Commit 3

main.ipynb

@@ -0,0 +1,225 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#import libraries\n",
+    "import torch \n",
+    "from torchvision import datasets, transforms    \n",
+    "import torch.nn as nn\n",
+    "import torch.optim as optim\n",
+    "from torch.utils.data import DataLoader\n",
+    "from torchvision.datasets import ImageFolder\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#define the data transforms\n",
+    "\n",
+    "transform = transforms.Compose([\n",
+    "  transforms.Resize((224,224)),\n",
+    "  transforms.ToTensor(),\n",
+    "  transforms.Normalize((0.485,0.456,0.406),(0.229,0.224,0.225))\n",
+    "  ])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#insert the datasets\n",
+    "\n",
+    "train_dataset = ImageFolder('./data/train', transform=transform)\n",
+    "test_dataset =ImageFolder('./data/test', transform=transform)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# make cnn model\n",
+    "\n",
+    "class CNN(nn.Module):\n",
+    "    def __init__(self):\n",
+    "        super(CNN, self).__init__()\n",
+    "        self.conv1 = nn.Conv2d(3, 6, 5)\n",
+    "        self.conv2 = nn.Conv2d(6, 16, 5)\n",
+    "        self.pool = nn.MaxPool2d(2, 2)\n",
+    "        self.fc1 = nn.Linear(16 * 53 * 53, 120)\n",
+    "        self.fc2 = nn.Linear(120, 84)\n",
+    "        self.fc3 = nn.Linear(84, 3)\n",
+    "\n",
+    "    def forward(self, x):\n",
+    "        x = self.conv1(x)\n",
+    "        x = self.pool(x)\n",
+    "        x = self.conv2(x)\n",
+    "        x = self.pool(x)\n",
+    "        x = x.view(-1, 16 * 53 * 53)\n",
+    "        x = self.fc1(x)\n",
+    "        x = self.fc2(x)\n",
+    "        x = self.fc3(x)\n",
+    "        return x\n",
+    "\n",
+    "  \n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "batch_size = 8\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)\n",
+    "test_loader = DataLoader(test_dataset, batch_size=batch_size, shuffle=True)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model = CNN()\n",
+    "loss_function = nn.CrossEntropyLoss()\n",
+    "optimizer = optim.Adam(model.parameters(), lr=0.001)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch [1/10], Step [1/34], Loss: 1.0981\n",
+      "Epoch [2/10], Step [1/34], Loss: 1.2921\n",
+      "Epoch [3/10], Step [1/34], Loss: 0.4883\n",
+      "Epoch [4/10], Step [1/34], Loss: 0.3408\n",
+      "Epoch [5/10], Step [1/34], Loss: 0.1063\n",
+      "Epoch [6/10], Step [1/34], Loss: 0.0406\n",
+      "Epoch [7/10], Step [1/34], Loss: 0.0009\n",
+      "Epoch [8/10], Step [1/34], Loss: 0.0066\n",
+      "Epoch [9/10], Step [1/34], Loss: 0.0009\n",
+      "Epoch [10/10], Step [1/34], Loss: 0.0012\n"
+     ]
+    }
+   ],
+   "source": [
+    "#Train the model\n",
+    "\n",
+    "for epoch in range(10):\n",
+    "    for i, (images, labels) in enumerate(train_loader):\n",
+    "\n",
+    "        outputs = model(images)\n",
+    "\n",
+    "        loss = loss_function(outputs, labels)\n",
+    "\n",
+    "        optimizer.zero_grad()\n",
+    "        loss.backward()\n",
+    "        optimizer.step()\n",
+    "\n",
+    "        if i % 200 == 0:\n",
+    "            print('Epoch [{}/{}], Step [{}/{}], Loss: {:.4f}'.format(epoch + 1, 10, i + 1, len(train_loader), loss.item()))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#iterate over the test data \n",
+    "\n",
+    "correct = 0\n",
+    "total = 0\n",
+    "for i, (images, labels) in enumerate(test_loader):\n",
+    "  outputs = model(images)\n",
+    "  \n",
+    "  _, predicted = torch.max(outputs.data, 1)\n",
+    "  correct += (predicted == labels).sum().item()\n",
+    "  total += labels.size(0)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Accuracy: 53.333333333333336%\n"
+     ]
+    }
+   ],
+   "source": [
+    "#calculate the accuracy\n",
+    "accuracy = 100 * correct / total\n",
+    "print('Accuracy: {}%' .format(accuracy))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model_scripted = torch.jit.script(model)\n",
+    "model_scripted.save('./models/cat_dog_cnn.pt')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "base",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}