{
 "cells": [
  {
   "cell_type": "code",
   "id": "initial_id",
   "metadata": {
    "collapsed": true,
    "ExecuteTime": {
     "end_time": "2026-03-22T08:08:29.467878519Z",
     "start_time": "2026-03-22T08:08:28.169276694Z"
    }
   },
   "source": [
    "import d2l\n",
    "import torch\n",
    "import d2l\n",
    "import numpy\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F"
   ],
   "outputs": [],
   "execution_count": 1
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-03-22T08:08:29.562521238Z",
     "start_time": "2026-03-22T08:08:29.470022979Z"
    }
   },
   "cell_type": "code",
   "source": [
    "net = nn.Sequential(nn.Linear(20, 256), nn.ReLU(), nn.Linear(256, 10))\n",
    "X = torch.rand(2, 20)\n",
    "net(X)"
   ],
   "id": "dcd5590e7795eec1",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[ 0.0362,  0.0737, -0.0211,  0.0666, -0.1115,  0.0158, -0.1162,  0.0884,\n",
       "          0.1486, -0.1063],\n",
       "        [ 0.1796, -0.0009,  0.1236, -0.0783, -0.0937, -0.0560,  0.0441,  0.0812,\n",
       "          0.2236, -0.0597]], grad_fn=<AddmmBackward0>)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 2
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-03-22T08:08:29.719824050Z",
     "start_time": "2026-03-22T08:08:29.644546772Z"
    }
   },
   "cell_type": "code",
   "source": [
    "class MLP(nn.Module):\n",
    "    def __init__(self):\n",
    "        super().__init__()\n",
    "        self.hidden=nn.Linear(20,256)\n",
    "        self.out=nn.Linear(256,10)\n",
    "    def forward(self,X):\n",
    "        return self.out(F.relu(self.hidden(X)))\n"
   ],
   "id": "4ae330604b643cb4",
   "outputs": [],
   "execution_count": 3
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-03-22T08:08:29.957480273Z",
     "start_time": "2026-03-22T08:08:29.789396471Z"
    }
   },
   "cell_type": "code",
   "source": [
    "net=MLP()\n",
    "net(X)"
   ],
   "id": "cca55c6c0c7da12f",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[ 0.0376, -0.2522, -0.0243, -0.0838,  0.1215,  0.0258, -0.2358,  0.0799,\n",
       "          0.0756,  0.0520],\n",
       "        [ 0.0098, -0.2070,  0.0638,  0.1173,  0.0275,  0.0116, -0.0448, -0.0448,\n",
       "         -0.0309, -0.0976]], grad_fn=<AddmmBackward0>)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 4
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-03-22T08:08:30.177898340Z",
     "start_time": "2026-03-22T08:08:30.069505281Z"
    }
   },
   "cell_type": "code",
   "source": [
    "class FixedHiddenMLP(nn.Module):\n",
    "    def __init__(self):\n",
    "        super().__init__()\n",
    "        # 不计算梯度的随机权重参数。因此其在训练期间保持不变\n",
    "        self.rand_weight = torch.rand((20, 20), requires_grad=False)\n",
    "        self.linear = nn.Linear(20, 20)\n",
    "    def forward(self, X):\n",
    "        X = self.linear(X)\n",
    "        # 使用创建的常量参数以及relu和mm函数\n",
    "        X = F.relu(torch.mm(X, self.rand_weight) + 1)\n",
    "        # 复用全连接层。这相当于两个全连接层共享参数\n",
    "        X = self.linear(X)\n",
    "        # 控制流\n",
    "        while X.abs().sum() > 1:\n",
    "            X /= 2\n",
    "        return X.sum()"
   ],
   "id": "4518d62611d5e749",
   "outputs": [],
   "execution_count": 5
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-03-22T08:08:30.414648562Z",
     "start_time": "2026-03-22T08:08:30.251182946Z"
    }
   },
   "cell_type": "code",
   "source": [
    "net = FixedHiddenMLP()\n",
    "net(X)"
   ],
   "id": "fae0187ece4ed5c6",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor(0.1704, grad_fn=<SumBackward0>)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 6
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-03-22T08:08:30.514145240Z",
     "start_time": "2026-03-22T08:08:30.426612891Z"
    }
   },
   "cell_type": "code",
   "source": [
    "class NestMLP(nn.Module):\n",
