cnn_mnist.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "f4adb66b-41e5-495d-a350-499ef2943e26",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mnsit at: /home/tibless/.cache/kagglehub/datasets/hojjatk/mnist-dataset/versions/1\n"
     ]
    }
   ],
   "source": [
    "import jax.numpy as jnp\n",
    "from jax import grad, vmap, jit, random, lax\n",
    "\n",
    "import kagglehub\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "# path = kagglehub.dataset_download(\"hojjatk/mnist-dataset\")  # download mnist\n",
    "path = '/home/tibless/.cache/kagglehub/datasets/hojjatk/mnist-dataset/versions/1'\n",
    "key = random.PRNGKey(239)  # 239; 412\n",
    "print(f'mnsit at: {path}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "1a01448a-876b-4ef6-8e21-e7392d93ab1b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "训练图像数据大小:  (60000, 28, 28)\n",
      "训练标签数据大小:  (60000,)\n",
      "测试图像数据大小:  (10000, 28, 28)\n",
      "测试标签数据大小:  (10000,)\n"
     ]
    }
   ],
   "source": [
    "# load mnist\n",
    "\n",
    "import os\n",
    "import struct\n",
    "import numpy as np\n",
    "\n",
    "def read_idx(filename):\n",
    "    \"\"\"\n",
    "    Read MNIST data from file using idx format.\n",
    "\n",
    "    Return\n",
    "    ------\n",
    "    np.ndarray\n",
    "    \"\"\"\n",
    "    with open(filename, 'rb') as f:\n",
    "        zero, data_type, dims = struct.unpack('>HBB', f.read(4))\n",
    "        shape = tuple(struct.unpack('>I', f.read(4))[0] for d in range(dims))\n",
    "        return np.frombuffer(f.read(), dtype=np.uint8).reshape(shape)\n",
    "\n",
    "TRAIN = 60000\n",
    "TEST  = 10000\n",
    "train_images = read_idx(os.path.join(path, 'train-images.idx3-ubyte'))[:TRAIN]\n",
    "train_labels = read_idx(os.path.join(path, 'train-labels.idx1-ubyte'))[:TRAIN]\n",
    "test_images = read_idx(os.path.join(path, 't10k-images.idx3-ubyte'))[:TEST]\n",
    "test_labels = read_idx(os.path.join(path, 't10k-labels.idx1-ubyte'))[:TEST]\n",
    "\n",
    "shuffle_kernel = np.random.permutation(np.arange(TRAIN))\n",
    "train_images = train_images[shuffle_kernel]\n",
    "train_labels = train_labels[shuffle_kernel]\n",
    "\n",
    "print(\"训练图像数据大小: \", train_images.shape)\n",
    "print(\"训练标签数据大小: \", train_labels.shape)\n",
    "print(\"测试图像数据大小: \", test_images.shape)\n",
    "print(\"测试标签数据大小: \", test_labels.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "523dc86d-6e89-4f57-8956-675bf9f52a48",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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BgCQKACRRACCJAgBJFABIogBAEgUAkigAkEQBgCQKACRRACCJAgBJFABIogBAEgUAkigAkEQBgCQKACRRACCJAgBJFABIXVv7waqq2vM5ADgEeFMAIIkCAEkUAEiiAEASBQCSKACQRAGAJAoAJFEAIP0Xk3mTKxmdx8gAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ix = 4\n",
    "plt.imshow(train_images[ix], cmap='gray')\n",
    "plt.title(f\"Label: {train_labels[ix]}\")\n",
    "plt.axis('off')  # 不显示坐标轴\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "3655f999-7413-4003-b339-182e88e96585",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = jnp.array(train_images.reshape(train_images.shape[0], 1, 28, 28)) / 255.\n",
    "y_train = jnp.array(train_labels)\n",
    "X_test = jnp.array(test_images.reshape(test_images.shape[0], 1, 28, 28)) / 255.\n",
