DL_EXP_01.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[-1.6339e+17,  1.0870e-19, -1.6338e+17],\n",
      "        [-1.0870e-19, -1.6339e+17, -3.6990e+19]])\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "x = torch.empty(2, 3)\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[0.5831, 0.6992, 0.9188],\n",
      "        [0.9686, 0.5431, 0.4877]])\n"
     ]
    }
   ],
   "source": [
    "x = torch.rand(2, 3)\n",
    "print(x)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[0, 0, 0],\n",
      "        [0, 0, 0]])\n"
     ]
    }
   ],
   "source": [
    "x = torch.zeros(2, 3, dtype=torch.long)\n",
    "print(x)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[5, 5, 3],\n",
      "        [2, 2, 5]])\n"
     ]
    }
   ],
   "source": [
    "x = torch.tensor([[5, 5, 3], [2, 2, 5]])\n",
    "print(x)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[1, 1, 1],\n",
      "        [1, 1, 1]])\n"
     ]
    }
   ],
   "source": [
    "x = x.new_ones(2, 3)\n",
    "print(x)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[0.6096, 0.8882, 0.8294],\n",
      "        [0.3682, 0.1706, 0.7608]])\n"
     ]
    }
   ],
   "source": [
    "x = torch.rand_like(x, dtype=torch.float)\n",
    "print(x)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([2, 3])\n",
      "torch.Size([2, 3])\n"
     ]
    }
   ],
   "source": [
    "print(x.size())\n",
    "print(x.shape)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[0.7645, 0.6439, 0.2719],\n",
      "        [0.5941, 0.4194, 0.3230]])\n",
      "tensor([[0.8693, 0.0487, 0.9718],\n",
      "        [0.9983, 0.7449, 0.7045]])\n",
      "tensor([[1.6338, 0.6926, 1.2436],\n",
      "        [1.5924, 1.1644, 1.0275]])\n"
     ]
    }
   ],
   "source": [
    "x = torch.rand(2, 3)\n",
    "y = torch.rand(2, 3)\n",
    "print(x)\n",
    "print(y)\n",
    "print(x + y)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[0.7645, 0.6439, 0.2719],\n",
      "        [0.5941, 0.4194, 0.3230]])\n",
      "tensor([[0.8693, 0.0487, 0.9718],\n",
      "        [0.9983, 0.7449, 0.7045]])\n",
      "tensor([[1.6338, 0.6926, 1.2436],\n",
      "        [1.5924, 1.1644, 1.0275]])\n"
     ]
    }
   ],
   "source": [
    "print(x)\n",
    "print(y)\n",
    "print(torch.add(x, y))"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[0.7645, 0.6439, 0.2719],\n",
      "        [0.5941, 0.4194, 0.3230]])\n",
      "tensor([[0.8693, 0.0487, 0.9718],\n",
      "        [0.9983, 0.7449, 0.7045]])\n",
      "tensor([[1.6338, 0.6926, 1.2436],\n",
      "        [1.5924, 1.1644, 1.0275]])\n"
     ]
    }
   ],
   "source": [
    "print(x)\n",
    "print(y)\n",
    "y.add_(x)\n",
    "print(y)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([1.7645, 1.6439, 1.2719])\n",
      "tensor([2.7645, 2.6439, 2.2719])\n",
      "tensor([2.7645, 2.6439, 2.2719])\n"
     ]
    }
   ],
   "source": [
    "y = x[0, :]\n",
    "print(y)\n",
    "y += 1\n",
    "print(y)\n",
    "print(x[0, :])"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([2, 3]) torch.Size([6]) torch.Size([3, 2])\n",
      "tensor([[2.7645, 2.6439, 2.2719],\n",
      "        [0.5941, 0.4194, 0.3230]]) tensor([2.7645, 2.6439, 2.2719, 0.5941, 0.4194, 0.3230]) tensor([[2.7645, 2.6439],\n",
      "        [2.2719, 0.5941],\n",
      "        [0.4194, 0.3230]])\n"
     ]
    }
   ],
   "source": [
    "y = x.view(6)\n",
    "z = x.view(3, 2)\n",
    "print(x.size(), y.size(), z.size())\n",
    "print(x, y, z)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[ 1.7645,  1.6439,  1.2719],\n",
      "        [-0.4059, -0.5806, -0.6770]])\n",
      "tensor([2.7645, 2.6439, 2.2719, 0.5941, 0.4194, 0.3230])\n"
