[pytorch 分层设置学习率] #pytorch

per-layer-lr.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "分层设置学习率"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "import torch.optim as optim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.4.0\n"
     ]
    }
   ],
   "source": [
    "print(torch.__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 构建一个简单多层网络结构\n",
    "class TwoLayerNet(torch.nn.Module):\n",
    "    def __init__(self, D_in, H, D_out):\n",
    "        \"\"\"\n",
    "        In the constructor we instantiate two nn.Linear modules and assign them as\n",
    "        member variables.\n",
    "        \"\"\"\n",
    "        super(TwoLayerNet, self).__init__()\n",
    "        self.linear1 = torch.nn.Linear(D_in, H)\n",
    "        self.linear2 = torch.nn.Linear(H, D_out)\n",
    "\n",
    "    def forward(self, x):\n",
    "        \"\"\"\n",
    "        In the forward function we accept a Tensor of input data and we must return\n",
    "        a Tensor of output data. We can use Modules defined in the constructor as\n",
    "        well as arbitrary operators on Tensors.\n",
    "        \"\"\"\n",
    "        h_relu = F.relu(self.linear1(x))\n",
    "        y_pred = self.linear2(h_relu)\n",
    "        return y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# N is batch size; D_in is input dimension;\n",
    "# H is hidden dimension; D_out is output dimension.\n",
    "N, D_in, H, D_out = 64, 1000, 100, 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = torch.randn(N, D_in)\n",
    "y = torch.randn(N, D_out)\n",
    "\n",
    "# Construct our model by instantiating the class defined above\n",
    "model = TwoLayerNet(D_in, H, D_out)\n",
    "\n",
    "# Construct our loss function and an Optimizer. The call to model.parameters()\n",
    "# in the SGD constructor will contain the learnable parameters of the two\n",
    "# nn.Linear modules which are members of the model.\n",
    "criterion = torch.nn.MSELoss(size_average=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "查看模型的参数名称"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "参数名: linear1.weight    , id: 140705409071936\n",
      "参数名: linear1.bias      , id: 140705409072008\n",
      "参数名: linear2.weight    , id: 140705409072296\n",
      "参数名: linear2.bias      , id: 140705409072656\n"
     ]
    }
   ],
   "source": [
    "for pname, p in model.named_parameters():\n",
    "    print('参数名: {: <18}, id: {}'.format(pname, id(p)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_parameters = model.parameters()\n",
    "\n",
    "lin1_parameters = []\n",
    "for pname, p in model.named_parameters():\n",
    "    if pname.find('linear1') >= 0:\n",
    "        lin1_parameters.append(p)\n",
    "\n",
    "lin1_parameters_id = list(map(id, lin1_parameters))\n",
    "other_parameters = list(filter(lambda p: id(p) not in lin1_parameters_id,\n",
    "                           all_parameters))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "现在获得了两组参数,一组是linear1,另一组是其他的(本程序中即linear2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "linear1组参数id: \n",
      "140705409071936\n",
      "140705409072008\n",
      "\n",
      "\n",
      "other组参数id: \n",
      "140705409072296\n",
      "140705409072656\n"
     ]
    }
   ],
   "source": [
    "print('linear1组参数id: ')\n",
    "for p in lin1_parameters:\n",
    "    print(id(p))\n",
    "    \n",
    "print('\\n')\n",
    "print('other组参数id: ')\n",
    "for p in other_parameters:\n",
    "    print(id(p))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "构造optim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "optimizer = optim.SGD([\n",
    "                {'params': lin1_parameters},\n",
    "                {'params': other_parameters, 'lr': 1e-3}\n",
    "            ], lr=1e-4)\n",
    "# linear1层的学习率1e-4,其它层1e-3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "训练网络"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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     ]
    }
   ],
   "source": [
    "for t in range(500):\n",
    "    # Forward pass: Compute predicted y by passing x to the model\n",
    "    y_pred = model(x)\n",
    "\n",
    "    # Compute and print loss\n",
    "    loss = criterion(y_pred, y)\n",
    "    print(t, loss.item())\n",
    "\n",
    "    # Zero gradients, perform a backward pass, and update the weights.\n",
    "    optimizer.zero_grad()\n",
    "    loss.backward()\n",
    "    optimizer.step()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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