From b8d13f49968562db05f80b46d423fd02de7839cd Mon Sep 17 00:00:00 2001
From: yukun-hh <zhangyukunhh@gmail.com>
Date: Sun, 28 Jun 2026 22:39:18 +0800
Subject: [PATCH] =?UTF-8?q?=E5=AE=8C=E6=88=90=E7=AC=AC=E5=8D=81=E7=AB=A0?=
 =?UTF-8?q?=E5=AD=A6=E4=B9=A0?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 chapter10.ipynb | 1059 +++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 1059 insertions(+)
 create mode 100644 chapter10.ipynb

diff --git a/chapter10.ipynb b/chapter10.ipynb
new file mode 100644
index 0000000..cd1ba10
--- /dev/null
+++ b/chapter10.ipynb
@@ -0,0 +1,1059 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "id": "initial_id",
+   "metadata": {
+    "collapsed": true,
+    "ExecuteTime": {
+     "end_time": "2026-05-29T15:19:42.541639835Z",
+     "start_time": "2026-05-29T15:19:40.265547407Z"
+    }
+   },
+   "source": [
+    "import torch\n",
+    "from d2l import torch as d2l\n"
+   ],
+   "outputs": [],
+   "execution_count": 2
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-05-29T15:19:42.593785744Z",
+     "start_time": "2026-05-29T15:19:42.545273168Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "def show_heatmaps(matrices,xlabel,ylabel,titles=None,figsize=(2.5,2.5),cmap='Reds'):\n",
+    "    d2l.use_svg_display()\n",
+    "    num_rows,num_cols = matrices.shape[0],matrices.shape[1]\n",
+    "    fig,axes = d2l.plt.subplots(num_rows,num_cols,figsize=figsize,sharex=True,squeeze=False)\n",
+    "    for i,(row_axes,row_matrices) in enumerate(zip(axes,matrices)):\n",
+    "        for j,(ax,matrix) in enumerate(zip(row_axes,row_matrices)):\n",
+    "            pcm = ax.imshow(matrix.detach().numpy(),cmap=cmap)\n",
+    "            if i == num_rows - 1:\n",
+    "                ax.set_xlabel(xlabel)\n",
+    "            if j == 0:\n",
+    "                ax.set_ylabel(ylabel)\n",
+    "            if titles:\n",
+    "                ax.set_title(titles[j])\n",
+    "    fig.colorbar(pcm,ax=axes,shrink=0.6)"
+   ],
+   "id": "a0e3f725b7764f08",
+   "outputs": [],
+   "execution_count": 3
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-05-29T15:19:42.832298248Z",
+     "start_time": "2026-05-29T15:19:42.594979091Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "attention_weights = torch.eye(10).reshape((1, 1, 10, 10))\n",
+    "\n",
+    "show_heatmaps(attention_weights, xlabel='Keys', ylabel='Queries')"
+   ],
+   "id": "bb798de34fc4d0fa",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 250x250 with 2 Axes>"
+      ],
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+     },
+     "metadata": {},
+     "output_type": "display_data",
+     "jetTransient": {
+      "display_id": null
+     }
+    }
+   ],
+   "execution_count": 4
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-05-29T15:19:42.925242334Z",
+     "start_time": "2026-05-29T15:19:42.844425757Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "n_train = 50\n",
+    "x_train,_ = torch.sort(torch.rand(n_train)*5)"
+   ],
+   "id": "a4070c75847fb887",
+   "outputs": [],
+   "execution_count": 5
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-05-29T15:19:42.978395734Z",
+     "start_time": "2026-05-29T15:19:42.926738710Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "def f(x):\n",
+    "    return 2*torch.sin(x)+x**0.8"
+   ],
+   "id": "2aca6952876d9cf5",
+   "outputs": [],
+   "execution_count": 6
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-05-29T15:19:43.056742866Z",
+     "start_time": "2026-05-29T15:19:42.982949395Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "y_train = f(x_train)+torch.normal(0.0,0.5,(n_train,))\n",
+    "x_test = torch.arange(0,5,0.1)\n",
+    "y_truth = f(x_test)\n",
+    "n_test = len(x_test)\n",
+    "n_test"
+   ],
+   "id": "ea7b94585dcde934",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "50"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "execution_count": 7
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-05-29T15:19:43.117629637Z",
+     "start_time": "2026-05-29T15:19:43.058324467Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "def plot_kernel_reg(y_hat):\n",