     ]
    }
   ],
   "source": [
    "x_cp = x.clone().view(6)\n",
    "x -= 1\n",
    "print(x)\n",
    "print(x_cp)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[1, 2]])\n",
      "tensor([[1],\n",
      "        [2],\n",
      "        [3]])\n",
      "tensor([[2, 3],\n",
      "        [3, 4],\n",
      "        [4, 5]])\n"
     ]
    }
   ],
   "source": [
    "x = torch.arange(1, 3).view(1, 2)\n",
    "print(x)\n",
    "y = torch.arange(1, 4).view(3, 1)\n",
    "print(y)\n",
    "print(x + y)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([1., 1., 1.]) [1. 1. 1.]\n"
     ]
    }
   ],
   "source": [
    "a = torch.ones(3)\n",
    "b = a.numpy()\n",
    "print(a, b)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([3., 3., 3.]) [3. 3. 3.]\n",
      "tensor([4., 4., 4.]) [4. 4. 4.]\n"
     ]
    }
   ],
   "source": [
    "a += 1\n",
    "print(a, b)\n",
    "b += 1\n",
    "print(a, b)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1. 1. 1.] tensor([1., 1., 1.], dtype=torch.float64)\n",
      "[2. 2. 2.] tensor([2., 2., 2.], dtype=torch.float64)\n",
      "[3. 3. 3.] tensor([3., 3., 3.], dtype=torch.float64)\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "a = np.ones(3)\n",
    "b = torch.from_numpy(a)\n",
    "print(a, b)\n",
    "\n",
    "a += 1\n",
    "print(a, b)\n",
    "b += 1\n",
    "print(a, b)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[4. 4. 4.] tensor([3., 3., 3.], dtype=torch.float64)\n"
     ]
    }
   ],
   "source": [
    "c = torch.tensor(a)\n",
    "a += 1\n",
    "print(a, c)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[2, 3]], device='cuda:0')\n",
      "tensor([[2., 3.]], dtype=torch.float64)\n"
     ]
    }
   ],
   "source": [
    "if torch.cuda.is_available():\n",
    "    device = torch.device(\"cuda\")\n",
    "    y = torch.ones_like(x, device=device)\n",
    "    x = x.to(device)\n",
    "    z = x + y\n",
    "    print(z)\n",
    "    print(z.to(\"cpu\", torch.double))"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[1., 1.],\n",
      "        [1., 1.]], requires_grad=True)\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "x = torch.ones(2, 2, requires_grad=True)\n",
    "print(x)\n",
    "print(x.grad_fn)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[3., 3.],\n",
      "        [3., 3.]], grad_fn=<AddBackward0>)\n",
      "<AddBackward0 object at 0x000002494CEEC220>\n"
     ]
    }
   ],
   "source": [
    "y = x + 2\n",
    "print(y)\n",
    "print(y.grad_fn)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[27., 27.],\n",
      "        [27., 27.]], grad_fn=<MulBackward0>)\n",
      "tensor(27., grad_fn=<MeanBackward0>)\n"
     ]
    }
   ],
   "source": [
    "z = y * y * 3\n",
    "out = z.mean()\n",
    "print(z)\n",
    "print(out)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "False\n",
      "True\n",
      "<SumBackward0 object at 0x000002494E507AC0>\n"
     ]
    }
   ],
   "source": [
    "a = torch.randn(2, 2)\n",
    "a = ((a * 3) / (a - 1))\n",
    "print(a.requires_grad)\n",
    "a.requires_grad_(True)\n",
    "print(a.requires_grad)\n",
    "b = (a * a).sum()\n",
    "print(b.grad_fn)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[4.5000, 4.5000],\n",
      "        [4.5000, 4.5000]])\n"
     ]
    }
   ],
   "source": [
    "x = torch.ones(2, 2, requires_grad=True)\n",
    "y = x + 2\n",
    "z = y * y * 3\n",
    "out = z.mean()\n",
    "out.backward()\n",
    "print(x.grad)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[8.5000, 8.5000],\n",
      "        [8.5000, 8.5000]])\n"
     ]
    }
   ],
   "source": [
    "out2 = x.sum()\n",
    "out2.backward()\n",