    "    def __init__(self):\n",
    "        super().__init__()\n",
    "        self.net = nn.Sequential(nn.Linear(20, 64), nn.ReLU(),\n",
    "                                    nn.Linear(64, 32), nn.ReLU())\n",
    "        self.linear = nn.Linear(32, 16)\n",
    "    def forward(self, X):\n",
    "        return self.linear(self.net(X))\n",
    "        chimera = nn.Sequential(NestMLP(), nn.Linear(16, 20), FixedHiddenMLP())\n",
    "        chimera(X)"
   ],
   "id": "407ef13a86453aae",
   "outputs": [],
   "execution_count": 7
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-03-22T08:08:30.617747011Z",
     "start_time": "2026-03-22T08:08:30.517586238Z"
    }
   },
   "cell_type": "code",
   "source": [
    "net = nn.Sequential(nn.Linear(4, 8), nn.ReLU(), nn.Linear(8, 1))\n",
    "X = torch.rand(size=(2, 4))\n",
    "net(X)"
   ],
   "id": "9f3526f263c7a249",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[-0.2445],\n",
       "        [-0.2901]], grad_fn=<AddmmBackward0>)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 8
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-03-22T08:08:30.825671299Z",
     "start_time": "2026-03-22T08:08:30.691388202Z"
    }
   },
   "cell_type": "code",
   "source": "print(net[2].state_dict())",
   "id": "8c73f8daa02ba28b",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "OrderedDict([('weight', tensor([[-0.2116,  0.3448,  0.0726, -0.0626, -0.2922,  0.3172,  0.3025, -0.3025]])), ('bias', tensor([-0.3315]))])\n"
     ]
    }
   ],
   "execution_count": 9
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-03-22T08:08:31.020445691Z",
     "start_time": "2026-03-22T08:08:30.902305394Z"
    }
   },
   "cell_type": "code",
   "source": "net[2].state_dict()",
   "id": "b6fee6b64fb96e3c",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "OrderedDict([('weight',\n",
       "              tensor([[-0.2116,  0.3448,  0.0726, -0.0626, -0.2922,  0.3172,  0.3025, -0.3025]])),\n",
       "             ('bias', tensor([-0.3315]))])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 10
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-03-22T08:08:31.127350738Z",
     "start_time": "2026-03-22T08:08:31.055037575Z"
    }
   },
   "cell_type": "code",
   "source": "print(type(net[2].bias))",
   "id": "b38e8dc384e038c5",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'torch.nn.parameter.Parameter'>\n"
     ]
    }
   ],
   "execution_count": 11
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-03-22T08:08:31.206008645Z",
     "start_time": "2026-03-22T08:08:31.139239226Z"
    }
   },
   "cell_type": "code",
   "source": [
    "print(net[2].bias)\n",
    "print(net[2].bias.data)\n"
   ],
   "id": "73f12ca3669d9ede",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameter containing:\n",
      "tensor([-0.3315], requires_grad=True)\n",
      "tensor([-0.3315])\n"
     ]
    }
   ],
   "execution_count": 12
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-03-22T08:08:31.286245677Z",
     "start_time": "2026-03-22T08:08:31.230728228Z"
    }
   },
   "cell_type": "code",
   "source": "net[2].weight.grad==None",
   "id": "db0fe33018c16fac",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 13
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-03-22T08:08:31.385905750Z",
     "start_time": "2026-03-22T08:08:31.307506282Z"
    }
   },
   "cell_type": "code",
   "source": [
    "print(*[(name, param.shape) for name, param in net[0].named_parameters()])\n",
    "print(*[(name, param.shape) for name, param in net.named_parameters()])"
   ],
   "id": "75847a1c608ee5c7",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('weight', torch.Size([8, 4])) ('bias', torch.Size([8]))\n",
      "('0.weight', torch.Size([8, 4])) ('0.bias', torch.Size([8])) ('2.weight', torch.Size([1, 8])) ('2.bias', torch.Size([1]))\n"
     ]
    }
   ],
   "execution_count": 14