    "y_test = jnp.array(test_labels)\n",
    "\n",
    "def one_hot(y: jnp.ndarray, num_class: int):\n",
    "    res = jnp.zeros((y.shape[0], num_class))\n",
    "    res = res.at[jnp.arange(y.shape[0]), y].set(1)\n",
    "    return res\n",
    "\n",
    "y_train_one_hot = one_hot(y_train, 10)\n",
    "y_test_one_hot = one_hot(y_test, 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "97727ee4-02b6-4880-b5f9-884e99f660e8",
   "metadata": {},
   "outputs": [],
   "source": [
    "import jax.numpy as jnp\n",
    "\n",
    "from plugins.minitorch.initer import Initer\n",
    "from plugins.minitorch.optimizer import Adam\n",
    "from plugins.minitorch.nn import Conv, Dense\n",
    "from plugins.minitorch.utils import softmax\n",
    "from plugins.minitorch.loss import CrossEntropyLoss\n",
    "\n",
    "class LeNet:\n",
    "    def __init__(self, lr=0.01, batch_size=32):\n",
    "        self.config = {\n",
    "            'conv2d:0': Conv.get_conv2d(1, 6, (5, 5), 2),\n",
    "            'max_pooling:0': Conv.get_max_pool2d((2, 2)),\n",
    "            'conv2d:1': Conv.get_conv2d(6, 16, (5, 5), 0),\n",
    "            'max_pooling:1': Conv.get_max_pool2d((2, 2)),\n",
    "            'fc:0': Dense.get_linear(400, 120),\n",
    "            'fc:1': Dense.get_linear(120, 84),\n",
    "            'fc:2': Dense.get_linear(84, 10),\n",
    "        }\n",
    "\n",
    "        initer = Initer(self.config, key)\n",
    "        self.optr = Adam(initer(), lr=lr, batch_size=batch_size)\n",
    "        self.losser = CrossEntropyLoss(self.forward)\n",
    "\n",
    "    def forward(self, x: jnp.ndarray, params, train=True):\n",
    "        res = Conv._conv2d(x, params['conv2d:0']['w'], params['conv2d:0']['b'], padding=self.config['conv2d:0']['padding'])\n",
    "        res = jnp.maximum(0, res)\n",
    "        res = Conv._max_pooling2d(res, pool_size=self.config['max_pooling:0']['pool_size'])\n",
    "        \n",
    "        res = Conv._conv2d(res, params['conv2d:1']['w'], params['conv2d:1']['b'], padding=self.config['conv2d:1']['padding'])\n",
    "        res = jnp.maximum(0, res)\n",
    "        res = Conv._max_pooling2d(res, pool_size=self.config['max_pooling:1']['pool_size'])\n",
    "\n",
    "        res = res.reshape(res.shape[0], -1)\n",
    "\n",
    "        res = res @ params['fc:0']['w'] + params['fc:0']['b']\n",
    "        res = jnp.maximum(0, res)\n",
    "        \n",
    "        res = res @ params['fc:1']['w'] + params['fc:1']['b']\n",
    "        res = jnp.maximum(0, res)\n",
    "        \n",
    "        res = res @ params['fc:2']['w'] + params['fc:2']['b']\n",
    "\n",
    "        return softmax(res)\n",
    "\n",
    "    def fit(self, x_train, y_train_proba, x_test, y_test_proba, epoches=100): \n",
    "        @jit\n",
    "        def _acc(y_true_proba, y_pred_proba):\n",
    "            y_true = jnp.argmax(y_true_proba, axis=1)\n",
    "            y_pred = jnp.argmax(y_pred_proba, axis=1)\n",
    "            return jnp.mean(y_true == y_pred)\n",
    "\n",
    "        _loss = self.losser.get_loss(train=True)\n",
    "        _loss = jit(_loss)\n",
    "        self.optr.open(_loss, x_train, y_train_proba)\n",
    "        \n",
    "        _tloss = self.losser.get_embed_loss(x_test, y_test_proba, train=False)\n",
