diff --git a/chapter_11_optimization/lr-scheduler.ipynb b/chapter_11_optimization/lr-scheduler.ipynb
index 397fb76..f1316da 100644
--- a/chapter_11_optimization/lr-scheduler.ipynb
+++ b/chapter_11_optimization/lr-scheduler.ipynb
@@ -18,10 +18,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 1,
    "id": "47980ea0",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[WARNING] ME(398508:47004611480960,MainProcess):2023-04-03-19:20:51.352.287 [mindspore/common/_decorator.py:40] 'Fills' is deprecated from version 2.0 and will be removed in a future version, use 'ops.fill' instead.\n"
+     ]
+    }
+   ],
    "source": [
     "%matplotlib inline\n",
     "import math\n",
@@ -30,20 +38,6 @@
     "from d2l import mindspore as d2l\n",
     "from mindspore import value_and_grad\n",
     "\n",
-    "# def net_fn():\n",
-    "#     model = nn.SequentialCell(\n",
-    "#         nn.Conv2d(1, 6, kernel_size=5, padding=2, pad_mode='pad', weight_init='xavier_uniform'), nn.ReLU(),\n",
-    "#         nn.MaxPool2d(kernel_size=2, stride=2),\n",
-    "#         nn.Conv2d(6, 16, kernel_size=5, pad_mode='valid', weight_init='xavier_uniform'), nn.ReLU(),\n",
-    "#         nn.MaxPool2d(kernel_size=2, stride=2),\n",
-    "#         nn.Flatten(),\n",
-    "#         nn.Dense(16 * 5 * 5, 120, weight_init='xavier_uniform'), nn.ReLU(),\n",
-    "#         nn.Dense(120, 84, weight_init='xavier_uniform'), nn.ReLU(),\n",
-    "#         nn.Dense(84, 10, weight_init='xavier_uniform'))\n",
-    "\n",
-    "#     return model\n",
-    "\n",
-    "\n",
     "def net_fn():\n",
     "    model = nn.SequentialCell(\n",
     "        nn.Conv2d(1, 6, kernel_size=5, padding=2, pad_mode='pad', weight_init='HeUniform'), nn.ReLU(),\n",
@@ -104,36 +98,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 2,
    "id": "f69d3a9d",
    "metadata": {},
-   "outputs": [],
-   "source": [
-    "lr, num_epochs = 0.3, 30\n",
-    "net = net_fn()\n",
-    "trainer = nn.SGD(net.trainable_params(), lr)\n",
-    "# train(net, train_iter, test_iter, num_epochs, loss, trainer)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "b6142bca",
-   "metadata": {},
-   "source": [
-    "# BUG"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "87b461e5",
-   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "train loss 694451963869065584049010050596864.000, train acc 0.209, test acc 0.301\n"
+      "train loss 0.119, train acc 0.955, test acc 0.889\n"
      ]
     },
     {
@@ -142,12 +115,12 @@
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+       "<svg xmlns:xlink=\"http://www.w3.org/1999/xlink\" width=\"238.965625pt\" height=\"183.35625pt\" viewBox=\"0 0 238.965625 183.35625\" xmlns=\"http://www.w3.org/2000/svg\" version=\"1.1\">\n",
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        "   <cc:Work>\n",
        "    <dc:type rdf:resource=\"http://purl.org/dc/dcmitype/StillImage\"/>\n",
-       "    <dc:date>2023-02-23T20:58:52.168125</dc:date>\n",
+       "    <dc:date>2023-04-03T19:24:42.750080</dc:date>\n",
        "    <dc:format>image/svg+xml</dc:format>\n",
        "    <dc:creator>\n",
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@@ -163,8 +136,8 @@
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+       "L 166.813125 92.179878 \n",
+       "L 173.323125 92.222061 \n",
+       "L 179.833125 92.420922 \n",
+       "L 186.343125 92.637861 \n",
+       "L 192.853125 92.866852 \n",
+       "L 199.363125 92.240139 \n",
+       "L 205.873125 92.234113 \n",
+       "L 212.383125 92.288348 \n",
+       "L 218.893125 92.27027 \n",
+       "L 225.403125 92.197957 \n",
+       "\" clip-path=\"url(#pec68262993)\" style=\"fill: none; stroke-dasharray: 9.6,2.4,1.5,2.4; stroke-dashoffset: 0; stroke: #008000; stroke-width: 1.5\"/>\n",
        "   </g>\n",
        "   <g id=\"patch_3\">\n",
        "    <path d=\"M 30.103125 145.8 \n",
@@ -748,13 +968,13 @@
        "z\n",
        "\" style=\"fill: #ffffff; opacity: 0.8; stroke: #cccccc; stroke-linejoin: miter\"/>\n",
        "    </g>\n",
-       "    <g id=\"line2d_26\">\n",
+       "    <g id=\"line2d_28\">\n",
        "     <path d=\"M 142.634375 20.298438 \n",
        "L 152.634375 20.298438 \n",
        "L 162.634375 20.298438 \n",
        "\" style=\"fill: none; stroke: #1f77b4; stroke-width: 1.5; stroke-linecap: square\"/>\n",
        "    </g>\n",
-       "    <g id=\"text_13\">\n",
+       "    <g id=\"text_14\">\n",
        "     <!-- train loss -->\n",
        "     <g transform=\"translate(170.634375 23.798438) scale(0.1 -0.1)\">\n",
        "      <defs>\n",
@@ -913,13 +1133,13 @@
        "      <use xlink:href=\"#DejaVuSans-73\" x=\"405.615234\"/>\n",
        "     </g>\n",
        "    </g>\n",
-       "    <g id=\"line2d_27\">\n",
+       "    <g id=\"line2d_29\">\n",
        "     <path d=\"M 142.634375 34.976563 \n",
        "L 152.634375 34.976563 \n",
        "L 162.634375 34.976563 \n",
        "\" style=\"fill: none; stroke-dasharray: 5.55,2.4; stroke-dashoffset: 0; stroke: #bf00bf; stroke-width: 1.5\"/>\n",
        "    </g>\n",
-       "    <g id=\"text_14\">\n",
+       "    <g id=\"text_15\">\n",
        "     <!-- train acc -->\n",
        "     <g transform=\"translate(170.634375 38.476563) scale(0.1 -0.1)\">\n",
        "      <use xlink:href=\"#DejaVuSans-74\"/>\n",
@@ -933,13 +1153,13 @@
        "      <use xlink:href=\"#DejaVuSans-63\" x=\"380.810547\"/>\n",
        "     </g>\n",
        "    </g>\n",
-       "    <g id=\"line2d_28\">\n",
+       "    <g id=\"line2d_30\">\n",
        "     <path d=\"M 142.634375 49.654688 \n",
        "L 152.634375 49.654688 \n",
        "L 162.634375 49.654688 \n",
        "\" style=\"fill: none; stroke-dasharray: 9.6,2.4,1.5,2.4; stroke-dashoffset: 0; stroke: #008000; stroke-width: 1.5\"/>\n",
        "    </g>\n",
-       "    <g id=\"text_15\">\n",
+       "    <g id=\"text_16\">\n",
        "     <!-- test acc -->\n",
        "     <g transform=\"translate(170.634375 53.154688) scale(0.1 -0.1)\">\n",
        "      <use xlink:href=\"#DejaVuSans-74\"/>\n",
@@ -956,7 +1176,7 @@
        "  </g>\n",
        " </g>\n",
        " <defs>\n",
-       "  <clipPath id=\"pfce1b4fd7c\">\n",
+       "  <clipPath id=\"pec68262993\">\n",
        "   <rect x=\"30.103125\" y=\"7.2\" width=\"195.3\" height=\"138.6\"/>\n",
        "  </clipPath>\n",
        " </defs>\n",
@@ -971,19 +1191,10 @@
     }
    ],
    "source": [
-    "lr, num_epochs = 0.3, 5\n",
-    "net10 = nn.SequentialCell(\n",
-    "        nn.Conv2d(1, 6, kernel_size=5, padding=2, pad_mode='pad', weight_init='HeUniform'), nn.ReLU(),\n",
-    "        nn.MaxPool2d(kernel_size=2, stride=2),\n",
-    "        nn.Conv2d(6, 16, kernel_size=5, pad_mode='valid', weight_init='HeUniform'), nn.ReLU(),\n",
-    "        nn.MaxPool2d(kernel_size=2, stride=2),\n",
-    "        nn.Flatten(),\n",
-    "        nn.Dense(16 * 5 * 5, 120, weight_init='HeUniform'), nn.ReLU(),\n",
-    "        nn.Dense(120, 84, weight_init='HeUniform'), nn.ReLU(),\n",
-    "        nn.Dense(84, 10, weight_init='HeUniform'))\n",
-    "\n",
-    "trainer = nn.SGD(net10.trainable_params(), lr)\n",
-    "train(net10, train_iter, test_iter, num_epochs, loss, trainer)"