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-03-22T08:08:31.470489659Z",
     "start_time": "2026-03-22T08:08:31.391689979Z"
    }
   },
   "cell_type": "code",
   "source": "net.state_dict()['2.bias'].data",
   "id": "cc74913e8742da7d",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([-0.3315])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 15
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-03-22T08:08:31.522581812Z",
     "start_time": "2026-03-22T08:08:31.482437170Z"
    }
   },
   "cell_type": "code",
   "source": [
    "def block1():\n",
    "    return nn.Sequential(nn.Linear(4, 8), nn.ReLU(), nn.Linear(8, 4),nn.ReLU())\n",
    "def block2():\n",
    "    net = nn.Sequential()\n",
    "    for i in range(4):\n",
    "        net.add_module(f'block{i}', block1())\n",
    "    return net"
   ],
   "id": "53c39c5e61fa7bf5",
   "outputs": [],
   "execution_count": 16
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-03-22T08:08:31.643076590Z",
     "start_time": "2026-03-22T08:08:31.558403449Z"
    }
   },
   "cell_type": "code",
   "source": [
    "rgnet = nn.Sequential(block2(),nn.Linear(4,1))\n",
    "rgnet(X)"
   ],
   "id": "d3ac7759b619aca",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[-0.3640],\n",
       "        [-0.3640]], grad_fn=<AddmmBackward0>)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 17
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-03-22T08:08:31.860114658Z",
     "start_time": "2026-03-22T08:08:31.722546330Z"
    }
   },
   "cell_type": "code",
   "source": "print(rgnet)",
   "id": "8fc60f64b07781e6",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sequential(\n",
      "  (0): Sequential(\n",
      "    (block0): Sequential(\n",
      "      (0): Linear(in_features=4, out_features=8, bias=True)\n",
      "      (1): ReLU()\n",
      "      (2): Linear(in_features=8, out_features=4, bias=True)\n",
      "      (3): ReLU()\n",
      "    )\n",
      "    (block1): Sequential(\n",
      "      (0): Linear(in_features=4, out_features=8, bias=True)\n",
      "      (1): ReLU()\n",
      "      (2): Linear(in_features=8, out_features=4, bias=True)\n",
      "      (3): ReLU()\n",
      "    )\n",
      "    (block2): Sequential(\n",
      "      (0): Linear(in_features=4, out_features=8, bias=True)\n",
      "      (1): ReLU()\n",
      "      (2): Linear(in_features=8, out_features=4, bias=True)\n",
      "      (3): ReLU()\n",
      "    )\n",
      "    (block3): Sequential(\n",
      "      (0): Linear(in_features=4, out_features=8, bias=True)\n",
      "      (1): ReLU()\n",
      "      (2): Linear(in_features=8, out_features=4, bias=True)\n",
      "      (3): ReLU()\n",
      "    )\n",
      "  )\n",
      "  (1): Linear(in_features=4, out_features=1, bias=True)\n",
      ")\n"
     ]
    }
   ],
   "execution_count": 18
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-03-22T08:08:32.103301882Z",
     "start_time": "2026-03-22T08:08:31.980555778Z"
    }
   },
   "cell_type": "code",
   "source": "rgnet[0][1][0].bias.data",
   "id": "e590aaafca787b50",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([ 0.3672, -0.3124, -0.3113, -0.3251, -0.4771, -0.3622,  0.1464, -0.4632])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 19
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-03-22T08:08:32.231455211Z",
     "start_time": "2026-03-22T08:08:32.137730392Z"
    }
   },
   "cell_type": "code",
   "source": [
    "def init_normal(m):\n",
    "    if type(m) == nn.Linear:\n",
    "        nn.init.normal_(m.weight, mean=0, std=0.01)\n",
    "        nn.init.zeros_(m.bias)\n",
    "net.apply(init_normal)\n",
    "net[0].weight.data[0], net[0].bias.data[0]"
   ],
   "id": "925ca33221d0a87e",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(tensor([-0.0004,  0.0166, -0.0085, -0.0099]), tensor(0.))"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 20
