Multilayer Perceptron.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "fa67f182-e31f-4400-a1e1-861365f3f910",
   "metadata": {},
   "source": [
    "## 数据导入"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "1e85bb1e-0bca-4b8f-965d-add4b6740e92",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torchvision\n",
    "from torch.utils import data\n",
    "from torchvision import transforms\n",
    "from d2l import torch as d2l\n",
    "\n",
    "batch_size=256\n",
    "\n",
    "d2l.use_svg_display()\n",
    "trans = transforms.ToTensor()\n",
    "mnist_train = torchvision.datasets.FashionMNIST(root=\"../data\",train=True,transform=trans,download=True)\n",
    "mnist_test = torchvision.datasets.FashionMNIST(root=\"../data\",train=False,transform=trans,download=True)\n",
    "\n",
    "train_iter = data.DataLoader(mnist_train,batch_size,shuffle=True,num_workers=4)\n",
    "test_iter = data.DataLoader(mnist_test,batch_size,shuffle=False,num_workers=4)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ffff6312-08d2-4645-a13c-dd21921eea6f",
   "metadata": {},
   "source": [
    "## 网络构建"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "28e67372-eb09-49a9-a3c8-9d62ffb9c1b3",
   "metadata": {},
   "outputs": [],
   "source": [
    "from torch.nn import Parameter\n",
    "\n",
    "input = 28*28\n",
    "output = 10\n",
    "hidden_layer_nodes = 256\n",
    "\n",
    "W_1 = Parameter(torch.normal(0,0.01,(input,hidden_layer_nodes),requires_grad=True))\n",
    "b_1 = Parameter(torch.zeros(hidden_layer_nodes,requires_grad=True))\n",
    "W_2 = Parameter(torch.normal(0,0.01,(hidden_layer_nodes,output),requires_grad=True))\n",
    "b_2 = Parameter(torch.zeros(output,requires_grad=True))\n",
    "parameters = (W_1,b_1,W_2,b_2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ed8f8a28-87d6-4982-9b04-52f3ac9ec99c",
   "metadata": {},
   "outputs": [],
   "source": [
    "def relu(X):\n",
    "    zeros = torch.zeros_like(X)\n",
    "    return torch.max(zeros,X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "11a17c46-62cf-4ae9-8779-20fd791a4734",
   "metadata": {},
   "outputs": [],
   "source": [
    "def net(X,W_1,b_1,W_2,b_2):\n",
    "    X = X.reshape((-1,input))\n",
    "    Hidden = relu(torch.matmul(X,W_1) + b_1)\n",
    "    Output = torch.matmul(Hidden,W_2) + b_2\n",
    "    return Output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "1a2115fb-da1e-4d52-8d22-7c2d9bd09bdb",
   "metadata": {},
   "outputs": [],
   "source": [
    "loss = torch.nn.CrossEntropyLoss()\n",
    "optim = torch.optim.SGD(parameters,lr=0.05)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c25f9b9a-07cb-4b2f-85fa-185703486c29",
   "metadata": {},
   "source": [
    "## 训练"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "a5d89e3a-1316-43cb-8cbc-8b46e8654a87",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch1    test_acc 0.6848\n",
      "epoch2    test_acc 0.7583\n",
      "epoch3    test_acc 0.7911\n",
      "epoch4    test_acc 0.7914\n",
      "epoch5    test_acc 0.809\n",
      "epoch6    test_acc 0.8145\n",
      "epoch7    test_acc 0.8216\n",
      "epoch8    test_acc 0.8276\n",
      "epoch9    test_acc 0.8029\n",
      "epoch10    test_acc 0.8337\n"
     ]
    }
   ],
   "source": [
    "epochs = 10\n",
    "\n",
    "for epoch in range(epochs):\n",
    "    for X,y in train_iter:\n",
    "        l = loss(net(X,W_1,b_1,W_2,b_2),y)\n",
    "        optim.zero_grad()\n",
    "        l.backward()\n",
    "        optim.step()\n",
    "    with torch.no_grad():\n",
    "        print(f\"epoch{epoch+1}    test_acc {d2l.evaluate_accuracy_gpu(lambda X:net(X,W_1,b_1,W_2,b_2),iter(test_iter))}\") "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c4947626-a0c8-44e2-8849-a0d058ad592f",
   "metadata": {},
   "outputs": [
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     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
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      ],
      "text/plain": [
       "<Figure size 900x150 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "n = 6\n",