+    "lr, num_epochs = 0.3, 30\n",
+    "net = net_fn()\n",
+    "trainer = nn.SGD(net.trainable_params(), lr)\n",
+    "train(net, train_iter, test_iter, num_epochs, loss, trainer)"
    ]
   },
   {
@@ -996,7 +1207,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 3,
    "id": "786b7035",
    "metadata": {},
    "outputs": [
@@ -1004,19 +1215,19 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "learning rate is now 0.30\n"
+      "learning rate is now 0.10\n"
      ]
     }
    ],
    "source": [
-    "# 改写learning_rate参数的值\n",
-    "mindspore.ops.assign(trainer.learning_rate, d2l.tensor(0.1))\n",
+    "lr = 0.1\n",
+    "mindspore.ops.assign(trainer.learning_rate, lr)\n",
     "print(f'learning rate is now {trainer.learning_rate.data.asnumpy():.2f}')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 8,
    "id": "7b859ea3",
    "metadata": {},
    "outputs": [],
@@ -1046,7 +1257,7 @@
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        "   <cc:Work>\n",
        "    <dc:type rdf:resource=\"http://purl.org/dc/dcmitype/StillImage\"/>\n",
-       "    <dc:date>2023-02-23T21:00:24.300243</dc:date>\n",
+       "    <dc:date>2023-04-03T19:27:25.859227</dc:date>\n",
        "    <dc:format>image/svg+xml</dc:format>\n",
        "    <dc:creator>\n",
        "     <cc:Agent>\n",
@@ -1082,16 +1293,16 @@
        "     <g id=\"line2d_1\">\n",
        "      <path d=\"M 45.342898 145.8 \n",
        "L 45.342898 7.2 \n",
-       "\" clip-path=\"url(#pc8a5adcc1c)\" style=\"fill: none; stroke: #b0b0b0; stroke-width: 0.8; stroke-linecap: square\"/>\n",
+       "\" clip-path=\"url(#pa44c91e5d2)\" style=\"fill: none; stroke: #b0b0b0; stroke-width: 0.8; stroke-linecap: square\"/>\n",
        "     </g>\n",
        "     <g id=\"line2d_2\">\n",
        "      <defs>\n",
-       "       <path id=\"mb895b53a04\" d=\"M 0 0 \n",
+       "       <path id=\"mdf1420867f\" d=\"M 0 0 \n",
        "L 0 3.5 \n",
        "\" style=\"stroke: #000000; stroke-width: 0.8\"/>\n",
        "      </defs>\n",
        "      <g>\n",
-       "       <use xlink:href=\"#mb895b53a04\" x=\"45.342898\" y=\"145.8\" style=\"stroke: #000000; stroke-width: 0.8\"/>\n",
+       "       <use xlink:href=\"#mdf1420867f\" x=\"45.342898\" y=\"145.8\" style=\"stroke: #000000; stroke-width: 0.8\"/>\n",
        "      </g>\n",
        "     </g>\n",
        "     <g id=\"text_1\">\n",
@@ -1128,11 +1339,11 @@
        "     <g id=\"line2d_3\">\n",
        "      <path d=\"M 106.565468 145.8 \n",
        "L 106.565468 7.2 \n",
-       "\" clip-path=\"url(#pc8a5adcc1c)\" style=\"fill: none; stroke: #b0b0b0; stroke-width: 0.8; stroke-linecap: square\"/>\n",
+       "\" clip-path=\"url(#pa44c91e5d2)\" style=\"fill: none; stroke: #b0b0b0; stroke-width: 0.8; stroke-linecap: square\"/>\n",
        "     </g>\n",
        "     <g id=\"line2d_4\">\n",
        "      <g>\n",
-       "       <use xlink:href=\"#mb895b53a04\" x=\"106.565468\" y=\"145.8\" style=\"stroke: #000000; stroke-width: 0.8\"/>\n",
+       "       <use xlink:href=\"#mdf1420867f\" x=\"106.565468\" y=\"145.8\" style=\"stroke: #000000; stroke-width: 0.8\"/>\n",
        "      </g>\n",
        "     </g>\n",
        "     <g id=\"text_2\">\n",
@@ -1163,11 +1374,11 @@
        "     <g id=\"line2d_5\">\n",
        "      <path d=\"M 167.788039 145.8 \n",
        "L 167.788039 7.2 \n",
-       "\" clip-path=\"url(#pc8a5adcc1c)\" style=\"fill: none; stroke: #b0b0b0; stroke-width: 0.8; stroke-linecap: square\"/>\n",
+       "\" clip-path=\"url(#pa44c91e5d2)\" style=\"fill: none; stroke: #b0b0b0; stroke-width: 0.8; stroke-linecap: square\"/>\n",
        "     </g>\n",
        "     <g id=\"line2d_6\">\n",
        "      <g>\n",
-       "       <use xlink:href=\"#mb895b53a04\" x=\"167.788039\" y=\"145.8\" style=\"stroke: #000000; stroke-width: 0.8\"/>\n",
+       "       <use xlink:href=\"#mdf1420867f\" x=\"167.788039\" y=\"145.8\" style=\"stroke: #000000; stroke-width: 0.8\"/>\n",
        "      </g>\n",
        "     </g>\n",
        "     <g id=\"text_3\">\n",
@@ -1208,11 +1419,11 @@
        "     <g id=\"line2d_7\">\n",
        "      <path d=\"M 229.010609 145.8 \n",
        "L 229.010609 7.2 \n",
-       "\" clip-path=\"url(#pc8a5adcc1c)\" style=\"fill: none; stroke: #b0b0b0; stroke-width: 0.8; stroke-linecap: square\"/>\n",
+       "\" clip-path=\"url(#pa44c91e5d2)\" style=\"fill: none; stroke: #b0b0b0; stroke-width: 0.8; stroke-linecap: square\"/>\n",
        "     </g>\n",
        "     <g id=\"line2d_8\">\n",
        "      <g>\n",
-       "       <use xlink:href=\"#mb895b53a04\" x=\"229.010609\" y=\"145.8\" style=\"stroke: #000000; stroke-width: 0.8\"/>\n",
+       "       <use xlink:href=\"#mdf1420867f\" x=\"229.010609\" y=\"145.8\" style=\"stroke: #000000; stroke-width: 0.8\"/>\n",
        "      </g>\n",
        "     </g>\n",
        "     <g id=\"text_4\">\n",
@@ -1263,16 +1474,16 @@
        "     <g id=\"line2d_9\">\n",
        "      <path d=\"M 36.465625 136.813943 \n",
        "L 231.765625 136.813943 \n",
-       "\" clip-path=\"url(#pc8a5adcc1c)\" style=\"fill: none; stroke: #b0b0b0; stroke-width: 0.8; stroke-linecap: square\"/>\n",
+       "\" clip-path=\"url(#pa44c91e5d2)\" style=\"fill: none; stroke: #b0b0b0; stroke-width: 0.8; stroke-linecap: square\"/>\n",
        "     </g>\n",
        "     <g id=\"line2d_10\">\n",
        "      <defs>\n",
-       "       <path id=\"m67659851b4\" d=\"M 0 0 \n",
+       "       <path id=\"m515ed1c302\" d=\"M 0 0 \n",
        "L -3.5 0 \n",
        "\" style=\"stroke: #000000; stroke-width: 0.8\"/>\n",
        "      </defs>\n",
        "      <g>\n",
-       "       <use xlink:href=\"#m67659851b4\" x=\"36.465625\" y=\"136.813943\" style=\"stroke: #000000; stroke-width: 0.8\"/>\n",
+       "       <use xlink:href=\"#m515ed1c302\" x=\"36.465625\" y=\"136.813943\" style=\"stroke: #000000; stroke-width: 0.8\"/>\n",
        "      </g>\n",
        "     </g>\n",
        "     <g id=\"text_5\">\n",
@@ -1298,11 +1509,11 @@
        "     <g id=\"line2d_11\">\n",
        "      <path d=\"M 36.465625 105.985457 \n",
        "L 231.765625 105.985457 \n",
-       "\" clip-path=\"url(#pc8a5adcc1c)\" style=\"fill: none; stroke: #b0b0b0; stroke-width: 0.8; stroke-linecap: square\"/>\n",
+       "\" clip-path=\"url(#pa44c91e5d2)\" style=\"fill: none; stroke: #b0b0b0; stroke-width: 0.8; stroke-linecap: square\"/>\n",
        "     </g>\n",
        "     <g id=\"line2d_12\">\n",
        "      <g>\n",
-       "       <use xlink:href=\"#m67659851b4\" x=\"36.465625\" y=\"105.985457\" style=\"stroke: #000000; stroke-width: 0.8\"/>\n",
+       "       <use xlink:href=\"#m515ed1c302\" x=\"36.465625\" y=\"105.985457\" style=\"stroke: #000000; stroke-width: 0.8\"/>\n",
        "      </g>\n",
        "     </g>\n",
        "     <g id=\"text_6\">\n",
@@ -1340,11 +1551,11 @@
        "     <g id=\"line2d_13\">\n",
        "      <path d=\"M 36.465625 75.156971 \n",
        "L 231.765625 75.156971 \n",
-       "\" clip-path=\"url(#pc8a5adcc1c)\" style=\"fill: none; stroke: #b0b0b0; stroke-width: 0.8; stroke-linecap: square\"/>\n",
+       "\" clip-path=\"url(#pa44c91e5d2)\" style=\"fill: none; stroke: #b0b0b0; stroke-width: 0.8; stroke-linecap: square\"/>\n",