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-03-22T08:08:32.322842445Z",
     "start_time": "2026-03-22T08:08:32.234982576Z"
    }
   },
   "cell_type": "code",
   "source": [
    "def init_xavier(m):\n",
    "    if type(m) == nn.Linear:\n",
    "        nn.init.xavier_uniform_(m.weight)\n",
    "def init_42(m):\n",
    "    if type(m) == nn.Linear:\n",
    "        nn.init.constant_(m.weight, 42)\n",
    "\n",
    "net[0].apply(init_xavier)\n",
    "net[2].apply(init_42)\n",
    "print(net[0].weight.data[0])\n",
    "print(net[2].weight.data)"
   ],
   "id": "81e2de84a8c4ef32",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([-0.3265, -0.5057, -0.5062, -0.2116])\n",
      "tensor([[42., 42., 42., 42., 42., 42., 42., 42.]])\n"
     ]
    }
   ],
   "execution_count": 21
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-03-22T08:08:32.377993392Z",
     "start_time": "2026-03-22T08:08:32.324885649Z"
    }
   },
   "cell_type": "code",
   "source": [
    "x = torch.arange(4)\n",
    "torch.save(x, 'x-file')"
   ],
   "id": "f05bb378bb60ab9e",
   "outputs": [],
   "execution_count": 22
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-03-22T08:08:32.474581135Z",
     "start_time": "2026-03-22T08:08:32.386815096Z"
    }
   },
   "cell_type": "code",
   "source": [
    "x2 = torch.load('x-file')\n",
    "x2"
   ],
   "id": "a74ecaaac0d826c6",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([0, 1, 2, 3])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 23
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-03-22T08:08:32.492632663Z",
     "start_time": "2026-03-22T08:08:32.476644136Z"
    }
   },
   "cell_type": "code",
   "source": [
    "class MLP(nn.Module):\n",
    "    def __init__(self):\n",
    "        super().__init__()\n",
    "        self.hidden = nn.Linear(20, 256)\n",
    "        self.output = nn.Linear(256, 10)\n",
    "    def forward(self, x):\n",
    "        return self.output(F.relu(self.hidden(x)))\n",
    "\n",
    "net = MLP()\n",
    "X = torch.randn(size=(2, 20))\n",
    "Y = net(X)"
   ],
   "id": "b42598f0c4a8e801",
   "outputs": [],
   "execution_count": 24
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-03-22T08:08:32.600312816Z",
     "start_time": "2026-03-22T08:08:32.528496997Z"
    }
   },
   "cell_type": "code",
   "source": "torch.save(net.state_dict(), 'mlp.params')",
   "id": "aaa22eef549caa6f",
   "outputs": [],
   "execution_count": 25
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-03-22T08:08:32.697771732Z",
     "start_time": "2026-03-22T08:08:32.616231856Z"
    }
   },
   "cell_type": "code",
   "source": [
    "clone = MLP()\n",
    "clone.load_state_dict(torch.load('mlp.params'))\n",
    "clone.eval()"
   ],
   "id": "b92f920229abeeae",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MLP(\n",
       "  (hidden): Linear(in_features=20, out_features=256, bias=True)\n",
       "  (output): Linear(in_features=256, out_features=10, bias=True)\n",
       ")"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 26
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-03-22T08:08:32.782119612Z",
     "start_time": "2026-03-22T08:08:32.715175504Z"
    }
   },
   "cell_type": "code",
   "source": [
    "Y_clone = clone(X)\n",
    "Y_clone == Y"
   ],
   "id": "646c9eb6d7cc81c2",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[True, True, True, True, True, True, True, True, True, True],\n",
       "        [True, True, True, True, True, True, True, True, True, True]])"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 27
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-03-22T08:08:32.908809253Z",
     "start_time": "2026-03-22T08:08:32.843784246Z"
    }
   },
   "cell_type": "code",
   "source": [
    "def corr2d(X,K):\n",
    "    h,w=K.shape\n",
    "    Y=torch.ones((X.shape[0]-h+1,X.shape[1]-w+1))\n",
