AI-learning/lab/5_MLP-tf.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 导库\n",
    "from sklearn.datasets import make_classification\n",
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.lines import Line2D\n",
    "from matplotlib.colors import ListedColormap\n",
    "from tensorflow.keras.layers import Dense\n",
    "\n",
    "# 使用GPU 6\n",
    "gpus = tf.config.experimental.list_physical_devices('GPU')\n",
    "if gpus:\n",
    "    try:\n",
    "        tf.config.experimental.set_visible_devices(gpus[6], 'GPU')\n",
    "        for gpu in gpus:\n",
    "            tf.config.experimental.set_virtual_device_configuration(\n",
    "                gpu,\n",
    "                [tf.config.experimental.VirtualDeviceConfiguration(memory_limit=4096)]  # 限制每个GPU使用4GB显存\n",
    "            )\n",
    "    except RuntimeError as e:\n",
    "        print(e)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 2560x1920 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 生成烤咖啡豆的数据集\n",
    "# 温度和时间作为特征，结果为好坏\n",
    "# X[:, 0] 表示温度，X[:, 1] 表示时间，y 表示结果（好坏）\n",
    "def generate_data():\n",
    "    # 生成符合条件的数据集，温度在150到200度之间，时间在10到20分钟之间\n",
    "    np.random.seed(38)\n",
    "    X = np.zeros((60, 2),dtype=int)\n",
    "    y = np.zeros(60, dtype=int)\n",
    "    X[:, 0] = np.random.randint(150, 250, size=60)\n",
    "    X[:, 1] = np.random.randint(10, 20, size=60)\n",
    "    \n",
    "    # 将中间的点标记为好，周围的点标记为坏\n",
    "    # 调整条件以确保至少5个点被标记为好\n",
    "    for i in range(60):\n",
    "        if 180 < X[i, 0] < 220 and 13 < X[i, 1] < 17:\n",
    "            y[i] = 1  # 好的部分在190到210度之间，时间在13到16分钟之间\n",
    "    \n",
    "    plt.figure(dpi=400)\n",
    "    cmap = ListedColormap(['red', 'blue'])\n",
    "    plt.scatter(X[:, 0], X[:, 1], c=y, cmap=cmap)\n",
    "    plt.title('Coast Coffee Beans')\n",
    "    plt.xlabel('Temperature')\n",
    "    plt.ylabel('Time')\n",
    "    # 添加图例，表示颜色所代表的含义\n",
    "    legend_elements = [\n",
    "        Line2D([0], [0], marker='o', color='w', label='Good',\n",
    "               markerfacecolor='blue', markersize=10),\n",
    "        Line2D([0], [0], marker='o', color='w', label='Bad',\n",
    "               markerfacecolor='red', markersize=10)\n",
    "    ]\n",
    "    plt.legend(handles=legend_elements, title=\"Quality\", loc='upper left')\n",
    "    plt.show()\n",
    "    return X, y\n",
    "\n",
    "x, y = generate_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "'+ptx85' is not a recognized feature for this target (ignoring feature)\n",
      "'+ptx85' is not a recognized feature for this target (ignoring feature)\n",
      "'+ptx85' is not a recognized feature for this target (ignoring feature)\n",
      "'+ptx85' is not a recognized feature for this target (ignoring feature)\n",
      "'+ptx85' is not a recognized feature for this target (ignoring feature)\n",
      "'+ptx85' is not a recognized feature for this target (ignoring feature)\n",
      "'+ptx85' is not a recognized feature for this target (ignoring feature)\n",
      "'+ptx85' is not a recognized feature for this target (ignoring feature)\n",
      "'+ptx85' is not a recognized feature for this target (ignoring feature)\n",
      "'+ptx85' is not a recognized feature for this target (ignoring feature)\n",
      "'+ptx85' is not a recognized feature for this target (ignoring feature)\n",
      "'+ptx85' is not a recognized feature for this target (ignoring feature)\n",
      "'+ptx85' is not a recognized feature for this target (ignoring feature)\n",