    "        _tloss = jit(_tloss)\n",
    "        \n",
    "\n",
    "        acc, loss, tacc, tloss = [], [], [], []  # train acc, train loss, test acc, test loss\n",
    "\n",
    "        for cnt in range(epoches):\n",
    "            loss.append(_loss(self.optr.get_params(), x_train, y_train_proba))\n",
    "            tloss.append(_tloss(self.optr.get_params()))\n",
    "\n",
    "            self.optr.update()\n",
    "            \n",
    "            acc.append(_acc(y_train_proba, self.forward(x_train, self.optr.get_params())))\n",
    "            tacc.append(_acc(y_test_proba, self.forward(x_test, self.optr.get_params())))\n",
    "            if (cnt + 1) % 2 == 0:\n",
    "                print(f'>> epoch: {cnt + 1}, train acc: {acc[-1]}, test acc: {tacc[-1]}')\n",
    "\n",
    "        return acc, loss, tacc, tloss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "fcf14990-0c34-4c07-8f34-80db6e1b6e44",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[*] oprimizer opened with 117 batches with batch size 512.\n",
      ">> epoch: 10, train acc: 0.9876333475112915, test acc: 0.98089998960495\n"
     ]
    }
   ],
   "source": [
    "epochs = 10\n",
    "batch_size = 512\n",
    "learning_rate = 0.01\n",
    "\n",
    "model = LeNet(lr=learning_rate, batch_size=batch_size)\n",
    "acc, loss, tacc, tloss = model.fit(\n",
    "    x_train=X_train, \n",
    "    y_train_proba=y_train_one_hot,\n",
    "    x_test=X_test,\n",
    "    y_test_proba=y_test_one_hot,\n",
    "    epoches=epochs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "36e31011-8930-403f-b12f-0909493fa5df",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "final train, test acc : 0.9876333475112915, 0.98089998960495\n",
      "final train, test loss: 0.040280524641275406, 0.06864224374294281\n"
     ]
    }
   ],
   "source": [
    "fig, ax1 = plt.subplots()\n",
    "\n",
    "plt.rcParams['font.family'] = 'Noto Serif SC'\n",
    "plt.rcParams['font.sans-serif'] = ['Noto Serif SC']\n",
    "\n",
    "color = 'tab:red'\n",
    "ax1.set_xlabel('Epochs')\n",
    "ax1.set_ylabel('Accuracy', color=color)\n",
    "ax1.plot(range(epochs), acc, color=color, label='Train Accuracy', linestyle='-')\n",
    "ax1.plot(range(epochs), tacc, color=color, label='Test Accuracy', linestyle='--')\n",
    "ax1.tick_params(axis='y', labelcolor=color)\n",
    "\n",
    "ax2 = ax1.twinx()  \n",
    "\n",
    "color = 'tab:blue'\n",
    "ax2.set_ylabel('Loss', color=color)  \n",
    "ax2.plot(range(epochs), loss, color=color, label='Train Loss', linestyle='-')\n",
    "ax2.plot(range(epochs), tloss, color=color, label='Test Loss', linestyle='--')\n",
    "ax2.tick_params(axis='y', labelcolor=color)\n",
    "\n",
    "handles1, labels1 = ax1.get_legend_handles_labels()\n",
    "handles2, labels2 = ax2.get_legend_handles_labels()\n",
    "ax1.legend(handles1 + handles2, labels1 + labels2, loc='lower right')\n",
    "\n",
    "plt.title('Training and Testing Accuracy and Loss over Epochs')\n",
    "fig.tight_layout()\n",
    "# plt.savefig('./assets/LeNet.svg', format='svg')\n",
    "plt.show()\n",
    "\n",
    "print(f'final train, test acc : {acc[-1]}, {tacc[-1]}')\n",
    "print(f'final train, test loss: {loss[-1]}, {tloss[-1]}')"
   ]
  }
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