+    "    d2l.plot(x_test, [y_truth, y_hat], 'x', 'y', legend=['Truth', 'Pred'],\n",
+    "        xlim=[0, 5], ylim=[-1, 5])\n",
+    "    d2l.plt.plot(x_train, y_train, 'o', alpha=0.5);"
+   ],
+   "id": "35c2403bbb5509cf",
+   "outputs": [],
+   "execution_count": 8
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-05-29T15:19:43.224202029Z",
+     "start_time": "2026-05-29T15:19:43.119223163Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "y_hat = torch.repeat_interleave(y_train.mean(),n_test)\n",
+    "plot_kernel_reg(y_hat)"
+   ],
+   "id": "f27ecb9c68aed894",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 350x250 with 1 Axes>"
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+     },
+     "metadata": {},
+     "output_type": "display_data",
+     "jetTransient": {
+      "display_id": null
+     }
+    }
+   ],
+   "execution_count": 9
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-05-29T15:19:43.317103476Z",
+     "start_time": "2026-05-29T15:19:43.225907041Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "from torch import nn\n",
+    "X_repeat = x_test.repeat_interleave(n_train).reshape((-1, n_train))\n",
+    "attention_weights = nn.functional.softmax(-(X_repeat - x_train)**2 / 2, dim=1)\n",
+    "y_hat = torch.matmul(attention_weights,y_train)\n",
+    "x_train,X_repeat,attention_weights"
+   ],
+   "id": "fa7579d7cd8bfcb7",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(tensor([9.4801e-04, 5.2621e-02, 2.3643e-01, 2.9541e-01, 3.2529e-01, 3.5294e-01,\n",
+       "         3.6399e-01, 4.1190e-01, 5.8751e-01, 6.8494e-01, 7.2698e-01, 8.0101e-01,\n",
+       "         9.2743e-01, 9.7225e-01, 1.1656e+00, 1.4397e+00, 1.4788e+00, 1.6088e+00,\n",
+       "         1.7147e+00, 1.8255e+00, 1.8951e+00, 1.9322e+00, 2.3849e+00, 2.4183e+00,\n",
+       "         2.4902e+00, 2.5441e+00, 2.6483e+00, 2.8371e+00, 2.9705e+00, 3.1383e+00,\n",
+       "         3.2709e+00, 3.3751e+00, 3.3849e+00, 3.4902e+00, 3.5679e+00, 3.5782e+00,\n",
+       "         3.5821e+00, 3.8648e+00, 4.1552e+00, 4.3914e+00, 4.4348e+00, 4.4414e+00,\n",
+       "         4.6038e+00, 4.6099e+00, 4.7481e+00, 4.7528e+00, 4.8005e+00, 4.8134e+00,\n",
+       "         4.8934e+00, 4.9044e+00]),\n",
+       " tensor([[0.0000, 0.0000, 0.0000,  ..., 0.0000, 0.0000, 0.0000],\n",
+       "         [0.1000, 0.1000, 0.1000,  ..., 0.1000, 0.1000, 0.1000],\n",
+       "         [0.2000, 0.2000, 0.2000,  ..., 0.2000, 0.2000, 0.2000],\n",
+       "         ...,\n",
+       "         [4.7000, 4.7000, 4.7000,  ..., 4.7000, 4.7000, 4.7000],\n",
+       "         [4.8000, 4.8000, 4.8000,  ..., 4.8000, 4.8000, 4.8000],\n",
+       "         [4.9000, 4.9000, 4.9000,  ..., 4.9000, 4.9000, 4.9000]]),\n",
+       " tensor([[6.8662e-02, 6.8567e-02, 6.6769e-02,  ..., 6.3916e-07, 4.3363e-07,\n",
+       "          4.1091e-07],\n",
+       "         [6.4257e-02, 6.4501e-02, 6.3975e-02,  ..., 9.6787e-07, 6.6191e-07,\n",
+       "          6.2792e-07],\n",
+       "         [5.9935e-02, 6.0473e-02, 6.1093e-02,  ..., 1.4608e-06, 1.0070e-06,\n",
+       "          9.5634e-07],\n",
+       "         ...,\n",
+       "         [9.4878e-07, 1.2079e-06, 2.7906e-06,  ..., 5.8779e-02, 5.8062e-02,\n",
+       "          5.7935e-02],\n",
+       "         [6.1962e-07, 7.9294e-07, 1.8658e-06,  ..., 6.2113e-02, 6.1848e-02,\n",
+       "          6.1781e-02],\n",
+       "         [4.0301e-07, 5.1842e-07, 1.2425e-06,  ..., 6.5370e-02, 6.5614e-02,\n",
+       "          6.5615e-02]]))"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "execution_count": 10
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-05-29T15:19:43.435846223Z",
+     "start_time": "2026-05-29T15:19:43.319732830Z"
+    }
+   },
+   "cell_type": "code",
+   "source": "plot_kernel_reg(y_hat)",
+   "id": "f76573b2eb74ba7",
+   "outputs": [
+    {
+     "data": {
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+     },
+     "metadata": {},
+     "output_type": "display_data",
+     "jetTransient": {
+      "display_id": null
+     }