        "     </g>\n",
        "     <g id=\"line2d_14\">\n",
        "      <g>\n",
-       "       <use xlink:href=\"#m67659851b4\" x=\"36.465625\" y=\"75.156971\" style=\"stroke: #000000; stroke-width: 0.8\"/>\n",
+       "       <use xlink:href=\"#m515ed1c302\" x=\"36.465625\" y=\"75.156971\" style=\"stroke: #000000; stroke-width: 0.8\"/>\n",
        "      </g>\n",
        "     </g>\n",
        "     <g id=\"text_7\">\n",
@@ -1393,11 +1604,11 @@
        "     <g id=\"line2d_15\">\n",
        "      <path d=\"M 36.465625 44.328486 \n",
        "L 231.765625 44.328486 \n",
-       "\" clip-path=\"url(#pc8a5adcc1c)\" style=\"fill: none; stroke: #b0b0b0; stroke-width: 0.8; stroke-linecap: square\"/>\n",
+       "\" clip-path=\"url(#pa44c91e5d2)\" style=\"fill: none; stroke: #b0b0b0; stroke-width: 0.8; stroke-linecap: square\"/>\n",
        "     </g>\n",
        "     <g id=\"line2d_16\">\n",
        "      <g>\n",
-       "       <use xlink:href=\"#m67659851b4\" x=\"36.465625\" y=\"44.328486\" style=\"stroke: #000000; stroke-width: 0.8\"/>\n",
+       "       <use xlink:href=\"#m515ed1c302\" x=\"36.465625\" y=\"44.328486\" style=\"stroke: #000000; stroke-width: 0.8\"/>\n",
        "      </g>\n",
        "     </g>\n",
        "     <g id=\"text_8\">\n",
@@ -1455,11 +1666,11 @@
        "     <g id=\"line2d_17\">\n",
        "      <path d=\"M 36.465625 13.5 \n",
        "L 231.765625 13.5 \n",
-       "\" clip-path=\"url(#pc8a5adcc1c)\" style=\"fill: none; stroke: #b0b0b0; stroke-width: 0.8; stroke-linecap: square\"/>\n",
+       "\" clip-path=\"url(#pa44c91e5d2)\" style=\"fill: none; stroke: #b0b0b0; stroke-width: 0.8; stroke-linecap: square\"/>\n",
        "     </g>\n",
        "     <g id=\"line2d_18\">\n",
        "      <g>\n",
-       "       <use xlink:href=\"#m67659851b4\" x=\"36.465625\" y=\"13.5\" style=\"stroke: #000000; stroke-width: 0.8\"/>\n",
+       "       <use xlink:href=\"#m515ed1c302\" x=\"36.465625\" y=\"13.5\" style=\"stroke: #000000; stroke-width: 0.8\"/>\n",
        "      </g>\n",
        "     </g>\n",
        "     <g id=\"text_9\">\n",
@@ -1504,7 +1715,7 @@
        "L 210.643838 138.512248 \n",
        "L 216.766095 139.018898 \n",
        "L 222.888352 139.5 \n",
-       "\" clip-path=\"url(#pc8a5adcc1c)\" style=\"fill: none; stroke: #1f77b4; stroke-width: 1.5; stroke-linecap: square\"/>\n",
+       "\" clip-path=\"url(#pa44c91e5d2)\" style=\"fill: none; stroke: #1f77b4; stroke-width: 1.5; stroke-linecap: square\"/>\n",
        "   </g>\n",
        "   <g id=\"patch_3\">\n",
        "    <path d=\"M 36.465625 145.8 \n",
@@ -1529,7 +1740,7 @@
        "  </g>\n",
        " </g>\n",
        " <defs>\n",
-       "  <clipPath id=\"pc8a5adcc1c\">\n",
+       "  <clipPath id=\"pa44c91e5d2\">\n",
        "   <rect x=\"36.465625\" y=\"7.2\" width=\"195.3\" height=\"138.6\"/>\n",
        "  </clipPath>\n",
        " </defs>\n",
@@ -1545,7 +1756,7 @@
    ],
    "source": [
     "scheduler = SquareRootScheduler(lr=0.1)\n",
-    "d2l.plot(d2l.arange(num_epochs), [scheduler(t) for t in range(num_epochs)])"
+    "d2l.plot(d2l.arange(num_epochs).numpy(), [scheduler(t) for t in range(num_epochs)])"
    ]
   },
   {
@@ -1558,10 +1769,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 10,
    "id": "71bbba03",
    "metadata": {},
    "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "train loss 0.236, train acc 0.914, test acc 0.892\n"
+     ]
+    },
     {
      "data": {
       "image/svg+xml": [
@@ -1573,7 +1791,7 @@
        "  <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>2023-02-23T21:04:10.181314</dc:date>\n",
+       "    <dc:date>2023-04-03T19:31:16.589334</dc:date>\n",
        "    <dc:format>image/svg+xml</dc:format>\n",
        "    <dc:creator>\n",
        "     <cc:Agent>\n",
@@ -1609,16 +1827,16 @@
        "     <g id=\"line2d_1\">\n",
        "      <path d=\"M 30.103125 145.8 \n",
        "L 30.103125 7.2 \n",
-       "\" clip-path=\"url(#pda04aa8e19)\" style=\"fill: none; stroke: #b0b0b0; stroke-width: 0.8; stroke-linecap: square\"/>\n",
+       "\" clip-path=\"url(#pe74770e75b)\" style=\"fill: none; stroke: #b0b0b0; stroke-width: 0.8; stroke-linecap: square\"/>\n",
        "     </g>\n",
        "     <g id=\"line2d_2\">\n",
        "      <defs>\n",
-       "       <path id=\"mdf157588f2\" d=\"M 0 0 \n",
+       "       <path id=\"m7c3ced4763\" d=\"M 0 0 \n",
        "L 0 3.5 \n",
        "\" style=\"stroke: #000000; stroke-width: 0.8\"/>\n",
        "      </defs>\n",
        "      <g>\n",
-       "       <use xlink:href=\"#mdf157588f2\" x=\"30.103125\" y=\"145.8\" style=\"stroke: #000000; stroke-width: 0.8\"/>\n",
+       "       <use xlink:href=\"#m7c3ced4763\" x=\"30.103125\" y=\"145.8\" style=\"stroke: #000000; stroke-width: 0.8\"/>\n",
        "      </g>\n",
        "     </g>\n",
        "     <g id=\"text_1\">\n",
@@ -1655,11 +1873,11 @@
        "     <g id=\"line2d_3\">\n",
        "      <path d=\"M 62.653125 145.8 \n",
        "L 62.653125 7.2 \n",
-       "\" clip-path=\"url(#pda04aa8e19)\" style=\"fill: none; stroke: #b0b0b0; stroke-width: 0.8; stroke-linecap: square\"/>\n",
+       "\" clip-path=\"url(#pe74770e75b)\" style=\"fill: none; stroke: #b0b0b0; stroke-width: 0.8; stroke-linecap: square\"/>\n",
        "     </g>\n",
        "     <g id=\"line2d_4\">\n",
        "      <g>\n",
-       "       <use xlink:href=\"#mdf157588f2\" x=\"62.653125\" y=\"145.8\" style=\"stroke: #000000; stroke-width: 0.8\"/>\n",
+       "       <use xlink:href=\"#m7c3ced4763\" x=\"62.653125\" y=\"145.8\" style=\"stroke: #000000; stroke-width: 0.8\"/>\n",
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@@ -1700,11 +1918,11 @@
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        "L 95.203125 7.2 \n",
-       "\" clip-path=\"url(#pda04aa8e19)\" style=\"fill: none; stroke: #b0b0b0; stroke-width: 0.8; stroke-linecap: square\"/>\n",
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        "     </g>\n",
        "     <g id=\"line2d_6\">\n",
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        "  </g>\n",
        " </g>\n",
        " <defs>\n",
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@@ -2484,83 +2876,10 @@
     "#### 11.11.3.1. 单因子调度器"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "c271bc7a",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "class FactorScheduler:\n",
-    "    def __init__(self, factor=1, stop_factor_lr=1e-7, base_lr=0.1):\n",
-    "        self.factor = factor\n",
-    "        self.stop_factor_lr = stop_factor_lr\n",
-    "        self.base_lr = base_lr\n",
-    "\n",
-    "    def __call__(self, num_update):\n",
-    "        self.base_lr = max(self.stop_factor_lr, self.base_lr * self.factor)\n",
-    "        return self.base_lr\n",
-    "\n",
-    "scheduler = FactorScheduler(factor=0.9, stop_factor_lr=1e-2, base_lr=2.0)\n",
-    "d2l.plot(d2l.arange(50), [scheduler(t) for t in range(50)])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "eb5f8ed5",
-   "metadata": {},
-   "source": [
-    "#### 11.11.3.2. 多因子调度器"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "0828bec8",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "class MultiFactorScheduler:\n",
