13_feedforward.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "93a841cb",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-04-11T02:41:12.235242Z",
     "start_time": "2024-04-11T02:41:06.398521Z"
    }
   },
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import torchvision\n",
    "import torchvision.transforms as transforms\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "51c99b6f",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-04-11T02:41:12.388238Z",
     "start_time": "2024-04-11T02:41:12.254236Z"
    }
   },
   "outputs": [],
   "source": [
    "# Device configuration\n",
    "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "09c3ff5f",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-04-11T02:41:20.002494Z",
     "start_time": "2024-04-11T02:41:19.996489Z"
    }
   },
   "outputs": [],
   "source": [
    "# Hyper-parameters \n",
    "input_size = 784 # 28x28\n",
    "hidden_size = 500 \n",
    "num_classes = 10\n",
    "num_epochs = 2\n",
    "batch_size = 100\n",
    "learning_rate = 0.001"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "a001aa19",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-04-11T02:41:20.852085Z",
     "start_time": "2024-04-11T02:41:20.804759Z"
    }
   },
   "outputs": [],
   "source": [
    "# MNIST dataset \n",
    "train_dataset = torchvision.datasets.MNIST(root='./data', \n",
    "                                           train=True, \n",
    "                                           transform=transforms.ToTensor(),  \n",
    "                                           download=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "2bbfd941",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-04-11T02:41:38.323089Z",
     "start_time": "2024-04-11T02:41:38.305672Z"
    }
   },
   "outputs": [],
   "source": [
    "test_dataset = torchvision.datasets.MNIST(root='./data', \n",
    "                                          train=False, \n",
    "                                          transform=transforms.ToTensor())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "cce5b04f",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-04-11T02:41:39.056983Z",
     "start_time": "2024-04-11T02:41:39.051991Z"
    }
   },
   "outputs": [],
   "source": [
    "# Data loader\n",
    "train_loader = torch.utils.data.DataLoader(dataset=train_dataset, \n",
    "                                           batch_size=batch_size, \n",
    "                                           shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "407b1062",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-04-11T02:41:39.461225Z",
     "start_time": "2024-04-11T02:41:39.455233Z"
    }
   },
   "outputs": [],
   "source": [
    "test_loader = torch.utils.data.DataLoader(dataset=test_dataset, \n",
    "                                          batch_size=batch_size, \n",
    "                                          shuffle=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "f38c1635",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-04-11T02:41:40.011714Z",
     "start_time": "2024-04-11T02:41:39.986475Z"
    }
   },
   "outputs": [],
   "source": [
    "examples = iter(test_loader)\n",
    "example_data, example_targets = next(examples)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "a7107993",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-04-11T02:41:41.505093Z",
     "start_time": "2024-04-11T02:41:40.906123Z"
    },
    "lines_to_next_cell": 1
   },
   "outputs": [
    {
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",
      "text/plain": [
       "<Figure size 640x480 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i in range(6):\n",
    "    plt.subplot(2,3,i+1)\n",
    "    plt.imshow(example_data[i][0], cmap='gray')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3686e08f",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-04-11T02:41:41.587093Z",
     "start_time": "2024-04-11T02:41:41.587093Z"
    },
    "lines_to_next_cell": 1
   },
   "outputs": [],
   "source": [
    "# Fully connected neural network with one hidden layer\n",
    "class NeuralNet(nn.Module):\n",
    "    def __init__(self, input_size, hidden_size, num_classes):\n",
    "        super(NeuralNet, self).__init__()\n",
    "        self.input_size = input_size\n",
    "        self.l1 = nn.Linear(input_size, hidden_size) \n",
    "        self.relu = nn.ReLU()\n",
    "        self.l2 = nn.Linear(hidden_size, num_classes)  \n",
    "    \n",
    "    def forward(self, x):\n",
    "        out = self.l1(x)\n",
    "        out = self.relu(out)\n",
    "        out = self.l2(out)\n",
    "        # no activation and no softmax at the end\n",
    "        return out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "e6a2a6e0",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = NeuralNet(input_size, hidden_size, num_classes).to(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "803a84f0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Loss and optimizer\n",
    "criterion = nn.CrossEntropyLoss()\n",
    "optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "94608dcc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [1/2], Step [100/600], Loss: 0.2800\n",
      "Epoch [1/2], Step [200/600], Loss: 0.2887\n",
      "Epoch [1/2], Step [300/600], Loss: 0.2062\n",
      "Epoch [1/2], Step [400/600], Loss: 0.1620\n",
      "Epoch [1/2], Step [500/600], Loss: 0.1255\n",
      "Epoch [1/2], Step [600/600], Loss: 0.1446\n",
      "Epoch [2/2], Step [100/600], Loss: 0.0636\n",
      "Epoch [2/2], Step [200/600], Loss: 0.2747\n",
      "Epoch [2/2], Step [300/600], Loss: 0.1469\n",
      "Epoch [2/2], Step [400/600], Loss: 0.0780\n",
      "Epoch [2/2], Step [500/600], Loss: 0.1346\n",
      "Epoch [2/2], Step [600/600], Loss: 0.0918\n"
     ]
    }
   ],
   "source": [
    "# Train the model\n",
    "n_total_steps = len(train_loader)\n",
    "for epoch in range(num_epochs):\n",
    "    for i, (images, labels) in enumerate(train_loader):  \n",
    "        # origin shape: [100, 1, 28, 28]\n",
    "        # resized: [100, 784]\n",
    "        images = images.reshape(-1, 28*28).to(device)\n",
    "        labels = labels.to(device)\n",
    "        \n",
    "        # Forward pass\n",
    "        outputs = model(images)\n",
    "        loss = criterion(outputs, labels)\n",
    "        \n",
    "        # Backward and optimize\n",
    "        optimizer.zero_grad()\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "        \n",
    "        if (i+1) % 100 == 0:\n",
    "            print (f'Epoch [{epoch+1}/{num_epochs}], Step [{i+1}/{n_total_steps}], Loss: {loss.item():.4f}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "7135c50c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of the network on the 10000 test images: 97.03 %\n"
     ]
    }
   ],
   "source": [
    "# Test the model\n",
    "# In test phase, we don't need to compute gradients (for memory efficiency)\n",
    "with torch.no_grad():\n",
    "    n_correct = 0\n",
    "    n_samples = 0\n",
    "    for images, labels in test_loader:\n",
    "        images = images.reshape(-1, 28*28).to(device)\n",
    "        labels = labels.to(device)\n",
    "        outputs = model(images)\n",
    "        # max returns (value ,index)\n",
    "        _, predicted = torch.max(outputs.data, 1)\n",
    "        n_samples += labels.size(0)\n",
    "        n_correct += (predicted == labels).sum().item()\n",
    "\n",
    "    acc = 100.0 * n_correct / n_samples\n",
    "    print(f'Accuracy of the network on the 10000 test images: {acc} %')"
   ]
  }
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