+    }
+   ],
+   "execution_count": 11
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-05-29T15:19:43.558539946Z",
+     "start_time": "2026-05-29T15:19:43.438360863Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "d2l.show_heatmaps(attention_weights.unsqueeze(0).unsqueeze(0),\n",
+    "        xlabel='Sorted training inputs',\n",
+    "        ylabel='Sorted testing inputs')"
+   ],
+   "id": "1bbe3af9e8a0f412",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 250x250 with 2 Axes>"
+      ],
+      "image/svg+xml": "<?xml version=\"1.0\" encoding=\"utf-8\" standalone=\"no\"?>\n<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n  \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n<svg xmlns:xlink=\"http://www.w3.org/1999/xlink\" width=\"199.80175pt\" height=\"159.039469pt\" viewBox=\"0 0 199.80175 159.039469\" xmlns=\"http://www.w3.org/2000/svg\" version=\"1.1\">\n <metadata>\n  <rdf:RDF xmlns:dc=\"http://purl.org/dc/elements/1.1/\" xmlns:cc=\"http://creativecommons.org/ns#\" xmlns:rdf=\"http://www.w3.org/1999/02/22-rdf-syntax-ns#\">\n   <cc:Work>\n    <dc:type rdf:resource=\"http://purl.org/dc/dcmitype/StillImage\"/>\n    <dc:date>2026-05-29T23:19:43.512317</dc:date>\n    <dc:format>image/svg+xml</dc:format>\n    <dc:creator>\n     <cc:Agent>\n      <dc:title>Matplotlib v3.7.2, https://matplotlib.org/</dc:title>\n     </cc:Agent>\n    </dc:creator>\n   </cc:Work>\n  </rdf:RDF>\n </metadata>\n <defs>\n  <style type=\"text/css\">*{stroke-linejoin: round; stroke-linecap: 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+     },
+     "metadata": {},
+     "output_type": "display_data",
+     "jetTransient": {
+      "display_id": null
+     }
+    }
+   ],
+   "execution_count": 12
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-05-29T15:19:43.610782245Z",
+     "start_time": "2026-05-29T15:19:43.560607937Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "class NWKernelRegression(nn.Module):\n",
+    "    def __init__(self,**kwargs):\n",
+    "        super().__init__(**kwargs)\n",
+    "        self.w = nn.Parameter(torch.randn((1,),requires_grad=True))\n",
+    "    def forward(self,queries,keys,values):\n",
+    "        queries = queries.repeat_interleave(keys.shape[1]).reshape(-1,keys.shape[1])\n",
+    "        self.attention_weights = nn.functional.softmax(\n",
+    "                -((queries - keys) * self.w)**2 / 2, dim=1)\n",
+    "        return torch.bmm(self.attention_weights.unsqueeze(1),values.unsqueeze(-1)).reshape(-1)"
+   ],
+   "id": "aeb79a3bd64c826",
+   "outputs": [],
+   "execution_count": 13
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-05-29T15:19:43.661276280Z",
+     "start_time": "2026-05-29T15:19:43.611752619Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "# X_tile的形状:(n_train，n_train)，每一行都包含着相同的训练输入\n",
+    "X_tile = x_train.repeat((n_train, 1))\n",
+    "# Y_tile的形状:(n_train，n_train)，每一行都包含着相同的训练输出\n",
+    "Y_tile = y_train.repeat((n_train, 1))\n",
+    "# keys的形状:('n_train'，'n_train'-1)\n",
+    "keys = X_tile[(1 - torch.eye(n_train)).type(torch.bool)].reshape((n_train, -1))\n",
+    "# values的形状:('n_train'，'n_train'-1)\n",
+    "values = Y_tile[(1 - torch.eye(n_train)).type(torch.bool)].reshape((n_train, -1))"
+   ],
+   "id": "7061ed7f139bc78e",
+   "outputs": [],
+   "execution_count": 14
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-05-29T15:19:44.080669795Z",
+     "start_time": "2026-05-29T15:19:43.662206275Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "net = NWKernelRegression()\n",
+    "loss = nn.MSELoss(reduction='none')\n",
+    "trainer = torch.optim.SGD(net.parameters(), lr=0.5)\n",
+    "animator = d2l.Animator(xlabel='epoch', ylabel='loss', xlim=[1, 5])\n",
+    "for epoch in range(5):\n",
+    "    trainer.zero_grad()\n",
+    "    l = loss(net(x_train, keys, values), y_train)\n",
+    "    l.sum().backward()\n",
+    "    trainer.step()\n",
+    "    print(f'epoch {epoch + 1}, loss {float(l.sum()):.6f}')\n",
+    "    animator.add(epoch + 1, float(l.sum()))"
+   ],