    "    for i in range(Y.shape[0]):\n",
    "        for j in range(Y.shape[1]):\n",
    "            Y[i,j]=(X[i:i+h,j:j+w]*K).sum()\n",
    "    return Y\n"
   ],
   "id": "d45f9adfe47fce20",
   "outputs": [],
   "execution_count": 28
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-03-22T08:08:33.174646764Z",
     "start_time": "2026-03-22T08:08:33.092115317Z"
    }
   },
   "cell_type": "code",
   "source": [
    "X = torch.tensor([[0.0, 1.0, 2.0], [3.0, 4.0, 5.0], [6.0, 7.0, 8.0]])\n",
    "K = torch.tensor([[0.0, 1.0], [2.0, 3.0]])\n",
    "corr2d(X,K)"
   ],
   "id": "db7279e13647c315",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[19., 25.],\n",
       "        [37., 43.]])"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 29
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-03-22T08:08:33.292523218Z",
     "start_time": "2026-03-22T08:08:33.234892901Z"
    }
   },
   "cell_type": "code",
   "source": [
    "class Conv2D(nn.Module):\n",
    "    def __init__(self, kernel_size):\n",
    "        super().__init__()\n",
    "        self.weight = nn.Parameter(torch.rand(kernel_size))\n",
    "        self.bias = nn.Parameter(torch.zeros(1))\n",
    "    def forward(self, x):\n",
    "        return corr2d(x, self.weight) + self.bias\n"
   ],
   "id": "d60be1bd12a1f37e",
   "outputs": [],
   "execution_count": 30
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-03-22T08:08:33.497922435Z",
     "start_time": "2026-03-22T08:08:33.387838879Z"
    }
   },
   "cell_type": "code",
   "source": [
    "X = torch.ones((6, 8))\n",
    "X[:, 2:6] = 0\n",
    "X"
   ],
   "id": "5083789b7a728442",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[1., 1., 0., 0., 0., 0., 1., 1.],\n",
       "        [1., 1., 0., 0., 0., 0., 1., 1.],\n",
       "        [1., 1., 0., 0., 0., 0., 1., 1.],\n",
       "        [1., 1., 0., 0., 0., 0., 1., 1.],\n",
       "        [1., 1., 0., 0., 0., 0., 1., 1.],\n",
       "        [1., 1., 0., 0., 0., 0., 1., 1.]])"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 31
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-03-22T08:08:33.751910360Z",
     "start_time": "2026-03-22T08:08:33.561038017Z"
    }
   },
   "cell_type": "code",
   "source": [
    "K = torch.tensor([[1.0, -1.0]])\n",
    "Y = corr2d(X, K)\n",
    "Y"
   ],
   "id": "ee8d6bedbde886ad",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[ 0.,  1.,  0.,  0.,  0., -1.,  0.],\n",
       "        [ 0.,  1.,  0.,  0.,  0., -1.,  0.],\n",
       "        [ 0.,  1.,  0.,  0.,  0., -1.,  0.],\n",
       "        [ 0.,  1.,  0.,  0.,  0., -1.,  0.],\n",
       "        [ 0.,  1.,  0.,  0.,  0., -1.,  0.],\n",
       "        [ 0.,  1.,  0.,  0.,  0., -1.,  0.]])"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 32
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-03-22T08:08:34.156019672Z",
     "start_time": "2026-03-22T08:08:33.891686033Z"
    }
   },
   "cell_type": "code",
   "source": "corr2d(X.t(), K)",
   "id": "a8278c3837fa9a1c",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[0., 0., 0., 0., 0.],\n",
       "        [0., 0., 0., 0., 0.],\n",
       "        [0., 0., 0., 0., 0.],\n",
       "        [0., 0., 0., 0., 0.],\n",
       "        [0., 0., 0., 0., 0.],\n",
       "        [0., 0., 0., 0., 0.],\n",
       "        [0., 0., 0., 0., 0.],\n",
       "        [0., 0., 0., 0., 0.]])"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 33
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-03-22T08:08:34.438245033Z",
     "start_time": "2026-03-22T08:08:34.313636464Z"
    }
   },
   "cell_type": "code",
   "source": "conv2d = nn.Conv2d(1,1, kernel_size=(1, 2), bias=False)",
   "id": "ec61cdb61a8cabff",
   "outputs": [],
   "execution_count": 34
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-03-22T08:08:34.619825611Z",
     "start_time": "2026-03-22T08:08:34.507329239Z"