    "print(x.grad)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[1., 1.],\n",
      "        [1., 1.]])\n"
     ]
    }
   ],
   "source": [
    "out3 = x.sum()\n",
    "x.grad.data.zero_()\n",
    "out3.backward()\n",
    "print(x.grad)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[2., 4.],\n",
      "        [6., 8.]], grad_fn=<ViewBackward0>)\n"
     ]
    }
   ],
   "source": [
    "x = torch.tensor([1.0, 2.0, 3.0, 4.0], requires_grad=True)\n",
    "y = 2 * x\n",
    "z = y.view(2, 2)\n",
    "print(z)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([2.0000, 0.2000, 0.0200, 0.0020])\n"
     ]
    }
   ],
   "source": [
    "v = torch.tensor([[1.0, 0.1], [0.01, 0.001]], dtype=torch.float)\n",
    "z.backward(v)\n",
    "print(x.grad)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "outputs": [],
   "source": [
    "import torch\n",
    "from IPython import display\n",
    "from matplotlib import pyplot as plt\n",
    "import numpy as np\n",
    "import random"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "outputs": [],
   "source": [
    "num_inputs = 2\n",
    "num_examples = 1000\n",
    "true_w = [2, -3.4]\n",
    "true_b = 4.2\n",
    "features = torch.tensor(np.random.normal(0, 1, (num_examples, num_inputs)), dtype=torch.float)\n",
    "labels = true_w[0] * features[:, 0] + true_w[1] * features[:, 1] + true_b\n",
    "labels += torch.tensor(np.random.normal(0, 0.01, size=labels.size()), dtype=float)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "outputs": [
    {
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     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    },
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     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib_inline.backend_inline\n",
    "def set_figsize(ipython_format, figsize=(3.5, 2.5)):\n",
    "    matplotlib_inline.backend_inline.set_matplotlib_formats('svg')\n",
    "    plt.rcParams['figure.figsize'] = figsize\n",
    "\n",
    "set_figsize('svg')\n",
    "plt.scatter(features[:, 1].numpy(), labels.numpy(), 1)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "outputs": [],
   "source": [
    "def data_iter(batch_size, features, labels):\n",
    "    num_examples = len(features)\n",
    "    indices = list(range(num_examples))\n",
    "    random.shuffle(indices)\n",
    "    for i in range(0, num_examples, batch_size):\n",
    "        j = torch.LongTensor(indices[i: min(i + batch_size, num_examples)]) # 最后一次可能不足一个batch\n",
    "        yield features.index_select(0, j), labels.index_select(0, j)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "outputs": [],
   "source": [
    "w = torch.tensor(np.random.normal(0, 0.01, (num_inputs, 1)), dtype=torch.float32)\n",
    "b = torch.zeros(1, dtype=torch.float32)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "outputs": [
    {
     "data": {
      "text/plain": "tensor([0.], requires_grad=True)"
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "w.requires_grad_(requires_grad=True)\n",
    "b.requires_grad_(requires_grad=True)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "outputs": [],
   "source": [
    "def linreg(X, w, b):\n",
    "    return torch.mm(X, w) + b"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "outputs": [],
   "source": [
    "def squared_loss(y_hat, y):\n",
    "    return (y_hat - y.view(y_hat.size()))**2/2"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "outputs": [],
   "source": [
    "def sgd(params, lr, batch_size):\n",
    "    for param in params:\n",
    "        param.data -= lr * param.grad / batch_size"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch 1, loss 0.042626\n",
