, 0.7647, 0.7647],\n",
       "         [0.6980, 0.6980, 0.6980,  ..., 0.7647, 0.7647, 0.7647],\n",
       "         [0.6980, 0.6980, 0.6980,  ..., 0.7647, 0.7647, 0.7647],\n",
       "         ...,\n",
       "         [0.6863, 0.6863, 0.6863,  ..., 0.7922, 0.7922, 0.7922],\n",
       "         [0.6863, 0.6863, 0.6863,  ..., 0.7922, 0.7922, 0.7922],\n",
       "         [0.6863, 0.6863, 0.6824,  ..., 0.7922, 0.7922, 0.7922]],\n",
       "\n",
       "        [[0.6980, 0.6980, 0.6980,  ..., 0.7647, 0.7647, 0.7647],\n",
       "         [0.6980, 0.6980, 0.6980,  ..., 0.7647, 0.7647, 0.7647],\n",
       "         [0.6980, 0.6980, 0.6980,  ..., 0.7647, 0.7647, 0.7647],\n",
       "         ...,\n",
       "         [0.7020, 0.7020, 0.7020,  ..., 0.7922, 0.7922, 0.7922],\n",
       "         [0.7020, 0.7020, 0.7020,  ..., 0.7922, 0.7922, 0.7922],\n",
       "         [0.7020, 0.7020, 0.6980,  ..., 0.7922, 0.7922, 0.7922]],\n",
       "\n",
       "        [[0.6980, 0.6980, 0.6980,  ..., 0.7647, 0.7647, 0.7647],\n",
       "         [0.6980, 0.6980, 0.6980,  ..., 0.7647, 0.7647, 0.7647],\n",
       "         [0.6980, 0.6980, 0.6980,  ..., 0.7647, 0.7647, 0.7647],\n",
       "         ...,\n",
       "         [0.7059, 0.7059, 0.7059,  ..., 0.7922, 0.7922, 0.7922],\n",
       "         [0.7059, 0.7059, 0.7059,  ..., 0.7922, 0.7922, 0.7922],\n",
       "         [0.7059, 0.7059, 0.7020,  ..., 0.7922, 0.7922, 0.7922]]])"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#load custom image an convert to torch.int32\n",
    "custom_image = torchvision.io.read_image(str(file)).type(torch.float32)/255\n",
    "custom_image\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "bf3fdd54",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f915297aa40>"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(custom_image.permute(1,2,0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "b33cdff9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "original shape torch.Size([3, 1280, 1280])\n",
      "transformed shape torch.Size([3, 28, 28])\n"
     ]
    }
   ],
   "source": [
    "#resize the image\n",
    "custom_image_transform= transforms.Compose([transforms.Resize(size=(28,28))])#%%#%%#%%\n",
    "#transform target image\n",
    "custom_image_transformed= custom_image_transform(custom_image)\n",
    "#print out the shape\n",
    "print('original shape', custom_image.shape)\n",
    "print('transformed shape',custom_image_transformed.shape)#transform the image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "f4261d39",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f9152a04f40>"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(custom_image_transformed.permute(1,2,0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "7041d5ae",
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "upsample_bilinear2d() received an invalid combination of arguments - got (Tensor, list, bool, NoneType), but expected one of:\n * (Tensor input, tuple of SymInts output_size, bool align_corners, tuple of floats scale_factors)\n      didn't match because some of the arguments have invalid types: (Tensor, !list!, bool, !NoneType!)\n * (Tensor input, tuple of SymInts output_size, bool align_corners, float scales_h, float scales_w, *, Tensor out)\n",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn [47], line 36\u001b[0m\n\u001b[1;32m     34\u001b[0m custom_image_transform\u001b[38;5;241m=\u001b[39m transforms\u001b[38;5;241m.\u001b[39mCompose([transforms\u001b[38;5;241m.\u001b[39mResize(size\u001b[38;5;241m=\u001b[39m(gray,(\u001b[38;5;241m28\u001b[39m,\u001b[38;5;241m28\u001b[39m)))])\n\u001b[1;32m     35\u001b[0m \u001b[38;5;66;03m#transform target image\u001b[39;00m\n\u001b[0;32m---> 36\u001b[0m custom_image_transformed\u001b[38;5;241m=\u001b[39m \u001b[43mcustom_image_transform\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcustom_image\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     37\u001b[0m \u001b[38;5;66;03m#print out the shape\u001b[39;00m\n\u001b[1;32m     38\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124moriginal shape\u001b[39m\u001b[38;5;124m'\u001b[39m, custom_image\u001b[38;5;241m.\u001b[39mshape)\n",
      "File \u001b[0;32m~/opt/anaconda3/envs/newenv/lib/python3.10/site-packages/torchvision/transforms/transforms.py:95\u001b[0m, in \u001b[0;36mCompose.__call__\u001b[0;34m(self, img)\u001b[0m\n\u001b[1;32m     93\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__call__\u001b[39m(\u001b[38;5;28mself\u001b[39m, img):\n\u001b[1;32m     94\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m t \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtransforms:\n\u001b[0;32m---> 95\u001b[0m         img \u001b[38;5;241m=\u001b[39m \u001b[43mt\u001b[49m\u001b[43m(\u001b[49m\u001b[43mimg\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     96\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m img\n",