      "'+ptx85' is not a recognized feature for this target (ignoring feature)\n",
      "'+ptx85' is not a recognized feature for this target (ignoring feature)\n",
      "'+ptx85' is not a recognized feature for this target (ignoring feature)\n",
      "'+ptx85' is not a recognized feature for this target (ignoring feature)\n",
      "'+ptx85' is not a recognized feature for this target (ignoring feature)\n",
      "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n",
      "I0000 00:00:1736497931.531534   21216 service.cc:146] XLA service 0x7f49f4954650 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n",
      "I0000 00:00:1736497931.531557   21216 service.cc:154]   StreamExecutor device (0): NVIDIA A800 80GB PCIe, Compute Capability 8.0\n",
      "2025-01-10 08:32:11.540274: I tensorflow/compiler/mlir/tensorflow/utils/dump_mlir_util.cc:268] disabling MLIR crash reproducer, set env var `MLIR_CRASH_REPRODUCER_DIRECTORY` to enable.\n",
      "2025-01-10 08:32:11.571961: I external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:531] Loaded cuDNN version 90600\n",
      "'+ptx85' is not a recognized feature for this target (ignoring feature)\n",
      "'+ptx85' is not a recognized feature for this target (ignoring feature)\n",
      "'+ptx85' is not a recognized feature for this target (ignoring feature)\n",
      "I0000 00:00:1736497931.646086   21216 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.\n",
      "'+ptx85' is not a recognized feature for this target (ignoring feature)\n",
      "'+ptx85' is not a recognized feature for this target (ignoring feature)\n",
      "'+ptx85' is not a recognized feature for this target (ignoring feature)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2/2 [==============================] - 2s 10ms/step - loss: 0.7364\n",
      "Epoch 2/100\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 0.7210\n",
      "Epoch 3/100\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 0.7066\n",
      "Epoch 4/100\n",
      "2/2 [==============================] - 0s 7ms/step - loss: 0.6929\n",
      "Epoch 5/100\n",
      "2/2 [==============================] - 0s 7ms/step - loss: 0.6786\n",
      "Epoch 6/100\n",
      "2/2 [==============================] - 0s 7ms/step - loss: 0.6668\n",
      "Epoch 7/100\n",
      "2/2 [==============================] - 0s 7ms/step - loss: 0.6531\n",
      "Epoch 8/100\n",
      "2/2 [==============================] - 0s 7ms/step - loss: 0.6411\n",
      "Epoch 9/100\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "'+ptx85' is not a recognized feature for this target (ignoring feature)\n",
      "'+ptx85' is not a recognized feature for this target (ignoring feature)\n",
      "'+ptx85' is not a recognized feature for this target (ignoring feature)\n",
      "'+ptx85' is not a recognized feature for this target (ignoring feature)\n",
      "'+ptx85' is not a recognized feature for this target (ignoring feature)\n",
      "'+ptx85' is not a recognized feature for this target (ignoring feature)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2/2 [==============================] - 0s 7ms/step - loss: 0.6245\n",
      "Epoch 10/100\n",
      "2/2 [==============================] - 0s 7ms/step - loss: 0.6015\n",
      "Epoch 11/100\n",
      "2/2 [==============================] - 0s 7ms/step - loss: 0.5786\n",
      "Epoch 12/100\n",
      "2/2 [==============================] - 0s 7ms/step - loss: 0.5240\n",
      "Epoch 13/100\n",
      "2/2 [==============================] - 0s 7ms/step - loss: 0.5058\n",
      "Epoch 14/100\n",