-    "    def __init__(self, step, factor, base_lr):\n",
-    "        self.step = step\n",
-    "        self.factor = factor\n",
-    "        self.base_lr = base_lr\n",
-    "\n",
-    "    def __call__(self, epoch):\n",
-    "        if epoch in self.step:\n",
-    "            self.base_lr = self.base_lr * self.factor\n",
-    "            return self.base_lr\n",
-    "        else:\n",
-    "            return self.base_lr\n",
-    "\n",
-    "scheduler = MultiFactorScheduler(step=[15, 30], factor=0.5, base_lr=0.5)\n",
-    "d2l.plot(d2l.arange(num_epochs), [scheduler(t) for t in range(num_epochs)])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "111352a0",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "lr_list = d2l.tensor([scheduler(t) for t in range(num_epochs) for i in range(steps_per_epoch)])\n",
-    "trainer = nn.SGD(net.trainable_params(), lr_list)\n",
-    "train(net, train_iter, test_iter, num_epochs, loss, trainer)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "4c27a8f8",
-   "metadata": {},
-   "source": [
-    "#### 11.11.3.3. 余弦调度器"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 11,
-   "id": "8e8a5c3e",
+   "id": "c271bc7a",
    "metadata": {},
    "outputs": [
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+       "</svg>\n"
+      ],
+      "text/plain": [
+       "<Figure size 350x250 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "class FactorScheduler:\n",
+    "    def __init__(self, factor=1, stop_factor_lr=1e-7, base_lr=0.1):\n",
+    "        self.factor = factor\n",
+    "        self.stop_factor_lr = stop_factor_lr\n",
+    "        self.base_lr = base_lr\n",
+    "\n",
+    "    def __call__(self, num_update):\n",
+    "        self.base_lr = max(self.stop_factor_lr, self.base_lr * self.factor)\n",
+    "        return self.base_lr\n",
+    "\n",
+    "scheduler = FactorScheduler(factor=0.9, stop_factor_lr=1e-2, base_lr=2.0)\n",
+    "d2l.plot(d2l.arange(50).numpy(), [scheduler(t) for t in range(50)])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "eb5f8ed5",
+   "metadata": {},
+   "source": [
+    "#### 11.11.3.2. 多因子调度器"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "0828bec8",
+   "metadata": {},
+   "outputs": [
+    {
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+      "text/plain": [
+       "<Figure size 350x250 with 1 Axes>"
+      ]
+     },
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+     "output_type": "display_data"
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+   ],
+   "source": [
+    "class MultiFactorScheduler:\n",
+    "    def __init__(self, step, factor, base_lr):\n",
+    "        self.step = step\n",
+    "        self.factor = factor\n",
+    "        self.base_lr = base_lr\n",
+    "\n",
+    "    def __call__(self, epoch):\n",
+    "        if epoch in self.step:\n",
+    "            self.base_lr = self.base_lr * self.factor\n",
+    "            return self.base_lr\n",
+    "        else:\n",
+    "            return self.base_lr\n",
+    "\n",
+    "scheduler = MultiFactorScheduler(step=[15, 30], factor=0.5, base_lr=0.5)\n",
+    "d2l.plot(d2l.arange(num_epochs).numpy(), [scheduler(t) for t in range(num_epochs)])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "111352a0",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "train loss 0.175, train acc 0.934, test acc 0.892\n"
+     ]
+    },
+    {
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     }
    ],
    "source": [
-    "# 缺少 warmup, 所以自定义 CosineScheduler\n",
-    "scheduler = nn.CosineDecayLR(min_lr=0.01, max_lr=0.3, decay_steps=20)\n",
-    "scheduler(d2l.tensor(0))\n",
-    "d2l.plot(d2l.arange(num_epochs), scheduler(d2l.tensor([t for t in range(num_epochs)])))"
+    "net = net_fn()\n",
+    "lr_list = d2l.tensor([scheduler(t) for t in range(num_epochs) for i in range(steps_per_epoch)])\n",
+    "trainer = nn.SGD(net.trainable_params(), lr_list)\n",
+    "train(net, train_iter, test_iter, num_epochs, loss, trainer)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4c27a8f8",
+   "metadata": {},
+   "source": [
+    "#### 11.11.3.3. 余弦调度器"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 14,
    "id": "60ba8445",
    "metadata": {},
    "outputs": [
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     "        return self.base_lr\n",
     "\n",
     "scheduler = CosineScheduler(max_update=20, base_lr=0.3, final_lr=0.01)\n",
-    "d2l.plot(d2l.arange(num_epochs), [scheduler(t) for t in range(num_epochs)])"
+    "d2l.plot(d2l.arange(num_epochs).numpy(), [scheduler(t) for t in range(num_epochs)])"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 15,
    "id": "5312a7d2",
    "metadata": {},
    "outputs": [
@@ -3523,7 +5448,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "train loss 3089455025148719102164992.000, train acc 0.135, test acc 0.142\n"
+      "train loss 0.158, train acc 0.943, test acc 0.897\n"
      ]
     },
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    ],
    "source": [
     "net = net_fn()\n",
-    "\n",
     "lr_list = d2l.tensor([scheduler(t) for t in range(num_epochs) for i in range(steps_per_epoch)])\n",
     "trainer = nn.SGD(net.trainable_params(), lr_list)\n",
     "train(net, train_iter, test_iter, num_epochs, loss, trainer)"
@@ -4378,7 +6549,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 16,
    "id": "ee8a5ba8",
    "metadata": {},
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    ],
    "source": [
     "scheduler = CosineScheduler(20, warmup_steps=5, base_lr=0.3, final_lr=0.01)\n",
-    "d2l.plot(d2l.arange(num_epochs), [scheduler(t) for t in range(num_epochs)])"
+    "d2l.plot(d2l.arange(num_epochs).numpy(), [scheduler(t) for t in range(num_epochs)])"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 17,
    "id": "05973299",
    "metadata": {},
    "outputs": [
@@ -4870,7 +6957,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "train loss 66452015380.343, train acc 0.100, test acc 0.100\n"
+      "train loss 0.155, train acc 0.943, test acc 0.897\n"
      ]
     },
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+       "L 225.403125 91.770104 \n",
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        "   <g id=\"patch_3\">\n",
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@@ -5490,13 +7816,13 @@
        "z\n",
        "\" style=\"fill: #ffffff; opacity: 0.8; stroke: #cccccc; stroke-linejoin: miter\"/>\n",
        "    </g>\n",
-       "    <g id=\"line2d_26\">\n",
+       "    <g id=\"line2d_28\">\n",
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        "L 162.634375 20.298438 \n",
        "\" style=\"fill: none; stroke: #1f77b4; stroke-width: 1.5; stroke-linecap: square\"/>\n",
        "    </g>\n",
-       "    <g id=\"text_13\">\n",
+       "    <g id=\"text_14\">\n",
        "     <!-- train loss -->\n",
        "     <g transform=\"translate(170.634375 23.798438) scale(0.1 -0.1)\">\n",
        "      <defs>\n",
@@ -5655,13 +7981,13 @@
        "      <use xlink:href=\"#DejaVuSans-73\" x=\"405.615234\"/>\n",
        "     </g>\n",
        "    </g>\n",
-       "    <g id=\"line2d_27\">\n",
