AI-learning/lab/7_Muticlass.ipynb

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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 多分类问题 - 手写数字识别\n",
    "\n",
    "## 数据集\n",
    "- minst数据集(手写数字数据集)\n",
    "\n",
    "## 激活函数\n",
    "- softmax\n",
    "\n",
    "## 损失函数\n",
    "- 交叉熵\n",
    "\n",
    "## 优化器\n",
    "- 梯度下降\n",
    "\n",
    "## 模型\n",
    "- 全连接层\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 导库\n",
    "import tensorflow as tf\n",
    "from tensorflow.keras import Sequential\n",
    "from tensorflow.keras.layers import Dense\n",
    "from tensorflow.keras.losses import SparseCategoricalCrossentropy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# TEST\n",
    "model = Sequential([Dense(units=25,activation='relu'),\n",
    "                    Dense(units=15,activation='relu'),\n",
    "                    Dense(units=10,activation='softmax')])\n",
    "model.compile(loss=SparseCategoricalCrossentropy())\n",
    "# 输出模型\n",
    "print(model.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 官方实例\n",
    "\n",
    "import tensorflow as tf\n",
    "mnist = tf.keras.datasets.mnist\n",
    "\n",
    "(x_train, y_train),(x_test, y_test) = mnist.load_data()\n",
    "x_train, x_test = x_train / 255.0, x_test / 255.0\n",
    "\n",
    "model = tf.keras.models.Sequential([\n",
    "  tf.keras.layers.Flatten(input_shape=(28, 28)),\n",
    "  tf.keras.layers.Dense(128, activation='relu'),\n",
    "  tf.keras.layers.Dropout(0.2),\n",
    "  tf.keras.layers.Dense(10, activation='softmax')\n",
    "])\n",
    "\n",
    "model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=0.001),\n",
    "  loss=SparseCategoricalCrossentropy(),\n",
    "  metrics=['accuracy'])\n",
    "\n",
    "model.fit(x_train, y_train, epochs=5)\n",
    "model.evaluate(x_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 用torch实现\n",
    "\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "from torchvision import datasets, transforms\n",
    "\n",
    "# 数据预处理\n",
    "transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.5,), (0.5,))])\n",
    "\n",
    "# 加载数据集\n",
    "trainset = datasets.MNIST(root='./data', train=True, download=True, transform=transform)\n",
    "trainloader = torch.utils.data.DataLoader(trainset, batch_size=64, shuffle=True)\n",
    "\n",
    "testset = datasets.MNIST(root='./data', train=False, download=True, transform=transform)\n",
    "testloader = torch.utils.data.DataLoader(testset, batch_size=64, shuffle=False)\n",
    "\n",
    "# 定义模型\n",
    "class SimpleNet(nn.Module):\n",
    "    def __init__(self):\n",
    "        super(SimpleNet, self).__init__()\n",
    "        self.flatten = nn.Flatten()\n",
    "        self.fc1 = nn.Linear(28 * 28, 128)\n",
    "        self.dropout = nn.Dropout(0.2)\n",
    "        self.fc2 = nn.Linear(128, 10)\n",
    "\n",
    "    def forward(self, x):\n",
    "        x = self.flatten(x)\n",
    "        x = torch.relu(self.fc1(x))\n",
    "        x = self.dropout(x)\n",
    "        x = self.fc2(x)\n",
    "        return x\n",
    "\n",
    "model = SimpleNet()\n",
    "\n",
    "if torch.backends.mps.is_available():\n",
    "    device = torch.device(\"mps\")\n",
    "else:\n",
    "    device = torch.device(\"cpu\")\n",
    "\n",
    "model.to(device)\n",
    "\n",
    "# 定义损失函数和优化器\n",
    "criterion = nn.CrossEntropyLoss()\n",
    "optimizer = optim.Adam(model.parameters(), lr=0.001)\n",
    "\n",
    "# 训练模型\n",
    "epochs = 5\n",
    "for epoch in range(epochs):\n",
    "    running_loss = 0\n",
    "    for images, labels in trainloader:\n",
    "        images, labels = images.to(device), labels.to(device)  # 将数据移动到设备上\n",
    "        optimizer.zero_grad()\n",
    "        output = model(images)\n",
    "        loss = criterion(output, labels)\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "        running_loss += loss.item()\n",
    "    print(f\"Epoch {epoch+1}/{epochs} - Loss: {running_loss/len(trainloader)}\")\n",
    "\n",
    "# 测试模型\n",
    "correct = 0\n",
    "total = 0\n",
    "with torch.no_grad():\n",
    "    for images, labels in testloader:\n",
    "        images, labels = images.to(device), labels.to(device)  # 将数据移动到设备上\n",
    "        output = model(images)\n",
    "        _, predicted = torch.max(output, 1)\n",
    "        total += labels.size(0)\n",
    "        correct += (predicted == labels).sum().item()\n",
    "\n",
    "print(f\"Accuracy: {100 * correct / total}%\")"
   ]
  }
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