pytorch编写代码基本步骤思想.ipynb

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   "source": [
    "https://mp.weixin.qq.com/s?__biz=MzIwODI2NDkxNQ==&mid=2247489520&idx=3&sn=c12ffd76f3fd242108443a087acef098&chksm=97049c4ca073155adaa54a85d9fa3eb8aa43f18f194d2a2f13f66d126ceecd38ebe0dc983c05&scene=126&sessionid=1594092662&key=d28e5ef1d5d51675aa0699137fca8e799eb5b83a1b32a26e4603a5d814ee6438aff641d1a285d92f69f2c889a1c2ffba7c9c8709dcbe8baf8858017fbf4ab3a86c695ec02a14f8c7258956bd9d8fa147&ascene=1&uin=MjA1MjAyODkxNg%3D%3D&devicetype=Windows+10+x64&version=62090529&lang=zh_CN&exportkey=ATdwNkFeY2EFmS9J31vKVw0%3D&pass_ticket=8rdE%2BKLs9u4lwx%2BtYJ8ezSC2ox%2BQrg7VmG%2FN6ZdVOUJio32zO7KP3jg0u%2Fa2UCg0"
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "####  数据处理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# torch.utils.data.Dataset 继承Dataset类需要override以下方法：\n",
    "from torch.utils.data import Dataset\n",
    "class trainDataset(Dataset):\n",
    "    def __init__(self):\n",
    "        # constructor\n",
    "        pass\n",
    "    def __getitem__(self,index):\n",
    "        # 获得第index号的数据和标签\n",
    "        pass\n",
    "    def __len__(self):\n",
    "        # 获得数据量\n",
    "        pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn.functional as F"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_data = torch.Tensor([[1.0],[2.0],[3.0]])\n",
    "y_data = torch.Tensor([[0],[0],[1]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "class LogisticRegressionModel(torch.nn.Module):\n",
    "    def __init__(self):\n",
    "        super(LogisticRegressionModel,self).__init__()\n",
    "        self.liner = torch.nn.Linear(1,1)\n",
    "    def forward(self,x):\n",
    "        y_pred = F.sigmoid(self.liner(x))\n",
    "        return y_pred\n",
    "model = LogisticRegressionModel()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\jianghaitao1\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\torch\\nn\\_reduction.py:43: UserWarning: size_average and reduce args will be deprecated, please use reduction='sum' instead.\n",
      "  warnings.warn(warning.format(ret))\n"
     ]
    }
   ],
   "source": [
    "criterion = torch.nn.BCELoss(size_average=False)\n",
    "optimizer = torch.optim.SGD(model.parameters(),lr=0.01)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\jianghaitao1\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\torch\\nn\\functional.py:1350: UserWarning: nn.functional.sigmoid is deprecated. Use torch.sigmoid instead.\n",
      "  warnings.warn(\"nn.functional.sigmoid is deprecated. Use torch.sigmoid instead.\")\n"
     ]
    },
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     ]
    }
   ],
   "source": [
    "for epoch in range(1000):\n",
    "    y_pred = model(x_data)\n",
    "    loss = criterion(y_pred,y_data)\n",
    "    print(epoch,loss.item())\n",
    "    optimizer.zero_grad()\n",
    "    loss.backward()\n",
    "    optimizer.step()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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