+       "    <g id=\"line2d_29\">\n",
        "     <path d=\"M 142.634375 34.976563 \n",
        "L 152.634375 34.976563 \n",
        "L 162.634375 34.976563 \n",
        "\" style=\"fill: none; stroke-dasharray: 5.55,2.4; stroke-dashoffset: 0; stroke: #bf00bf; stroke-width: 1.5\"/>\n",
        "    </g>\n",
-       "    <g id=\"text_14\">\n",
+       "    <g id=\"text_15\">\n",
        "     <!-- train acc -->\n",
        "     <g transform=\"translate(170.634375 38.476563) scale(0.1 -0.1)\">\n",
        "      <use xlink:href=\"#DejaVuSans-74\"/>\n",
@@ -5675,13 +8001,13 @@
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        "     </g>\n",
        "    </g>\n",
-       "    <g id=\"line2d_28\">\n",
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        "    </g>\n",
-       "    <g id=\"text_15\">\n",
+       "    <g id=\"text_16\">\n",
        "     <!-- test acc -->\n",
        "     <g transform=\"translate(170.634375 53.154688) scale(0.1 -0.1)\">\n",
        "      <use xlink:href=\"#DejaVuSans-74\"/>\n",
@@ -5698,7 +8024,7 @@
        "  </g>\n",
        " </g>\n",
        " <defs>\n",
-       "  <clipPath id=\"p2165b24d68\">\n",
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        "  </clipPath>\n",
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@@ -5746,6 +8072,19 @@
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
    "version": "3.9.5"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": true,
+   "sideBar": true,
+   "skip_h1_title": false,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {},
+   "toc_section_display": true,
+   "toc_window_display": false
   }
  },
  "nbformat": 4,
diff --git a/chapter_13_chapter_computer-vision/ssd.ipynb b/chapter_13_chapter_computer-vision/ssd.ipynb
index 80d6164..86c9687 100644
--- a/chapter_13_chapter_computer-vision/ssd.ipynb
+++ b/chapter_13_chapter_computer-vision/ssd.ipynb
@@ -29,7 +29,15 @@
    "execution_count": 1,
    "id": "e6901d6d",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[WARNING] ME(300375:47194544307584,MainProcess):2023-04-03-16:15:37.357.548 [mindspore/common/_decorator.py:40] 'Fills' is deprecated from version 2.0 and will be removed in a future version, use 'ops.fill' instead.\n"
+     ]
+    }
+   ],
    "source": [
     "%matplotlib inline\n",
     "import mindspore\n",
@@ -103,7 +111,7 @@
    "outputs": [],
    "source": [
     "def flatten_pred(pred):\n",
-    "    return d2l.flatten(pred.permute(0, 2, 3, 1)) # flatten不改变0轴的size\n",
+    "    return d2l.flatten(pred.permute(0, 2, 3, 1))\n",
     "\n",
     "def concat_preds(preds):\n",
     "    return d2l.concat([flatten_pred(p) for p in preds], axis=1)"
@@ -250,7 +258,7 @@
    "source": [
     "def blk_forward(X, blk, size, ratio, cls_predictor, bbox_predictor):\n",
     "    Y = blk(X)\n",
-    "    anchors = d2l.multibox_prior(Y, sizes=size, ratios=ratio)   ##### 需要第四章\n",
+    "    anchors = d2l.multibox_prior(Y, sizes=size, ratios=ratio)\n",
     "    cls_preds = cls_predictor(Y)\n",
     "    bbox_preds = bbox_predictor(Y)\n",
     "    return (Y, anchors, cls_preds, bbox_preds)"
@@ -363,7 +371,7 @@
    ],
    "source": [
     "batch_size = 32\n",
-    "train_iter, _ = d2l.load_data_bananas(batch_size) # "
+    "train_iter, _ = d2l.load_data_bananas(batch_size)"
    ]
   },
   {
@@ -413,318 +421,3147 @@
    "source": [
     "def cls_eval(cls_preds, cls_labels):\n",
     "    # 由于类别预测结果放在最后一维，argmax需要指定最后一维。\n",
-    "    return float((cls_preds.argmax(axis=-1).type(\n",
+    "    return float((cls_preds.argmax(axis=-1).astype(\n",
     "        cls_labels.dtype) == cls_labels).sum())\n",
     "\n",
     "def bbox_eval(bbox_preds, bbox_labels, bbox_masks):\n",
-    "#     return float((d2l.abs((bbox_labels - bbox_preds) * bbox_masks)).sum())\n",
     "    return float(((bbox_labels - bbox_preds) * bbox_masks).abs().sum())"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 18,
-   "id": "259d2abc",
+   "cell_type": "markdown",
+   "id": "6912bb2d",
    "metadata": {},
-   "outputs": [],
    "source": [
-    "from mindspore import ops\n",
-    "def box_iou(boxes1, boxes2):\n",
-    "    \"\"\"计算两个锚框或边界框列表中成对的交并比\"\"\"\n",
-    "    box_area = lambda boxes: ((boxes[:, 2] - boxes[:, 0]) *\n",
-    "                              (boxes[:, 3] - boxes[:, 1]))\n",
-    "    # boxes1,boxes2,areas1,areas2的形状:\n",
-    "    # boxes1：(boxes1的数量,4),\n",
-    "    # boxes2：(boxes2的数量,4),\n",
-    "    # areas1：(boxes1的数量,),\n",
-    "    # areas2：(boxes2的数量,)\n",
-    "    areas1 = box_area(boxes1)\n",
-    "    areas2 = box_area(boxes2)\n",
-    "    # inter_upperlefts,inter_lowerrights,inters的形状:\n",
-    "    # (boxes1的数量,boxes2的数量,2)\n",
-    "    inter_upperlefts = ops.maximum(boxes1[:, None, :2], boxes2[:, :2])\n",
-    "    inter_lowerrights = ops.minimum(boxes1[:, None, 2:], boxes2[:, 2:])\n",
-    "    inters = (inter_lowerrights - inter_upperlefts).clamp(min=0)\n",
-    "    # inter_areasandunion_areas的形状:(boxes1的数量,boxes2的数量)\n",
-    "    inter_areas = inters[:, :, 0] * inters[:, :, 1]\n",
-    "    union_areas = areas1[:, None] + areas2 - inter_areas\n",
-    "    return inter_areas / union_areas\n",
-    "\n",
-    "def assign_anchor_to_bbox(ground_truth, anchors, iou_threshold=0.5):\n",
-    "    \"\"\"将最接近的真实边界框分配给锚框\"\"\"\n",
-    "    num_anchors, num_gt_boxes = anchors.shape[0], ground_truth.shape[0]\n",
-    "    # 位于第i行和第j列的元素x_ij是锚框i和真实边界框j的IoU\n",
-    "    jaccard = box_iou(anchors, ground_truth)\n",
-    "    # 对于每个锚框，分配的真实边界框的张量\n",
-    "    anchors_bbox_map = ops.full((num_anchors,), -1, dtype=mindspore.int64)\n",
-    "    # 根据阈值，决定是否分配真实边界框\n",
-    "    indices, max_ious = ops.max(jaccard, axis=1)\n",
-    "    anc_i = ops.nonzero(max_ious >= iou_threshold).reshape(-1)\n",
-    "    box_j = ops.masked_select(indices, max_ious >= iou_threshold)\n",
-    "    anchors_bbox_map[anc_i] = box_j \n",
-    "    col_discard = ops.full((num_anchors,), -1)\n",
-    "    row_discard = ops.full((num_gt_boxes,), -1)\n",
-    "    for _ in range(num_gt_boxes):\n",
-    "        max_idx = ops.argmax(jaccard)\n",
-    "        box_idx = (max_idx % num_gt_boxes).long()\n",
-    "        anc_idx = (max_idx / num_gt_boxes).long()\n",
-    "        anchors_bbox_map[anc_idx] = box_idx\n",
-    "        jaccard[:, box_idx] = col_discard\n",
-    "        jaccard[anc_idx, :] = row_discard\n",
-    "    return anchors_bbox_map\n",
-    "\n",
-    "def offset_boxes(anchors, assigned_bb, eps=1e-6):\n",
-    "    \"\"\"对锚框偏移量的转换\"\"\"\n",
-    "    c_anc = d2l.box_corner_to_center(anchors)\n",
-    "    c_assigned_bb = d2l.box_corner_to_center(assigned_bb)\n",
-    "    offset_xy = 10 * (c_assigned_bb[:, :2] - c_anc[:, :2]) / c_anc[:, 2:]\n",
-    "    offset_wh = 5 * ops.log(eps + c_assigned_bb[:, 2:] / c_anc[:, 2:])\n",
-    "    offset = ops.concat([offset_xy, offset_wh], axis=1)\n",
-    "    return offset\n",
-    "\n",
-    "def multibox_target(anchors, labels):\n",
-    "    \"\"\"使用真实边界框标记锚框\"\"\"\n",
-    "    batch_size, anchors = labels.shape[0], anchors.squeeze(0)\n",
-    "    batch_offset, batch_mask, batch_class_labels = [], [], []\n",