    }
   },
   "cell_type": "code",
   "source": [
    "X = X.reshape((1, 1, 6, 8))\n",
    "Y = Y.reshape((1, 1, 6, 7))\n",
    "lr = 3e-2"
   ],
   "id": "d2fc19d84c79a10",
   "outputs": [],
   "execution_count": 35
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-03-22T08:08:35.980584379Z",
     "start_time": "2026-03-22T08:08:34.640606389Z"
    }
   },
   "cell_type": "code",
   "source": [
    "for i in range(100):\n",
    "    Y_hat = conv2d(X)\n",
    "    l = (Y_hat - Y) ** 2\n",
    "    conv2d.zero_grad()\n",
    "    l.sum().backward()\n",
    "    # 迭代卷积核\n",
    "    conv2d.weight.data[:] -= lr * conv2d.weight.grad\n",
    "    if (i + 1) % 20 == 0:\n",
    "        print(f'epoch {i+1}, loss {l.sum():.3f}')"
   ],
   "id": "51fbb2e6398a9bd5",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch 20, loss 0.000\n",
      "epoch 40, loss 0.000\n",
      "epoch 60, loss 0.000\n",
      "epoch 80, loss 0.000\n",
      "epoch 100, loss 0.000\n"
     ]
    }
   ],
   "execution_count": 36
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-03-22T08:08:36.147166039Z",
     "start_time": "2026-03-22T08:08:36.070619821Z"
    }
   },
   "cell_type": "code",
   "source": "conv2d.weight.data.reshape((1, 2))\n",
   "id": "bf53a423f429dfe4",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[ 1.0000, -1.0000]])"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 37
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-03-22T08:08:36.349805416Z",
     "start_time": "2026-03-22T08:08:36.243749022Z"
    }
   },
   "cell_type": "code",
   "source": [
    "\n",
    "# 为了方便起见，我们定义了一个计算卷积层的函数。\n",
    "# 此函数初始化卷积层权重，并对输入和输出提高和缩减相应的维数\n",
    "def comp_conv2d(conv2d, X):\n",
    "# 这里的（1，1）表示批量大小和通道数都是1\n",
    "    X = X.reshape((1, 1) + X.shape)\n",
    "    Y = conv2d(X)\n",
    "    # 省略前两个维度：批量大小和通道\n",
    "    return Y.reshape(Y.shape[2:])\n",
    "# 请注意，这里每边都填充了1行或1列，因此总共添加了2行或2列\n",
    "conv2d = nn.Conv2d(1, 1, kernel_size=3, padding=1)"
   ],
   "id": "77b61d8c9a2363cc",
   "outputs": [],
   "execution_count": 38
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-03-22T08:08:36.565573176Z",
     "start_time": "2026-03-22T08:08:36.473905736Z"
    }
   },
   "cell_type": "code",
   "source": [
    "X = torch.rand(size=(8, 8))\n",
    "comp_conv2d(conv2d, X).shape"
   ],
   "id": "beda6ffa67ec2677",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([8, 8])"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 39
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-03-22T08:08:36.629064391Z",
     "start_time": "2026-03-22T08:08:36.577410135Z"
    }
   },
   "cell_type": "code",
   "source": [
    "conv2d = nn.Conv2d(1, 1, kernel_size=(5, 3), padding=(2, 1))\n",
    "comp_conv2d(conv2d, X).shape"
   ],
   "id": "8c51095daea1432d",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([8, 8])"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 40
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-03-22T08:08:36.778747354Z",
     "start_time": "2026-03-22T08:08:36.631642133Z"
    }
   },
   "cell_type": "code",
   "source": [
    "conv2d = nn.Conv2d(1, 1, kernel_size=3, padding=1, stride=2)\n",
    "comp_conv2d(conv2d, X).shape"
   ],
   "id": "581bf1b15162cbf6",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([4, 4])"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 41
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-03-22T08:08:36.912486341Z",
     "start_time": "2026-03-22T08:08:36.816037554Z"
    }
   },
   "cell_type": "code",
   "source": [
    "conv2d = nn.Conv2d(1, 1, kernel_size=(3, 5), padding=(0, 1), stride=(3, 4))\n",
    "comp_conv2d(conv2d, X).shape"
   ],
   "id": "6f7a2411247baff0",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([2, 2])"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 42
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-03-22T08:09:37.230541174Z",