      "File \u001b[0;32m~/opt/anaconda3/envs/newenv/lib/python3.10/site-packages/torch/nn/modules/module.py:1190\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m   1186\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m   1187\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m   1188\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m   1189\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1190\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1191\u001b[0m \u001b[38;5;66;03m# Do not call functions when jit is used\u001b[39;00m\n\u001b[1;32m   1192\u001b[0m full_backward_hooks, non_full_backward_hooks \u001b[38;5;241m=\u001b[39m [], []\n",
      "File \u001b[0;32m~/opt/anaconda3/envs/newenv/lib/python3.10/site-packages/torchvision/transforms/transforms.py:346\u001b[0m, in \u001b[0;36mResize.forward\u001b[0;34m(self, img)\u001b[0m\n\u001b[1;32m    338\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mforward\u001b[39m(\u001b[38;5;28mself\u001b[39m, img):\n\u001b[1;32m    339\u001b[0m     \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m    340\u001b[0m \u001b[38;5;124;03m    Args:\u001b[39;00m\n\u001b[1;32m    341\u001b[0m \u001b[38;5;124;03m        img (PIL Image or Tensor): Image to be scaled.\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    344\u001b[0m \u001b[38;5;124;03m        PIL Image or Tensor: Rescaled image.\u001b[39;00m\n\u001b[1;32m    345\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[0;32m--> 346\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mF\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mresize\u001b[49m\u001b[43m(\u001b[49m\u001b[43mimg\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msize\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43minterpolation\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmax_size\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mantialias\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/opt/anaconda3/envs/newenv/lib/python3.10/site-packages/torchvision/transforms/functional.py:476\u001b[0m, in \u001b[0;36mresize\u001b[0;34m(img, size, interpolation, max_size, antialias)\u001b[0m\n\u001b[1;32m    473\u001b[0m     pil_interpolation \u001b[38;5;241m=\u001b[39m pil_modes_mapping[interpolation]\n\u001b[1;32m    474\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m F_pil\u001b[38;5;241m.\u001b[39mresize(img, size\u001b[38;5;241m=\u001b[39moutput_size, interpolation\u001b[38;5;241m=\u001b[39mpil_interpolation)\n\u001b[0;32m--> 476\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mF_t\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mresize\u001b[49m\u001b[43m(\u001b[49m\u001b[43mimg\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msize\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moutput_size\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43minterpolation\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minterpolation\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mvalue\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mantialias\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mantialias\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/opt/anaconda3/envs/newenv/lib/python3.10/site-packages/torchvision/transforms/functional_tensor.py:469\u001b[0m, in \u001b[0;36mresize\u001b[0;34m(img, size, interpolation, antialias)\u001b[0m\n\u001b[1;32m    466\u001b[0m \u001b[38;5;66;03m# Define align_corners to avoid warnings\u001b[39;00m\n\u001b[1;32m    467\u001b[0m align_corners \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m \u001b[38;5;28;01mif\u001b[39;00m interpolation \u001b[38;5;129;01min\u001b[39;00m [\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mbilinear\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mbicubic\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m--> 469\u001b[0m img \u001b[38;5;241m=\u001b[39m \u001b[43minterpolate\u001b[49m\u001b[43m(\u001b[49m\u001b[43mimg\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msize\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msize\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmode\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minterpolation\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43malign_corners\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43malign_corners\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mantialias\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mantialias\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    471\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m interpolation \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mbicubic\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mand\u001b[39;00m out_dtype \u001b[38;5;241m==\u001b[39m torch\u001b[38;5;241m.\u001b[39muint8:\n\u001b[1;32m    472\u001b[0m     img \u001b[38;5;241m=\u001b[39m img\u001b[38;5;241m.\u001b[39mclamp(\u001b[38;5;28mmin\u001b[39m\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0\u001b[39m, \u001b[38;5;28mmax\u001b[39m\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m255\u001b[39m)\n",