-    "    num_anchors = anchors.shape[0]\n",
-    "    for i in range(batch_size):\n",
-    "        label = labels[i, :, :]\n",
-    "        anchors_bbox_map = assign_anchor_to_bbox(\n",
-    "            label[:, 1:], anchors)\n",
-    "        bbox_mask = ops.tile((anchors_bbox_map >= 0).float().unsqueeze(-1), (1, 4))\n",
-    "        # 将类标签和分配的边界框坐标初始化为零\n",
-    "        class_labels = ops.zeros(num_anchors, dtype=mindspore.int32)\n",
-    "        assigned_bb = ops.zeros((num_anchors, 4), dtype=mindspore.float32)\n",
-    "        # 使用真实边界框来标记锚框的类别。\n",
-    "        # 如果一个锚框没有被分配，标记其为背景（值为零）\n",
-    "        indices_true = ops.nonzero(anchors_bbox_map >= 0)\n",
-    "        bb_idx = anchors_bbox_map[indices_true]\n",
-    "        class_labels[indices_true] = label[bb_idx, 0].long() + 1 \n",
-    "        assigned_bb[indices_true] = label[bb_idx, 1:]\n",
-    "        # 偏移量转换\n",
-    "        offset = offset_boxes(anchors, assigned_bb) * bbox_mask\n",
-    "        batch_offset.append(offset.reshape(-1))\n",
-    "        batch_mask.append(bbox_mask.reshape(-1))\n",
-    "        batch_class_labels.append(class_labels)\n",
-    "    bbox_offset = ops.stack(batch_offset)\n",
-    "    bbox_mask = ops.stack(batch_mask)\n",
-    "    class_labels = ops.stack(batch_class_labels)\n",
-    "    return (bbox_offset, bbox_mask, class_labels)\n",
-    "\n",
-    "# print(anchors.shape, Y.shape) # (1, 5444, 4) (32, 1, 5)\n",
-    "# bbox_labels, bbox_masks, cls_labels = multibox_target(anchors, Y)"
+    "#### 13.7.2.3. 训练模型"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 19,
-   "id": "d80ea58d",
+   "id": "3870f282",
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "(32, 3, 256, 256) (32, 1, 5)\n"
+      "class err 2.85e-03, bbox mae 2.70e-03\n",
+      "6.8 examples/sec on \n"
      ]
+    },
+    {
+     "data": {
+      "image/svg+xml": [
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    ],
    "source": [
-    "#forward\n",
-    "for X, Y in train_iter:\n",
-    "    print(X.shape, Y.shape)\n",
-    "    break"
+    "num_epochs, timer = 20, d2l.Timer()\n",
+    "animator = d2l.Animator(xlabel='epoch', xlim=[1, num_epochs],\n",
+    "                        legend=['class error', 'bbox mae'])\n",
+    "\n",
+    "def forward_fn(X, Y):\n",
+    "    # 生成多尺度的锚框，为每个锚框预测类别和偏移量\n",
+    "    anchors, cls_preds, bbox_preds = net(X)\n",
+    "    # 为每个锚框标注类别和偏移量\n",
+    "    bbox_labels, bbox_masks, cls_labels = d2l.multibox_target(anchors, Y)\n",
+    "    # 根据类别和偏移量的预测和标注值计算损失函数\n",
+    "    l = calc_loss(cls_preds, cls_labels, bbox_preds, bbox_labels,\n",
+    "                  bbox_masks).mean()\n",
+    "    return l, cls_preds, cls_labels, bbox_preds, bbox_labels, bbox_masks, bbox_labels\n",
+    "    \n",
+    "grad_fn = mindspore.value_and_grad(forward_fn, None, trainer.parameters, has_aux=True)\n",
+    "    \n",
+    "def train_step(inputs, targets):\n",
+    "    (l, cls_preds, cls_labels, bbox_preds, bbox_labels, bbox_masks, bbox_labels), grads = grad_fn(inputs, targets)\n",
+    "    trainer(grads)\n",
+    "    return l, cls_preds, cls_labels, bbox_preds, bbox_labels, bbox_masks, bbox_labels\n",
+    "    \n",
+    "for epoch in range(num_epochs):\n",
+    "    # 训练精确度的和，训练精确度的和中的示例数\n",
+    "    # 绝对误差的和，绝对误差的和中的示例数\n",
+    "    metric = d2l.Accumulator(4)\n",
+    "    net.set_train()\n",
+    "    for features, target in train_iter:\n",
+    "        timer.start()\n",
+    "        l, cls_preds, cls_labels, bbox_preds, bbox_labels, bbox_masks, bbox_labels = train_step(features, target)\n",
+    "        metric.add(cls_eval(cls_preds, cls_labels), cls_labels.numel(),\n",
+    "                   bbox_eval(bbox_preds, bbox_labels, bbox_masks),\n",
+    "                   bbox_labels.numel())\n",
+    "    cls_err, bbox_mae = 1 - metric[0] / metric[1], metric[2] / metric[3]\n",
+    "    animator.add(epoch + 1, (cls_err, bbox_mae))\n",
+    "print(f'class err {cls_err:.2e}, bbox mae {bbox_mae:.2e}')\n",
+    "print(f'{train_iter.get_dataset_size()/ timer.stop():.1f} examples/sec on ')"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 20,
-   "id": "ca1c0d14",
+   "cell_type": "markdown",
+   "id": "425916e0",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "output anchors: (1, 5444, 4)\n",
-      "output class preds: (32, 5444, 2)\n",
-      "output bbox preds: (32, 21776)\n"
-     ]
-    }
-   ],
    "source": [
-    "anchors, cls_preds, bbox_preds = net(X)\n",
-    "print('output anchors:', anchors.shape)\n",
-    "print('output class preds:', cls_preds.shape)\n",
-    "print('output bbox preds:', bbox_preds.shape)"
+    "### 13.7.3. 预测目标"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
-   "id": "d1f7601f",
+   "execution_count": 20,
+   "id": "a3b40a56",
    "metadata": {
     "scrolled": true
    },
+   "outputs": [],
+   "source": [
+    "img = mindspore.dataset.vision.read_image('../img/banana.jpg').astype('float32')\n",
+    "X = mindspore.Tensor(img.transpose(2,0,1), dtype=mindspore.float32).unsqueeze(0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "ab17d783",
+   "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[WARNING] KERNEL(269803,2aecc71afd80,python):2023-02-23-20:56:28.763.896 [mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/cuda_class/cuda_class_common.h:51] CalShapesSizeInBytes] For 'Argmax', the shapes[0] is ( )\n",
-      "[WARNING] KERNEL(269803,2aecc71afd80,python):2023-02-23-20:56:28.763.965 [mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/cuda_class/cuda_class_common.h:51] CalShapesSizeInBytes] For 'Argmax', the shapes[0] is ( )\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "bbox_labels: (32, 21776) (32, 21776)\n",
-      "bbox_masks: (32, 21776) Float32\n",
-      "cls_labels: (32, 5444) Int32\n"
+      "[WARNING] KERNEL(300375,2aec9fa87700,python):2023-04-03-17:11:46.720.731 [mindspore/ccsrc/kernel/kernel.h:514] CheckShapeNull] For 'Reshape', the shape of input cannot contain zero, but got (0, 1)\n",
+      "[WARNING] KERNEL(300375,2aec9fa87700,python):2023-04-03-17:11:46.723.968 [mindspore/ccsrc/kernel/kernel.h:514] CheckShapeNull] For 'Add', the shape of input_0 cannot contain zero, but got (0)\n",
+      "[WARNING] KERNEL(300375,2aec9fa87700,python):2023-04-03-17:11:46.734.618 [mindspore/ccsrc/kernel/kernel.h:514] CheckShapeNull] For 'Gather', the shape of indices cannot contain zero, but got (0)\n",
+      "<__array_function__ internals>:200: RuntimeWarning: invalid value encountered in cast\n"
      ]
     }
    ],
    "source": [
-    "bbox_labels, bbox_masks, cls_labels = multibox_target(anchors, Y)\n",
-    "print('bbox_labels:', bbox_labels.shape, bbox_labels.shape)\n",
-    "print('bbox_masks:', bbox_masks.shape, bbox_masks.dtype)\n",
-    "print('cls_labels:', cls_labels.shape, cls_labels.dtype)"
+    "def predict(X):\n",
+    "    net.set_train(False)\n",
+    "    anchors, cls_preds, bbox_preds = net(X)\n",
+    "    cls_probs = d2l.softmax(cls_preds, axis=2).permute(0, 2, 1)\n",
+    "    output = d2l.multibox_detection(cls_probs, bbox_preds, anchors) # d2l.\n",
+    "    idx = [i for i, row in enumerate(output[0]) if row[0] != -1]\n",
+    "    return output[0][idx]\n",
