notebooks/chapter07_optimization/7.3_minibatch-sgd.ipynb

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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 7.3 小批量随机梯度下降"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.0.0\n"
     ]
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import time\n",
    "import torch\n",
    "from torch import nn, optim\n",
    "import sys\n",
    "sys.path.append(\"..\") \n",
    "import L0CV\n",
    "\n",
    "print(torch.__version__)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 7.3.1 读取数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([1500, 5])"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def get_data_ch7():  # 本函数已保存在L0CV包中方便以后使用\n",
    "    data = np.genfromtxt('../../data/airfoil_self_noise.dat', delimiter='\\t')\n",
    "    data = (data - data.mean(axis=0)) / data.std(axis=0) # 标准化\n",
    "    return torch.tensor(data[:1500, :-1], dtype=torch.float32), \\\n",
    "           torch.tensor(data[:1500, -1], dtype=torch.float32) # 前1500个样本(每个样本5个特征)\n",
    "\n",
    "features, labels = get_data_ch7()\n",
    "features.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 7.3.2 从零开始实现"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def sgd(params, states, hyperparams):\n",
    "    for p in params:\n",
    "        p.data -= hyperparams['lr'] * p.grad.data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 本函数已保存在d2lzh_pytorch包中方便以后使用\n",
    "def train_ch7(optimizer_fn, states, hyperparams, features, labels,\n",
    "              batch_size=10, num_epochs=2):\n",
    "    # 初始化模型\n",
    "    net, loss = d2l.linreg, d2l.squared_loss\n",
    "    \n",
    "    w = torch.nn.Parameter(torch.tensor(np.random.normal(0, 0.01, size=(features.shape[1], 1)), dtype=torch.float32),\n",
    "                           requires_grad=True)\n",
    "    b = torch.nn.Parameter(torch.zeros(1, dtype=torch.float32), requires_grad=True)\n",
    "\n",
    "    def eval_loss():\n",
    "        return loss(net(features, w, b), labels).mean().item()\n",
    "\n",
    "    ls = [eval_loss()]\n",
    "    data_iter = torch.utils.data.DataLoader(\n",
    "        torch.utils.data.TensorDataset(features, labels), batch_size, shuffle=True)\n",
    "    \n",
    "    for _ in range(num_epochs):\n",
    "        start = time.time()\n",
    "        for batch_i, (X, y) in enumerate(data_iter):\n",
    "            l = loss(net(X, w, b), y).mean()  # 使用平均损失\n",
    "            \n",
    "            # 梯度清零\n",
    "            if w.grad is not None:\n",
    "                w.grad.data.zero_()\n",
    "                b.grad.data.zero_()\n",
    "                \n",
    "            l.backward()\n",
    "            optimizer_fn([w, b], states, hyperparams)  # 迭代模型参数\n",
    "            if (batch_i + 1) * batch_size % 100 == 0:\n",
    "                ls.append(eval_loss())  # 每100个样本记录下当前训练误差\n",
    "    # 打印结果和作图\n",
    "    print('loss: %f, %f sec per epoch' % (ls[-1], time.time() - start))\n",
    "    d2l.set_figsize()\n",
    "    d2l.plt.plot(np.linspace(0, num_epochs, len(ls)), ls)\n",
    "    d2l.plt.xlabel('epoch')\n",
    "    d2l.plt.ylabel('loss')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loss: 0.244678, 0.012216 sec per epoch\n"
     ]
    },
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    "def train_sgd(lr, batch_size, num_epochs=2):\n",
    "    train_ch7(sgd, None, {'lr': lr}, features, labels, batch_size, num_epochs)\n",
    "\n",
    "train_sgd(1, 1500, 6)"
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     "text": [
      "loss: 0.246391, 0.334214 sec per epoch\n"
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   "source": [
    "train_sgd(0.05, 10)"
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 7.3.3 简洁实现"
   ]
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   "execution_count": 8,
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    "# 本函数与原书不同的是这里第一个参数优化器函数而不是优化器的名字\n",
    "# 例如: optimizer_fn=torch.optim.SGD, optimizer_hyperparams={\"lr\": 0.05}\n",
    "def train_pytorch_ch7(optimizer_fn, optimizer_hyperparams, features, labels,\n",
    "                    batch_size=10, num_epochs=2):\n",
    "    # 初始化模型\n",
    "    net = nn.Sequential(\n",
    "        nn.Linear(features.shape[-1], 1)\n",
    "    )\n",
    "    loss = nn.MSELoss()\n",
    "    optimizer = optimizer_fn(net.parameters(), **optimizer_hyperparams)\n",
    "\n",
    "    def eval_loss():\n",
    "        return loss(net(features).view(-1), labels).item() / 2\n",
    "\n",
    "    ls = [eval_loss()]\n",
    "    data_iter = torch.utils.data.DataLoader(\n",
    "        torch.utils.data.TensorDataset(features, labels), batch_size, shuffle=True)\n",
    "\n",
    "    for _ in range(num_epochs):\n",
    "        start = time.time()\n",
    "        for batch_i, (X, y) in enumerate(data_iter):\n",
    "            # 除以2是为了和train_ch7保持一致, 因为squared_loss中除了2\n",
    "            l = loss(net(X).view(-1), y) / 2 \n",
    "            \n",
    "            optimizer.zero_grad()\n",
    "            l.backward()\n",
    "            optimizer.step()\n",
    "            if (batch_i + 1) * batch_size % 100 == 0:\n",
    "                ls.append(eval_loss())\n",
    "    # 打印结果和作图\n",
    "    print('loss: %f, %f sec per epoch' % (ls[-1], time.time() - start))\n",
    "    d2l.set_figsize()\n",
    "    d2l.plt.plot(np.linspace(0, num_epochs, len(ls)), ls)\n",
    "    d2l.plt.xlabel('epoch')\n",
    "    d2l.plt.ylabel('loss')"
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     "text": [
      "loss: 0.245491, 0.044150 sec per epoch\n"
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