+   "id": "3dca11d4e962d859",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 350x250 with 1 Axes>"
+      ],
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+     },
+     "metadata": {},
+     "output_type": "display_data",
+     "jetTransient": {
+      "display_id": null
+     }
+    }
+   ],
+   "execution_count": 15
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-05-29T15:19:44.196985129Z",
+     "start_time": "2026-05-29T15:19:44.082458369Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "# keys的形状:(n_test，n_train)，每一行包含着相同的训练输入（例如，相同的键）\n",
+    "keys = x_train.repeat((n_test, 1))\n",
+    "# value的形状:(n_test，n_train)\n",
+    "values = y_train.repeat((n_test, 1))\n",
+    "y_hat = net(x_test, keys, values).unsqueeze(1).detach()\n",
+    "plot_kernel_reg(y_hat)"
+   ],
+   "id": "4ff00f0b4983b55e",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 350x250 with 1 Axes>"
+      ],
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+     },
+     "metadata": {},
+     "output_type": "display_data",
+     "jetTransient": {
+      "display_id": null
+     }
+    }
+   ],
+   "execution_count": 16
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-05-29T15:19:44.303651198Z",
+     "start_time": "2026-05-29T15:19:44.199584310Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "d2l.show_heatmaps(net.attention_weights.unsqueeze(0).unsqueeze(0),\n",
+    "xlabel='Sorted training inputs',\n",
+    "ylabel='Sorted testing inputs')"
+   ],
+   "id": "97a690478767c3c3",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 250x250 with 2 Axes>"
+      ],
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+     },
+     "metadata": {},
+     "output_type": "display_data",
+     "jetTransient": {
+      "display_id": null
+     }
+    }
+   ],
+   "execution_count": 17
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-05-29T15:19:44.358514818Z",
+     "start_time": "2026-05-29T15:19:44.306386112Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "def masked_softmax(X,valid_lens):\n",
+    "    # X:3D valid_len 1D or 2D\n",
+    "    if valid_lens is None:\n",
+    "        return nn.functional.softmax(X,dim=-1)\n",
+    "    else:\n",
+    "        shape = X.shape\n",
+    "        if valid_lens.dim()==1:\n",
+    "            valid_lens = torch.repeat_interleave(valid_lens,shape[1])\n",
+    "        else:\n",
+    "            valid_lens = valid_lens.reshape(-1)\n",
+    "        #print(valid_lens)\n",
+    "        X = d2l.sequence_mask(X.reshape(-1,shape[-1]),valid_lens,value=-1e6)\n",
+    "        return nn.functional.softmax(X.reshape(shape),dim=-1)"
+   ],
+   "id": "8d03d0c7f735b755",
+   "outputs": [],
+   "execution_count": 18
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-05-29T15:19:44.420710191Z",
+     "start_time": "2026-05-29T15:19:44.359351187Z"
+    }
+   },
+   "cell_type": "code",
+   "source": "masked_softmax(torch.rand(2,2,4),torch.tensor([2,3]))",
+   "id": "27f8c24ec572b267",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "tensor([[[0.6937, 0.3063, 0.0000, 0.0000],\n",
+       "         [0.6165, 0.3835, 0.0000, 0.0000]],\n",
+       "\n",
+       "        [[0.3526, 0.3291, 0.3183, 0.0000],\n",
+       "         [0.2735, 0.2359, 0.4906, 0.0000]]])"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "execution_count": 19
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-05-29T15:19:44.470391356Z",
+     "start_time": "2026-05-29T15:19:44.422576814Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "class AdditiveAttention(nn.Module):\n",
+    "    def __init__(self,key_size,query_size,num_hiddens,dropout,**kwargs):\n",
+    "        super(AdditiveAttention,self).__init__(**kwargs)\n",
+    "        self.W_k = nn.Linear(key_size,num_hiddens,bias=False)\n",
+    "        self.W_q = nn.Linear(query_size,num_hiddens,bias=False)\n",
+    "        self.w_v = nn.Linear(num_hiddens,1,bias=False)\n",
+    "        self.dropout=nn.Dropout(dropout)\n",
+    "    def forward(self,queries,keys,value,valid_lens):\n",
+    "        queries,keys=self.W_q(queries),self.W_k(keys)\n",
+    "        # queries (batch_size,n_q,1,num_hidden)\n",