     "start_time": "2026-03-22T08:09:37.139260256Z"
    }
   },
   "cell_type": "code",
   "source": [
    "def corr2d_multi_in(X,K):\n",
    "    return sum(corr2d(x,k) for x,k in zip(X,K))\n",
    "X = torch.tensor([[[0.0, 1.0, 2.0], [3.0, 4.0, 5.0], [6.0, 7.0, 8.0]],\n",
    "[[1.0, 2.0, 3.0], [4.0, 5.0, 6.0], [7.0, 8.0, 9.0]]])\n",
    "K = torch.tensor([[[0.0, 1.0], [2.0, 3.0]], [[1.0, 2.0], [3.0, 4.0]]])\n",
    "corr2d_multi_in(X, K)"
   ],
   "id": "7ac0f17f97b2daa8",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[ 56.,  72.],\n",
       "        [104., 120.]])"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 50
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-03-22T08:09:01.390798830Z",
     "start_time": "2026-03-22T08:09:01.334900206Z"
    }
   },
   "cell_type": "code",
   "source": [
    "def corr2d_multi_in_out(X,K) ->torch.Tensor :\n",
    "    return torch.stack([corr2d_multi_in(X,k) for k in K],0)\n"
   ],
   "id": "d409110d0d6b4b49",
   "outputs": [],
   "execution_count": 47
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-03-22T08:09:39.731392821Z",
     "start_time": "2026-03-22T08:09:39.608604541Z"
    }
   },
   "cell_type": "code",
   "source": [
    "K = torch.stack((K, K + 1, K + 2), 0)\n",
    "K.shape"
   ],
   "id": "4114cd871a627075",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([3, 2, 2, 2])"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 51
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-03-22T08:09:43.297502870Z",
     "start_time": "2026-03-22T08:09:43.186648920Z"
    }
   },
   "cell_type": "code",
   "source": "corr2d_multi_in_out(X, K)",
   "id": "ce52f41dc9585f8c",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[[ 56.,  72.],\n",
       "         [104., 120.]],\n",
       "\n",
       "        [[ 76., 100.],\n",
       "         [148., 172.]],\n",
       "\n",
       "        [[ 96., 128.],\n",
       "         [192., 224.]]])"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 52
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-03-22T08:23:20.460754568Z",
     "start_time": "2026-03-22T08:23:20.424813665Z"
    }
   },
   "cell_type": "code",
   "source": [
    "def corr2d_multi_in_out_1x1(X, K):\n",
    "    h_i,h,w=X.shape\n",
    "    h_o=K.shape[0]\n",
    "    X=X.reshape((h_i,h*w))\n",
    "    print(X.shape)\n",
    "    K=K.reshape((h_o,h_i))\n",
    "    print(K.shape)\n",
    "    Y=torch.matmul(K,X)\n",
    "    return Y.reshape((h_o,h,w))"
   ],
   "id": "362d8c692b3c1d75",
   "outputs": [],
   "execution_count": 56
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-03-22T08:23:21.690978973Z",
     "start_time": "2026-03-22T08:23:21.638506037Z"
    }
   },
   "cell_type": "code",
   "source": [
    "X = torch.normal(0, 1, (3, 3, 3))\n",
    "K = torch.normal(0, 1, (2, 3, 1, 1))"
   ],
   "id": "28e761f677df8b16",
   "outputs": [],
   "execution_count": 57
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-03-22T08:23:22.844449890Z",
     "start_time": "2026-03-22T08:23:22.694694019Z"
    }
   },
   "cell_type": "code",
   "source": "Y1 = corr2d_multi_in_out_1x1(X, K)",
   "id": "8eb276fed751a6b9",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([3, 9])\n",
      "torch.Size([2, 3])\n"
     ]
    }
   ],
   "execution_count": 58
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-03-22T08:24:53.417955565Z",
     "start_time": "2026-03-22T08:24:53.297421833Z"
    }
   },
   "cell_type": "code",
   "source": [
    "Y2 = corr2d_multi_in_out(X, K)\n",
    "assert float(torch.abs(Y1 - Y2).sum()) < 1e-6"
   ],
   "id": "be28e27d30f36e2c",
   "outputs": [],
   "execution_count": 59
  },
  {
   "metadata": {},
   "cell_type": "code",
   "outputs": [],
   "execution_count": null,
   "source": "",
   "id": "3c3f71349a2e54c0"
  }
 ],
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