+    "\n",
+    "output = predict(X)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
-   "id": "993302d8",
+   "execution_count": 26,
+   "id": "7c7d41f6",
    "metadata": {},
    "outputs": [
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+       "   </g>\n",
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-       "Tensor(shape=[], dtype=Float32, value= 0.69856)"
+       "<Figure size 500x500 with 1 Axes>"
       ]
      },
-     "execution_count": 22,
      "metadata": {},
-     "output_type": "execute_result"
+     "output_type": "display_data"
     }
    ],
    "source": [
-    "l = calc_loss(cls_preds, cls_labels, bbox_preds, bbox_labels, bbox_masks).mean()\n",
-    "l"
+    "def display(img, output, threshold):\n",
+    "    d2l.set_figsize((5, 5))\n",
+    "    fig = d2l.plt.imshow(img)\n",
+    "    for row in output:\n",
+    "        score = float(row[1])\n",
+    "        if score < threshold:\n",
+    "            continue\n",
+    "        h, w = img.shape[0:2]\n",
+    "        bbox = [row[2:6] * d2l.tensor((w, h, w, h))]\n",
+    "        d2l.show_bboxes(fig.axes, bbox, '%.2f' % score, 'w')\n",
+    "img = X.numpy().squeeze().transpose(1,2,0)/255\n",
+    "display(img, output, threshold=0.9)"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "9c78354a",
+   "id": "a49128d0",
    "metadata": {},
    "source": [
-    "# BUG 直接运行的话，这里会卡住"
+    "### 13.7.5. 练习"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "5e0d0317",
+   "execution_count": 27,
+   "id": "77b6ea35",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
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    "source": [
-    "#forward\n",
-    "for X, Y in train_iter:\n",
-    "    print(X.shape, Y.shape)\n",
-    "    anchors, cls_preds, bbox_preds = net(X)\n",
-    "    print('output anchors:', anchors.shape)\n",
-    "    print('output class preds:', cls_preds.shape)\n",
-    "    print('output bbox preds:', bbox_preds.shape)\n",
+    "import numpy as np\n",
+    "def smooth_l1(data, scalar):\n",
+    "    out = []\n",
+    "    for i in data:\n",
+    "        if abs(i) < 1 / (scalar ** 2):\n",
+    "            out.append(((scalar * i) ** 2) / 2)\n",
+    "        else:\n",
+    "            out.append(abs(i) - 0.5 / (scalar ** 2))\n",
     "    \n",
-    "    bbox_labels, bbox_masks, cls_labels = multibox_target(anchors, Y)\n",
-    "    print('bbox_labels:', bbox_labels.shape, bbox_labels.shape)\n",
-    "    print('bbox_masks:', bbox_masks.shape, bbox_masks.dtype)\n",
-    "    print('cls_labels:', cls_labels.shape, cls_labels.dtype)    \n",
-    "    \n",
-    "    l = calc_loss(cls_preds, cls_labels, bbox_preds, bbox_labels,\n",
-    "                  bbox_masks)\n",
-    "    print(l)\n",
-    "    break"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "6ef20694",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "X, Y = next(train_iter)\n",
-    "print(X.shape, Y.shape)\n",
-    "anchors, cls_preds, bbox_preds = net(X)\n",
-    "print('output anchors:', anchors.shape)\n",
-    "print('output class preds:', cls_preds.shape)\n",
-    "print('output bbox preds:', bbox_preds.shape)\n",
+    "    return np.array(out)\n",
     "\n",
-    "bbox_labels, bbox_masks, cls_labels = multibox_target(anchors, Y)\n",
-    "print('bbox_labels:', bbox_labels.shape, bbox_labels.shape)\n",
-    "print('bbox_masks:', bbox_masks.shape, bbox_masks.dtype)\n",
-    "print('cls_labels:', cls_labels.shape, cls_labels.dtype)    \n",
+    "sigmas = [10, 1, 0.5]\n",
+    "lines = ['-', '--', '-.']\n",
+    "x = d2l.arange(-2, 2, 0.1).numpy()\n",
+    "d2l.set_figsize()\n",
     "\n",
-    "l = calc_loss(cls_preds, cls_labels, bbox_preds, bbox_labels,\n",
-    "              bbox_masks)\n",
-    "print(l)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "6912bb2d",
-   "metadata": {},
-   "source": [
-    "#### 13.7.2.3. 训练模型"
+    "for l, s in zip(lines, sigmas):\n",
+    "    y = smooth_l1(x, scalar=s)\n",
+    "    \n",
+    "    d2l.plt.plot(x, y, l, label='sigma=%.1f' % s)\n",
+    "d2l.plt.legend();"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "3870f282",
+   "execution_count": 28,
+   "id": "289f5472",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
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+       " <defs>\n",
+       "  <clipPath id=\"p05774b41ba\">\n",
+       "   <rect x=\"20.5625\" y=\"7.2\" width=\"195.3\" height=\"138.6\"/>\n",
+       "  </clipPath>\n",
+       " </defs>\n",
+       "</svg>\n"
+      ],
+      "text/plain": [
+       "<Figure size 350x250 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "# num_epochs, timer = 20, d2l.Timer()\n",
-    "# animator = d2l.Animator(xlabel='epoch', xlim=[1, num_epochs],\n",
-    "#                         legend=['class error', 'bbox mae'])\n",
+    "def focal_loss(gamma, x):\n",
+    "    return -(1 - x) ** gamma * np.log(x)\n",
     "\n",
-    "# def forward_fn(X, Y):\n",
-    "#     # 生成多尺度的锚框，为每个锚框预测类别和偏移量\n",
-    "#     anchors, cls_preds, bbox_preds = net(X)\n",
-    "#     # 为每个锚框标注类别和偏移量\n",
-    "#     bbox_labels, bbox_masks, cls_labels = multibox_target(anchors, Y) # d2l.\n",
-    "#     # 根据类别和偏移量的预测和标注值计算损失函数\n",
-    "#     l = calc_loss(cls_preds, cls_labels, bbox_preds, bbox_labels,\n",
-    "#                   bbox_masks).mean()\n",
-    "#     return l, cls_preds, cls_labels, bbox_preds, bbox_labels, bbox_masks, bbox_labels\n",
-    "    \n",
-    "# grad_fn = mindspore.value_and_grad(forward_fn, None, trainer.parameters, has_aux=True)\n",
-    "    \n",
-    "# def train_step(inputs, targets):\n",
-    "#     (l, cls_preds, cls_labels, bbox_preds, bbox_labels, bbox_masks, bbox_labels), grads = grad_fn(inputs, targets)\n",
-    "#     trainer(grads)\n",
-    "#     return l, cls_preds, cls_labels, bbox_preds, bbox_labels, bbox_masks, bbox_labels\n",
-    "    \n",
-    "# for epoch in range(num_epochs):\n",
-    "#     # 训练精确度的和，训练精确度的和中的示例数\n",
-    "#     # 绝对误差的和，绝对误差的和中的示例数\n",
-    "#     metric = d2l.Accumulator(4)\n",
-    "#     net.set_train()\n",
-    "#     for features, target in train_iter:\n",
-    "#         timer.start()\n",
-    "#         print(epoch)\n",
-    "#         l, cls_preds, cls_labels, bbox_preds, bbox_labels, bbox_masks, bbox_labels = train_step(features, target)\n",
-    "#         metric.add(cls_eval(cls_preds, cls_labels), cls_labels.numel(),\n",
-    "#                    bbox_eval(bbox_preds, bbox_labels, bbox_masks),\n",
-    "#                    bbox_labels.numel())\n",
-    "#     cls_err, bbox_mae = 1 - metric[0] / metric[1], metric[2] / metric[3]\n",
-    "#     animator.add(epoch + 1, (cls_err, bbox_mae))\n",
-    "# print(f'class err {cls_err:.2e}, bbox mae {bbox_mae:.2e}')\n",
-    "# print(f'{len(train_iter.dataset) / timer.stop():.1f} examples/sec on '\n",
-    "#       f'{str(device)}')"
+    "x = np.arange(0.01, 1, 0.01)\n",
+    "for l, gamma in zip(lines, [0, 1, 5]):\n",
+    "    y = d2l.plt.plot(x, focal_loss(gamma, x), l, label='gamma=%.1f' % gamma)\n",
+    "d2l.plt.legend();"
    ]
   }
  ],
diff --git a/d2l/mindspore.py b/d2l/mindspore.py
index 7b9523b..5a30d75 100644
--- a/d2l/mindspore.py