+    "        # key     (batch_size,1,n_k,num_hiddens)\n",
+    "        features = queries.unsqueeze(2) + keys.unsqueeze(1)\n",
+    "        #features (batch_size,n_q,n_k,num_hidden)\n",
+    "        features = torch.tanh(features)\n",
+    "        scores = self.w_v(features).squeeze(-1)\n",
+    "        #print(f\"Inside AdditiveAttention: value.shape = {value.shape}\")  # 检查此处形状\n",
+    "        self.attention_weights = masked_softmax(scores, valid_lens)\n",
+    "\n",
+    "        return torch.bmm(self.dropout(self.attention_weights), value)"
+   ],
+   "id": "c7e5254395b14b1",
+   "outputs": [],
+   "execution_count": 20
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-05-29T15:19:44.536311739Z",
+     "start_time": "2026-05-29T15:19:44.471725853Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "queries,keys = torch.normal(0,1,(2,1,20)) , torch.ones((2,10,2))\n",
+    "values =  torch.arange(40,dtype=torch.float32).reshape(1,10,4).repeat(2,1,1)\n",
+    "valid_lens = torch.tensor([2, 6])\n",
+    "attention = AdditiveAttention(key_size=2, query_size=20, num_hiddens=8,\n",
+    "                                dropout=0.1)\n",
+    "attention.eval()\n",
+    "attention(queries,keys,values,valid_lens)"
+   ],
+   "id": "77e325a032e70d98",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "tensor([[[ 2.0000,  3.0000,  4.0000,  5.0000]],\n",
+       "\n",
+       "        [[10.0000, 11.0000, 12.0000, 13.0000]]], grad_fn=<BmmBackward0>)"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "execution_count": 21
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-05-29T15:19:44.618227172Z",
+     "start_time": "2026-05-29T15:19:44.538570102Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "d2l.show_heatmaps(attention.attention_weights.reshape((1, 1, 2, 10)),\n",
+    "        xlabel='Keys', ylabel='Queries')"
+   ],
+   "id": "93ceedfd6e03982f",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 250x250 with 2 Axes>"
+      ],
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+     },
+     "metadata": {},
+     "output_type": "display_data",
+     "jetTransient": {
+      "display_id": null
+     }
+    }
+   ],
+   "execution_count": 22
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-05-29T15:19:44.668038302Z",
+     "start_time": "2026-05-29T15:19:44.620075434Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "import math\n",
+    "class DotProductAttention(nn.Module):\n",
+    "    \"\"\"缩放点积注意力\"\"\"\n",
+    "    def __init__(self, dropout, **kwargs):\n",
+    "        super(DotProductAttention, self).__init__(**kwargs)\n",
+    "        self.dropout = nn.Dropout(dropout)\n",
+    "# queries的形状：(batch_size，查询的个数，d)\n",
+    "# keys的形状：(batch_size，“键－值”对的个数，d)\n",
+    "# values的形状：(batch_size，“键－值”对的个数，值的维度)\n",
+    "# valid_lens的形状:(batch_size，)或者(batch_size，查询的个数)\n",
+    "    def forward(self, queries, keys, values, valid_lens=None):\n",
+    "        d = queries.shape[-1]\n",
+    "        # 设置transpose_b=True为了交换keys的最后两个维度\n",
+    "        scores = torch.bmm(queries, keys.transpose(1,2)) / math.sqrt(d)\n",
+    "        self.attention_weights = masked_softmax(scores, valid_lens)\n",
+    "\n",
+    "        return torch.bmm(self.dropout(self.attention_weights), values)"
+   ],
+   "id": "8453c76623a5b435",
+   "outputs": [],
+   "execution_count": 23
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-05-29T15:19:44.754268677Z",
+     "start_time": "2026-05-29T15:19:44.668929892Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "queries = torch.normal(0, 1, (2, 1, 2))\n",
+    "attention = DotProductAttention(dropout=0.5)\n",
+    "attention.eval()\n",
+    "attention(queries, keys, values, valid_lens)"
+   ],
+   "id": "8af6b28944977a62",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "tensor([[[ 2.0000,  3.0000,  4.0000,  5.0000]],\n",
+       "\n",
+       "        [[10.0000, 11.0000, 12.0000, 13.0000]]])"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "execution_count": 24
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-05-29T15:19:44.822905172Z",
