hw-2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.utils.data as data\n",
    "import torchvision\n",
    "import torchvision.transforms as transforms\n",
    "from tqdm import tqdm\n",
    "from torch import optim\n",
    "from torch.autograd import Variable\n",
    "import time\n",
    "\n",
    "BATCH_SIZE = 128\n",
    "NUM_EPOCHS = 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "# preprocessing\n",
    "normalize = transforms.Normalize(mean=[.5], std=[.5])\n",
    "transform = transforms.Compose([transforms.ToTensor(), normalize])\n",
    "\n",
    "# download and load the data\n",
    "train_dataset = torchvision.datasets.MNIST(root='./mnist/', train=True, transform=transform, download=True)\n",
    "test_dataset = torchvision.datasets.MNIST(root='./mnist/', train=False, transform=transform, download=False)\n",
    "\n",
    "# encapsulate them into dataloader form\n",
    "train_loader = data.DataLoader(train_dataset, batch_size=BATCH_SIZE, shuffle=True, drop_last=True)\n",
    "test_loader = data.DataLoader(test_dataset, batch_size=BATCH_SIZE, shuffle=False, drop_last=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "class SimpleNet(nn.Module):\n",
    "    def __init__(self, in_dim, n_hidden_1, n_hidden_2, out_dim):\n",
    "        super(SimpleNet, self).__init__()\n",
    "        self.layer1 = nn.Sequential(nn.Linear(in_dim, n_hidden_1), nn.BatchNorm1d(n_hidden_1), nn.ReLU(True))\n",
    "        self.layer2 = nn.Sequential(nn.Linear(n_hidden_1, n_hidden_2), nn.BatchNorm1d(n_hidden_2), nn.ReLU(True))\n",
    "        self.layer3 = nn.Sequential(nn.Linear(n_hidden_2, out_dim))\n",
    " \n",
    "    def forward(self, x):\n",
    "        x = self.layer1(x)\n",
    "        x = self.layer2(x)\n",
    "        x = self.layer3(x)\n",
    "        return x\n",
    "\n",
    "model = SimpleNet(28 * 28, 300, 100, 10)\n",
    "criterion = nn.CrossEntropyLoss()\n",
    "optimizer = optim.SGD(model.parameters(), lr = 0.01)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train accuracy: 93.00%\n",
      "test accuracy：97.00%\n"
     ]
    }
   ],
   "source": [
    "test_correct = 0\n",
    "train_correct = 0\n",
    "train_total = 0\n",
    "train_total = 0\n",
    "\n",
    "for epoch in range(NUM_EPOCHS):\n",
    "    for data in train_loader:\n",
    "        img, label = data\n",
    "        img = img.view(img.size(0), -1)\n",
    "        img = Variable(img)\n",
    "        label = Variable(label)\n",
    "        out = model(img)\n",
    "        loss = criterion(out, label)\n",
    "        _, pred = torch.max(out, 1)\n",
    "        train_total += labels.size(0)\n",
    "        train_correct += (pred == label).sum()\n",
    "        \n",
    "        optimizer.zero_grad()\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "            \n",
    "model.eval()  \n",
    "for data in test_loader:\n",
    "    img, label = data\n",
    "    img = img.view(img.size(0), -1)\n",
    "\n",
    "    out = model(img)\n",
    "    loss = criterion(out, label)\n",
    "    _, pred = torch.max(out, 1)\n",
    "    train_total += labels.size(0)\n",
    "    test_correct += (pred == label).sum()\n",
    "        \n",
    "print('train accuracy: %.2f%%' % (100 * train_correct / train_total))\n",
    "print('test accuracy：%.2f%%' % (100 * test_correct / test_total))      "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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