+++ b/d2l/mindspore.py
@@ -2485,50 +2485,6 @@ def resnet_block(in_channels, out_channels, num_residuals,
 
     return net
 
-def train_batch_ch13(train_step, X, y):
-    """用GPU进行小批量训练"""
-    l, pred = train_step(X, y)
-    train_loss_sum = l.sum()
-    train_acc_sum = d2l.accuracy(pred, y)
-    return train_loss_sum, train_acc_sum
-
-def train_ch13(net, train_iter, test_iter, loss, trainer, num_epochs):
-    """用GPU进行模型训练"""
-    timer, num_batches = d2l.Timer(), train_iter.get_dataset_size()
-    animator = d2l.Animator(xlabel='epoch', xlim=[1, num_epochs], ylim=[0, 1],
-                            legend=['train loss', 'train acc', 'test acc'])
-
-    def forward_fn(inputs, targets):
-        logits = net(inputs)
-        l = loss(logits, targets)
-        return l, logits
-
-    grad_fn = mindspore.value_and_grad(forward_fn, None, trainer.parameters, has_aux=True)
-
-    def train_step(inputs, targets):
-        (l, logits), grads = grad_fn(inputs, targets)
-        trainer(grads)
-        return l, logits
-
-    for epoch in range(num_epochs):
-        # 4个维度：储存训练损失，训练准确度，实例数，特点数
-        metric = d2l.Accumulator(4)
-        net.set_train()
-        for i, (features, labels) in enumerate(train_iter):
-            timer.start()
-            l, acc = train_batch_ch13(
-                train_step, features, labels)  # , loss, trainer, devices
-            metric.add(l, acc, labels.shape[0], labels.numel())
-            timer.stop()
-            if (i + 1) % (num_batches // 5) == 0 or i == num_batches - 1:
-                animator.add(epoch + (i + 1) / num_batches,
-                             (metric[0] / metric[2], metric[1] / metric[3],
-                              None))
-        test_acc = d2l.evaluate_accuracy_gpu(net, test_iter)
-        animator.add(epoch + 1, (None, None, test_acc))
-    print(f'loss {metric[0] / metric[2]:.3f}, train acc '
-          f'{metric[1] / metric[3]:.3f}, test acc {test_acc:.3f}')
-    print(f'{metric[2] * num_epochs / timer.sum():.1f} examples/sec')
 
 def multibox_prior(data, sizes, ratios):
     """生成以每个像素为中心具有不同形状的锚框"""
@@ -2648,7 +2604,7 @@ def assign_anchor_to_bbox(ground_truth, anchors, iou_threshold=0.5):
     # 对于每个锚框，分配的真实边界框的张量
     anchors_bbox_map = ops.full((num_anchors,), -1, dtype=mindspore.int64)
     # 根据阈值，决定是否分配真实边界框
-    indices, max_ious = ops.max(jaccard, axis=1)
+    max_ious, indices  = ops.max(jaccard, axis=1)
     anc_i = ops.nonzero(max_ious >= iou_threshold).reshape(-1)
     box_j = ops.masked_select(indices, max_ious >= iou_threshold)
     anchors_bbox_map[anc_i] = box_j
@@ -2683,14 +2639,14 @@ def multibox_target(anchors, labels):
             label[:, 1:], anchors)
         bbox_mask = ops.tile((anchors_bbox_map >= 0).float().unsqueeze(-1), (1, 4))
         # 将类标签和分配的边界框坐标初始化为零
-        class_labels = ops.zeros(num_anchors, dtype=mindspore.int32)
+        class_labels = ops.zeros(num_anchors, dtype=mindspore.int64)
         assigned_bb = ops.zeros((num_anchors, 4), dtype=mindspore.float32)
         # 使用真实边界框来标记锚框的类别。
         # 如果一个锚框没有被分配，标记其为背景（值为零）
         indices_true = ops.nonzero(anchors_bbox_map >= 0)
         bb_idx = anchors_bbox_map[indices_true]
-        class_labels[indices_true] = label[bb_idx, 0].long() + 1
-        assigned_bb[indices_true] = label[bb_idx, 1:]
+        class_labels[indices_true] = label[:, 0][bb_idx].long() + 1
+        assigned_bb[indices_true] = label[:, 1:][bb_idx]
         # 偏移量转换
         offset = offset_boxes(anchors, assigned_bb) * bbox_mask
         batch_offset.append(offset.reshape(-1))
@@ -2698,7 +2654,7 @@ def multibox_target(anchors, labels):
         batch_class_labels.append(class_labels)
     bbox_offset = ops.stack(batch_offset)
     bbox_mask = ops.stack(batch_mask)
-    class_labels = ops.stack(batch_class_labels)
+    class_labels = ops.stack(batch_class_labels).astype('int32')
     return (bbox_offset, bbox_mask, class_labels)
 
 d2l.DATA_HUB['banana-detection'] = (d2l.DATA_URL + 'banana-detection.zip',
@@ -2746,6 +2702,70 @@ def load_data_bananas(batch_size):
     return train_iter, val_iter
 
 
+def offset_inverse(anchors, offset_preds):
+    """根据带有预测偏移量的锚框来预测边界框"""
+    anc = d2l.box_corner_to_center(anchors)
+    pred_bbox_xy = (offset_preds[:, :2] * anc[:, 2:] / 10) + anc[:, :2]
+    pred_bbox_wh = ops.exp(offset_preds[:, 2:] / 5) * anc[:, 2:]
+    pred_bbox = ops.concat((pred_bbox_xy, pred_bbox_wh), axis=1)
+    predicted_bbox = d2l.box_center_to_corner(pred_bbox)
+    return predicted_bbox
+
+
+def nms(boxes, scores, iou_threshold):
+    """对预测边界框的置信度进行排序"""
+    B = ops.argsort(scores, axis=-1, descending=True)
+    keep = []  # 保留预测边界框的指标
+    while B.numel() > 0:
+        i = B[0]
+        keep.append(i.asnumpy())
+        if B.numel() == 1: break
+        iou = box_iou(boxes[i].reshape(-1, 4),
+                      boxes[:, :][B[1:]].reshape(-1, 4)).reshape(-1)
+        inds = ops.nonzero(iou <= iou_threshold).reshape(-1)
+        B = B[inds + 1]
+    return mindspore.Tensor(keep)
+
+
+def multibox_detection(cls_probs, offset_preds, anchors, nms_threshold=0.5,
+                       pos_threshold=0.009999999):
+    """使用非极大值抑制来预测边界框"""
+    batch_size = cls_probs.shape[0]
+    anchors = anchors.squeeze(0)
+    num_classes, num_anchors = cls_probs.shape[1], cls_probs.shape[2]
+    out = []
+    for i in range(batch_size):
+        cls_prob, offset_pred = cls_probs[i], offset_preds[i].reshape(-1, 4)
+        conf, class_id = ops.max(cls_prob[1:], 0)
+        predicted_bb = offset_inverse(anchors, offset_pred)
+        keep = nms(predicted_bb, conf, nms_threshold)
+        # 找到所有的non_keep索引，并将类设置为背景
+        all_idx = ops.arange(num_anchors, dtype=mindspore.int64)
+        combined = ops.concat((keep, all_idx))
+        unique = ops.unique(combined)
+        uniques, _ = unique[0], unique[1]
+        # 统计未重复的，mindspore的unique无法统计同一个元素出现次数
+        counts = mnp.bincount(combined)
+        non_keep = mindspore.Tensor([int(uniques[i]) for i in range(len(counts))
+                                     if (counts[i] == 1)])
+        all_id_sorted = ops.concat((keep, non_keep))
+        class_id[non_keep] = -1
+        class_id = class_id[all_id_sorted]
+        conf, predicted_bb = conf[all_id_sorted], predicted_bb[all_id_sorted]
+        # pos_threshold是一个用于非背景预测的阈值
+        below_min_idx = (conf < pos_threshold)
+        class_id = mindspore.Tensor.float(class_id)
+        class_id[below_min_idx] = -1
+        for j in range(len(conf)):
+            if below_min_idx[j] == True:
+                conf[j] = 1 - conf[j]
+
+        pred_info = ops.concat((class_id.unsqueeze(1),
+                                conf.unsqueeze(1).astype('float32'),
+                                predicted_bb), axis=1)
+        out.append(pred_info)
+    return ops.stack(out)
+
 d2l.DATA_HUB['voc2012'] = (d2l.DATA_URL + 'VOCtrainval_11-May-2012.tar',
                            '4e443f8a2eca6b1dac8a6c57641b67dd40621a49')
 
@@ -3016,6 +3036,7 @@ def get_pooled_rois(self,feature_map, roi_batch):
 square = ops.square
 sqrt = ops.sqrt
 sign = ops.sign
+softmax = ops.softmax
 meshgrid = ops.meshgrid
 linspace = ops.linspace
 zeros_like = ops.zeros_like