+     "start_time": "2026-05-29T15:19:44.756082157Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "d2l.show_heatmaps(attention.attention_weights.reshape((1, 1, 2, 10)),\n",
+    "xlabel='Keys', ylabel='Queries')"
+   ],
+   "id": "28f7e16771076e33",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 250x250 with 2 Axes>"
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+     },
+     "metadata": {},
+     "output_type": "display_data",
+     "jetTransient": {
+      "display_id": null
+     }
+    }
+   ],
+   "execution_count": 25
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-05-29T15:19:44.874393721Z",
+     "start_time": "2026-05-29T15:19:44.824430337Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "class AttentionDecoder(d2l.Decoder):\n",
+    "    def __init__(self,**kwargs):\n",
+    "        super(AttentionDecoder, self).__init__(**kwargs)\n",
+    "    @property\n",
+    "    def attention_weight(self):\n",
+    "        raise NotImplementedError"
+   ],
+   "id": "33507bd2e1917a29",
+   "outputs": [],
+   "execution_count": 26
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-05-29T15:19:44.926627628Z",
+     "start_time": "2026-05-29T15:19:44.875567483Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "class Seq2SeqAttentionDecoder(AttentionDecoder):\n",
+    "    def __init__(self,vocab_size,embed_size,num_hiddens,num_layers,dropout=0,**kwargs):\n",
+    "        super(Seq2SeqAttentionDecoder, self).__init__(**kwargs)\n",
+    "        self.attention = AdditiveAttention(\n",
+    "        num_hiddens,  num_hiddens, num_hiddens,dropout)\n",
+    "        self.embedding = nn.Embedding(vocab_size, embed_size)\n",
+    "        self.rnn = nn.GRU(\n",
+    "        embed_size + num_hiddens, num_hiddens, num_layers,\n",
+    "        dropout=dropout)\n",
+    "        self.dense = nn.Linear(num_hiddens, vocab_size)\n",
+    "    def init_state(self, enc_outputs, enc_valid_lens, *args):\n",
+    "# outputs的形状为(batch_size，num_steps，num_hiddens).\n",
+    "# hidden_state的形状为(num_layers，batch_size，num_hiddens)\n",
+    "        outputs, hidden_state = enc_outputs\n",
+    "        #print(f\"Encoder outputs shape before permute: {outputs.shape}\")  # 应为 (num_steps, batch_size, num_hiddens) 或 (batch_size, num_steps, num_hiddens)\n",
+    "        enc_outputs_permuted = outputs.permute(1, 0, 2)\n",
+    "        #print(f\"After permute: {enc_outputs_permuted.shape}\")  # 期望 (batch_size, num_steps, num_hiddens)\n",
+    "        return (enc_outputs_permuted, hidden_state, enc_valid_lens)\n",
+    "    def forward(self, X, state):\n",
+    "        # enc_outputs的形状为(batch_size,num_steps,num_hiddens).\n",
+    "        # hidden_state的形状为(num_layers,batch_size,\n",
+    "        # num_hiddens)\n",
+    "        enc_outputs, hidden_state, enc_valid_lens = state\n",
+    "        # 输出X的形状为(num_steps,batch_size,embed_size)\n",
+    "        X = self.embedding(X).permute(1, 0, 2)\n",
+    "        outputs, self._attention_weights = [], []\n",
+    "        for x in X:\n",
+    "            # query的形状为(batch_size,1,num_hiddens)\n",
+    "            query = torch.unsqueeze(hidden_state[-1], dim=1)\n",
+    "            # context的形状为(batch_size,1,num_hiddens)\n",
+    "            #print(f\"values shape before attention: {enc_outputs.shape}\")  # 应为 (4, 7, 16)\n",
+    "            context = self.attention(\n",
+    "            query, enc_outputs, enc_outputs, enc_valid_lens)\n",
+    "            # 在特征维度上连结\n",
+    "            x = torch.cat((context, torch.unsqueeze(x, dim=1)), dim=-1)\n",
+    "            # 将x变形为(1,batch_size,embed_size+num_hiddens)\n",
+    "            out, hidden_state = self.rnn(x.permute(1, 0, 2), hidden_state)\n",
+    "            outputs.append(out)\n",
+    "            self._attention_weights.append(self.attention.attention_weights)\n",
+    "        # 全连接层变换后，outputs的形状为\n",
+    "        # (num_steps,batch_size,vocab_size)\n",
+    "        outputs = self.dense(torch.cat(outputs, dim=0))\n",
+    "        return outputs.permute(1, 0, 2), [enc_outputs, hidden_state,\n",