      "epoch 2, loss 0.000161\n",
      "epoch 3, loss 0.000049\n"
     ]
    }
   ],
   "source": [
    "lr = 0.03\n",
    "num_epochs = 3\n",
    "batch_size = 10\n",
    "net = linreg\n",
    "loss = squared_loss\n",
    "\n",
    "for epoch in range(num_epochs):\n",
    "    for X, y in data_iter(batch_size, features, labels):\n",
    "        l = loss(net(X, w, b), y).sum()\n",
    "        l.backward()\n",
    "        sgd([w, b], lr, batch_size)\n",
    "        w.grad.data.zero_()\n",
    "        b.grad.data.zero_()\n",
    "    train_l = loss(net(features, w, b), labels)\n",
    "    print('epoch %d, loss %f' % (epoch + 1, train_l.mean().item()))"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2, -3.4] \n",
      " tensor([[ 1.9997],\n",
      "        [-3.3996]], requires_grad=True)\n",
      "4.2 \n",
      " tensor([4.1990], requires_grad=True)\n"
     ]
    }
   ],
   "source": [
    "print(true_w, '\\n', w)\n",
    "print(true_b, '\\n', b)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "outputs": [],
   "source": [
    "lr = 0.03\n",
    "import torch.utils.data as Data\n",
    "batch_size = 10\n",
    "# 将训练数据的特征和标签组合\n",
    "dataset = Data.TensorDataset(features, labels)\n",
    "# 把 dataset 放入 DataLoader\n",
    "data_iter = Data.DataLoader(\n",
    "dataset=dataset, # torch TensorDataset format\n",
    "batch_size=batch_size, # mini batch size\n",
    "shuffle=True, # 是否打乱数据 (训练集一般需要进行打乱)\n",
    "num_workers=0, # 多线程来读数据，注意在Windows下需要设置为0\n",
    ")"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "outputs": [],
   "source": [
    "import torch.nn as nn\n",
    "class LinearNet(nn.Module):\n",
    "    def __init__(self, n_feature):\n",
    "        super(LinearNet, self).__init__()\n",
    "        self.linear = nn.Linear(n_feature, 1)\n",
    "\n",
    "# forward 定义前向传播\n",
    "def forward(self, x):\n",
    "    y = self.linear(x)\n",
    "    return y\n",
    "\n",
    "from collections import OrderedDict\n",
    "net = nn.Sequential(OrderedDict([\n",
    "('linear', nn.Linear(num_inputs, 1))\n",
    "]))"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "outputs": [
    {
     "data": {
      "text/plain": "Parameter containing:\ntensor([0.], requires_grad=True)"
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from torch.nn import init\n",
    "init.normal_(net[0].weight, mean=0, std=0.01)\n",
    "init.constant_(net[0].bias, val=0) #也可以直接修改bias的data：net[0].bias.data.fill_(0)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "outputs": [],
   "source": [
    "loss = nn.MSELoss()"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "outputs": [],
   "source": [
    "import torch.optim as optim\n",
    "optimizer = optim.SGD(net.parameters(), lr=0.03) #梯度下降的学习率指定为0.03\n",
    "# 可以为不同的子网络设置不同学习率\n",
    "# optimizer =optim.SGD([\n",
    "# # 如果不指定学习率，则用默认的最外层学习率\n",
    "# {'params': net.subnet1.parameters()}, # lr=0.03\n",
    "# {'params': net.subnet2.parameters(), 'lr': 0.01}\n",
    "# ], lr=0.03)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch 1, loss: 0.000184\n",
      "epoch 2, loss: 0.000080\n",
      "epoch 3, loss: 0.000027\n"
     ]
    }
   ],
   "source": [
    "num_epochs = 3\n",
    "for epoch in range(1, num_epochs + 1):\n",
    "    for X, y in data_iter:\n",
    "        output = net(X)\n",
    "        l = loss(output, y.view(-1, 1))\n",
    "        optimizer.zero_grad() # 梯度清零，等价于net.zero_grad()\n",
    "        l.backward()\n",
    "        optimizer.step()\n",
    "    print('epoch %d, loss: %f' % (epoch, l.item()))"
   ],
   "metadata": {
    "collapsed": false
   }
  }
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