    "preds = d2l.get_fashion_mnist_labels(net(X,W_1,b_1,W_2,b_2).argmax(1))\n",
    "trues = d2l.get_fashion_mnist_labels(y)\n",
    "titles = [pred + \"\\n\" + true for pred,true in zip(preds,trues)]\n",
    "images = X[0:6].reshape((n,28,28))\n",
    "d2l.show_images(images,1,n,titles=titles)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f36dbf80-ce76-4a86-9740-31c95f59b4c8",
   "metadata": {},
   "source": [
    "## 利用 torch.nn 实现"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "33852703-d8f7-4e9e-9426-f7b0a3211f12",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch.nn as nn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "c580d791-16b7-439d-bb7a-7bcd2ba32bca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sequential(\n",
       "  (0): Flatten(start_dim=1, end_dim=-1)\n",
       "  (1): Linear(in_features=784, out_features=256, bias=True)\n",
       "  (2): ReLU()\n",
       "  (3): Linear(in_features=256, out_features=10, bias=True)\n",
       "  (4): Softmax(dim=1)\n",
       ")"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "net = nn.Sequential(nn.Flatten(),\n",
    "                    nn.Linear(28*28,256),\n",
    "                    nn.ReLU(),\n",
    "                    nn.Linear(256,10),\n",
    "                    nn.Softmax(dim=1))\n",
    "\n",
    "def init_layer(m):\n",
    "    if type(m) == nn.Linear:\n",
    "        m.weight.data.normal_(0,0.01)\n",
    "        m.bias.data.fill_(0)\n",
    "\n",
    "loss = nn.CrossEntropyLoss()\n",
    "trainer = torch.optim.SGD(net.parameters(),lr=0.05)\n",
    "\n",
    "net.apply(init_layer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "3f994c9f-7fb5-4869-9275-4751f4707558",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_iter = data.DataLoader(mnist_train,batch_size,shuffle=True,num_workers=4)\n",
    "test_iter = data.DataLoader(mnist_test,batch_size,shuffle=False,num_workers=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "36586817-04ac-47a9-9217-3ec440b391ab",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " epoch5 test_acc 0.6667\n",
      " epoch10 test_acc 0.7561\n",
      " epoch15 test_acc 0.7787\n",
      " epoch20 test_acc 0.7887\n",
      " epoch25 test_acc 0.7923\n",
      " epoch30 test_acc 0.7955\n",
      " epoch35 test_acc 0.8013\n",
      " epoch40 test_acc 0.8023\n",
      " epoch45 test_acc 0.8057\n"
     ]
    }
   ],
   "source": [
    "from torch.nn.functional import one_hot\n",
    "epochs = 45\n",
    "\n",
    "for epoch in range(epochs):\n",
    "    for X,y in train_iter:\n",
    "        l = loss(net(X),y)\n",
    "        trainer.zero_grad()\n",
    "        l.backward()\n",
    "        trainer.step()\n",
    "    with torch.no_grad():\n",
    "        if (epoch+1)%5 == 0:\n",
    "            print(f\" epoch{epoch+1} test_acc {d2l.evaluate_accuracy_gpu(net,iter(test_iter))}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "e0891e29-ecdc-49e2-ae25-216749f97ebc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([<Axes: title={'center': 'ankle boot\\nankle boot'}>,\n",
       "       <Axes: title={'center': 'pullover\\npullover'}>,\n",
       "       <Axes: title={'center': 'trouser\\ntrouser'}>,\n",
       "       <Axes: title={'center': 'trouser\\ntrouser'}>,\n",
       "       <Axes: title={'center': 'pullover\\nshirt'}>,\n",
       "       <Axes: title={'center': 'trouser\\ntrouser'}>], dtype=object)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
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      ],
      "text/plain": [
       "<Figure size 900x150 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "n = 6\n",
    "X,y = next(iter(test_iter))\n",
    "preds = d2l.get_fashion_mnist_labels(net(X).argmax(1))\n",
    "trues = d2l.get_fashion_mnist_labels(y)\n",
    "titles = [pred + \"\\n\" + true for pred,true in zip(preds,trues)]\n",
    "images = X[0:6].reshape((n,28,28))\n",
    "d2l.show_images(images,1,n,titles=titles)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7929cc83-21e7-4f20-81a7-efb2e1af774a",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
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