      "File \u001b[0;32m~/opt/anaconda3/envs/newenv/lib/python3.10/site-packages/torch/nn/functional.py:3950\u001b[0m, in \u001b[0;36minterpolate\u001b[0;34m(input, size, scale_factor, mode, align_corners, recompute_scale_factor, antialias)\u001b[0m\n\u001b[1;32m   3948\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m antialias:\n\u001b[1;32m   3949\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m torch\u001b[38;5;241m.\u001b[39m_C\u001b[38;5;241m.\u001b[39m_nn\u001b[38;5;241m.\u001b[39m_upsample_bilinear2d_aa(\u001b[38;5;28minput\u001b[39m, output_size, align_corners, scale_factors)\n\u001b[0;32m-> 3950\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mtorch\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_C\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_nn\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mupsample_bilinear2d\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43moutput_size\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43malign_corners\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mscale_factors\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   3951\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28minput\u001b[39m\u001b[38;5;241m.\u001b[39mdim() \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m5\u001b[39m \u001b[38;5;129;01mand\u001b[39;00m mode \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtrilinear\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[1;32m   3952\u001b[0m     \u001b[38;5;28;01massert\u001b[39;00m align_corners \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n",
      "\u001b[0;31mTypeError\u001b[0m: upsample_bilinear2d() received an invalid combination of arguments - got (Tensor, list, bool, NoneType), but expected one of:\n * (Tensor input, tuple of SymInts output_size, bool align_corners, tuple of floats scale_factors)\n      didn't match because some of the arguments have invalid types: (Tensor, !list!, bool, !NoneType!)\n * (Tensor input, tuple of SymInts output_size, bool align_corners, float scales_h, float scales_w, *, Tensor out)\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "import torchvision.transforms as transforms\n",
    "from PIL import Image\n",
    "\n",
    "# Read the image\n",
    "k= r\"/Users/kush/Downloads/WhatsApp Image 2022-11-19 at 9.38.57 PM (2).jpeg\"\n",
    "img = Image.open(k)\n",
    "\n",
    "# define a transform to convert the image to grayscale\n",
    "transform = transforms.Grayscale()\n",
    "\n",
    "# apply the above transform on the image\n",
    "img = transform(img)\n",
    "\n",
    "# dispaly the image\n",
    "img.show()\n",
    "\n",
    "# # num of output channels = 1\n",
    "# print(img.mode)\n",
    "custom_image_uint8= torchvision.io.read_image(str(k)) #io means input output\n",
    "custom_image_uint8\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "# load custom image an convert to torch.int32\n",
    "custom_image = torchvision.io.read_image(str(file)).type(torch.float32)/255\n",
    "custom_image\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "# resize the image\n",
    "custom_image_transform= transforms.Compose([transforms.Resize(size=(gray,(28,28)))])\n",
    "#transform target image\n",
    "custom_image_transformed= custom_image_transform(custom_image)\n",
    "#print out the shape\n",
    "print('original shape', custom_image.shape)\n",
    "print('transformed shape',custom_image_transformed.shape)#transform the image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0bc8b8e1",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torchvision\n",
    "import cv2\n",
    "file = r\"/Users/kush/Downloads/WhatsApp Image 2022-11-19 at 9.38.57 PM (2).jpeg\"\n",
    "# read custom image\n",
    "custom_image_uint8= torchvision.io.read_image(str(file)) #io means input output\n",
    "custom_image_uint8\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "# load custom image an convert to torch.int32\n",
    "custom_image = torchvision.io.read_image(str(file)).type(torch.float32)/255\n",
    "custom_image\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "# resize the image\n",
    "custom_image_transform= transforms.Compose([transforms.Resize(size=(gray,(28,28)))])#%%#%%#%%#convert custom image to tensor\n",
    "#transform target image\n",
    "custom_image_transformed= custom_image_transform(custom_image)\n",
    "#print out the shape\n",
    "print('original shape', custom_image.shape)\n",
    "print('transformed shape',custom_image_transformed.shape)#transform the image\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3b9db007",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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