+    "        enc_valid_lens]\n",
+    "    @property\n",
+    "    def attention_weights(self):\n",
+    "        return self._attention_weights"
+   ],
+   "id": "a8a497c9041910a1",
+   "outputs": [],
+   "execution_count": 27
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-05-29T15:19:44.996664037Z",
+     "start_time": "2026-05-29T15:19:44.927689328Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "encoder = d2l.Seq2SeqEncoder(vocab_size=10, embed_size=8, num_hiddens=16,\n",
+    "num_layers=2)\n",
+    "encoder.eval()\n",
+    "decoder = Seq2SeqAttentionDecoder(vocab_size=10, embed_size=8, num_hiddens=16,\n",
+    "num_layers=2)\n",
+    "decoder.eval()\n",
+    "X = torch.zeros((4, 7), dtype=torch.long) # (batch_size,num_steps)\n",
+    "state = decoder.init_state(encoder(X), None)\n",
+    "output, state = decoder(X, state)\n",
+    "output.shape, len(state), state[0].shape, len(state[1]), state[1][0].shape"
+   ],
+   "id": "b2a6bc735743bd0f",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(torch.Size([4, 7, 10]), 3, torch.Size([4, 7, 16]), 2, torch.Size([4, 16]))"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "execution_count": 28
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-05-29T15:19:45.047118759Z",
+     "start_time": "2026-05-29T15:19:44.998053092Z"
+    }
+   },
+   "cell_type": "code",
+   "source": "",
+   "id": "4c7bf86095f15c98",
+   "outputs": [],
+   "execution_count": 28
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-05-29T15:20:14.678359202Z",
+     "start_time": "2026-05-29T15:19:45.048442204Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "embed_size, num_hiddens, num_layers, dropout = 32, 32, 2, 0.1\n",
+    "batch_size, num_steps = 64, 10\n",
+    "lr, num_epochs, device = 0.005, 250, d2l.try_gpu()\n",
+    "train_iter, src_vocab, tgt_vocab = d2l.load_data_nmt(batch_size, num_steps)\n",
+    "encoder = d2l.Seq2SeqEncoder(\n",
+    "len(src_vocab), embed_size, num_hiddens, num_layers, dropout)\n",
+    "decoder = Seq2SeqAttentionDecoder(\n",
+    "len(tgt_vocab), embed_size, num_hiddens, num_layers, dropout)\n",
+    "net = d2l.EncoderDecoder(encoder, decoder)\n",
+    "d2l.train_seq2seq(net, train_iter, lr, num_epochs, tgt_vocab, device)"
+   ],
+   "id": "5f5cedf74b97bd12",
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "loss 0.019, 17862.0 tokens/sec on cuda:0\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 350x250 with 1 Axes>"
+      ],
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+     },
+     "metadata": {},
+     "output_type": "display_data",
+     "jetTransient": {
+      "display_id": null
+     }
+    }
+   ],
+   "execution_count": 29
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-05-29T15:20:14.794039857Z",
+     "start_time": "2026-05-29T15:20:14.727727180Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "engs = ['go .', \"i lost .\", 'he\\'s calm .', 'i\\'m home .']\n",
+    "fras = ['va !', 'j\\'ai perdu .', 'il est calme .', 'je suis chez moi .']\n",
+    "for eng, fra in zip(engs, fras):\n",
+    "    translation, dec_attention_weight_seq = d2l.predict_seq2seq(\n",
+    "        net, eng, src_vocab, tgt_vocab, num_steps, device, True)\n",
+    "    print(f'{eng} => {translation}, ',\n",
+    "                f'bleu {d2l.bleu(translation, fra, k=2):.3f}')"
+   ],
+   "id": "ddaed8bd284ee01b",
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "go . => va !,  bleu 1.000\n",
+      "i lost . => j'ai perdu .,  bleu 1.000\n",
+      "he's calm . => il est <unk> .,  bleu 0.658\n",
+      "i'm home . => je suis chez moi .,  bleu 1.000\n"
+     ]
+    }
+   ],
+   "execution_count": 30
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-05-29T15:20:38.342057516Z",
+     "start_time": "2026-05-29T15:20:38.288567783Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "class MultiHeadAttention(nn.Module):\n",
+    "    def __init__(self, key_size, query_size, value_size, num_hiddens,\n",
+    "             num_heads, dropout, bias=False, **kwargs):\n",
+    "        super(MultiHeadAttention, self).__init__(**kwargs)\n",
+    "        self.num_heads = num_heads\n",
+    "        self.attention = d2l.DotProductAttention(dropout)\n",
+    "        self.W_q = nn.Linear(query_size, num_hiddens, bias=bias)\n",
+    "        self.W_k = nn.Linear(key_size, num_hiddens, bias=bias)\n",
+    "        self.W_v = nn.Linear(value_size, num_hiddens, bias=bias)\n",
+    "        self.W_o = nn.Linear(num_hiddens, num_hiddens, bias=bias)\n",
+    "    def forward(self, queries, keys, values, valid_lens):\n",
+    "        # 1. 线性投影 + 变换形状以分割多头\n",
+    "        queries = transpose_qkv(self.W_q(queries), self.num_heads)\n",
+    "        keys    = transpose_qkv(self.W_k(keys),    self.num_heads)\n",
+    "        values  = transpose_qkv(self.W_v(values),  self.num_heads)\n",
+    "\n",
+    "        # 2. 处理有效长度掩码（valid_lens）以适配多头\n",
+    "        if valid_lens is not None:\n",
+    "            valid_lens = torch.repeat_interleave(valid_lens, repeats=self.num_heads, dim=0)\n",
+    "\n",
+    "        # 3. 计算注意力（每个头独立计算）\n",
+    "        output = self.attention(queries, keys, values, valid_lens)\n",
+    "\n",
+    "        # 4. 合并多头，并通过输出线性层\n",
+    "        output_concat = transpose_output(output, self.num_heads)\n",
+    "        return self.W_o(output_concat)\n",
+    "def transpose_qkv(X, num_heads):\n",
+    "    \"\"\"为了多注意力头的并行计算而变换形状\"\"\"\n",
+    "    # 输入X的形状:(batch_size，查询或者“键－值”对的个数，num_hiddens)\n",
+    "    # 输出X的形状:(batch_size，查询或者“键－值”对的个数，num_heads，\n",
+    "    # num_hiddens/num_heads)\n",
+    "    X = X.reshape(X.shape[0], X.shape[1], num_heads, -1)\n",
+    "    # 输出X的形状:(batch_size，num_heads，查询或者“键－值”对的个数,\n",
+    "    # num_hiddens/num_heads)\n",
+    "    X = X.permute(0, 2, 1, 3)\n",
+    "    # 最终输出的形状:(batch_size*num_heads,查询或者“键－值”对的个数,\n",
+    "    # num_hiddens/num_heads)\n",
+    "    return X.reshape(-1, X.shape[2], X.shape[3])\n",
+    "\n",
+    "def transpose_output(X, num_heads):\n",
+    "    \"\"\"逆转transpose_qkv函数的操作\"\"\"\n",
+    "    X = X.reshape(-1, num_heads, X.shape[1], X.shape[2])\n",
+    "    X = X.permute(0, 2, 1, 3)\n",
+    "    return X.reshape(X.shape[0], X.shape[1], -1)\n"
+   ],
+   "id": "13d19f8b5048c74d",
+   "outputs": [],
+   "execution_count": 32
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-05-29T15:20:39.251763154Z",
+     "start_time": "2026-05-29T15:20:39.201715213Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "num_hiddens, num_heads = 100, 5\n",
+    "attention = MultiHeadAttention(num_hiddens, num_hiddens, num_hiddens,\n",
+    "num_hiddens, num_heads, 0.5)\n",
+    "attention.eval()"
+   ],
+   "id": "b25c661a511d7763",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "MultiHeadAttention(\n",
+       "  (attention): DotProductAttention(\n",
+       "    (dropout): Dropout(p=0.5, inplace=False)\n",
+       "  )\n",
+       "  (W_q): Linear(in_features=100, out_features=100, bias=False)\n",
+       "  (W_k): Linear(in_features=100, out_features=100, bias=False)\n",
+       "  (W_v): Linear(in_features=100, out_features=100, bias=False)\n",
+       "  (W_o): Linear(in_features=100, out_features=100, bias=False)\n",
+       ")"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "execution_count": 33
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-05-29T15:20:40.455647106Z",
+     "start_time": "2026-05-29T15:20:40.307682855Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "batch_size, num_queries = 2, 4\n",
+    "num_kvpairs, valid_lens = 6, torch.tensor([3, 2])\n",
+    "X = torch.ones((batch_size, num_queries, num_hiddens))\n",
+    "Y = torch.ones((batch_size, num_kvpairs, num_hiddens))\n",
+    "attention(X, Y, Y, valid_lens).shape"
+   ],
+   "id": "49877c929741ec94",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "torch.Size([2, 4, 100])"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "execution_count": 34
+  },
+  {
+   "metadata": {},
+   "cell_type": "code",
+   "outputs": [],
+   "execution_count": null,
+   "source": "",
+   "id": "b8a10576c7789750"
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}