      "2/2 [==============================] - 0s 7ms/step - loss: 0.4971\n",
      "Epoch 15/100\n",
      "2/2 [==============================] - 0s 7ms/step - loss: 0.4868\n",
      "Epoch 16/100\n",
      "2/2 [==============================] - 0s 7ms/step - loss: 0.4798\n",
      "Epoch 17/100\n",
      "2/2 [==============================] - 0s 7ms/step - loss: 0.4722\n",
      "Epoch 18/100\n",
      "2/2 [==============================] - 0s 7ms/step - loss: 0.4654\n",
      "Epoch 19/100\n",
      "2/2 [==============================] - 0s 7ms/step - loss: 0.4600\n",
      "Epoch 20/100\n",
      "2/2 [==============================] - 0s 7ms/step - loss: 0.4560\n",
      "Epoch 21/100\n",
      "2/2 [==============================] - 0s 7ms/step - loss: 0.4506\n",
      "Epoch 22/100\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 0.4460\n",
      "Epoch 23/100\n",
      "2/2 [==============================] - 0s 7ms/step - loss: 0.4432\n",
      "Epoch 24/100\n",
      "2/2 [==============================] - 0s 7ms/step - loss: 0.4404\n",
      "Epoch 25/100\n",
      "2/2 [==============================] - 0s 7ms/step - loss: 0.4380\n",
      "Epoch 26/100\n",
      "2/2 [==============================] - 0s 7ms/step - loss: 0.4349\n",
      "Epoch 27/100\n",
      "2/2 [==============================] - 0s 7ms/step - loss: 0.4335\n",
      "Epoch 28/100\n",
      "2/2 [==============================] - 0s 7ms/step - loss: 0.4313\n",
      "Epoch 29/100\n",
      "2/2 [==============================] - 0s 7ms/step - loss: 0.4301\n",
      "Epoch 30/100\n",
      "2/2 [==============================] - 0s 7ms/step - loss: 0.4293\n",
      "Epoch 31/100\n",
      "2/2 [==============================] - 0s 7ms/step - loss: 0.4278\n",
      "Epoch 32/100\n",
      "2/2 [==============================] - 0s 7ms/step - loss: 0.4270\n",
      "Epoch 33/100\n",
      "2/2 [==============================] - 0s 7ms/step - loss: 0.4259\n",
      "Epoch 34/100\n",
      "2/2 [==============================] - 0s 7ms/step - loss: 0.4257\n",
      "Epoch 35/100\n",
      "2/2 [==============================] - 0s 7ms/step - loss: 0.4249\n",
      "Epoch 36/100\n",
      "2/2 [==============================] - 0s 7ms/step - loss: 0.4245\n",
      "Epoch 37/100\n",
      "2/2 [==============================] - 0s 7ms/step - loss: 0.4247\n",
      "Epoch 38/100\n",
      "2/2 [==============================] - 0s 7ms/step - loss: 0.4239\n",
      "Epoch 39/100\n",
      "2/2 [==============================] - 0s 7ms/step - loss: 0.4238\n",
      "Epoch 40/100\n",
      "2/2 [==============================] - 0s 7ms/step - loss: 0.4238\n",
      "Epoch 41/100\n",
      "2/2 [==============================] - 0s 7ms/step - loss: 0.4232\n",
      "Epoch 42/100\n",
      "2/2 [==============================] - 0s 7ms/step - loss: 0.4232\n",
      "Epoch 43/100\n",
      "2/2 [==============================] - 0s 7ms/step - loss: 0.4232\n",
      "Epoch 44/100\n",
      "2/2 [==============================] - 0s 8ms/step - loss: 0.4229\n",
      "Epoch 45/100\n",
      "2/2 [==============================] - 0s 7ms/step - loss: 0.4229\n",
      "Epoch 46/100\n",
      "2/2 [==============================] - 0s 7ms/step - loss: 0.4230\n",
      "Epoch 47/100\n",
      "2/2 [==============================] - 0s 7ms/step - loss: 0.4229\n",
      "Epoch 48/100\n",
      "2/2 [==============================] - 0s 7ms/step - loss: 0.4228\n",
      "Epoch 49/100\n",
      "2/2 [==============================] - 0s 7ms/step - loss: 0.4228\n",
      "Epoch 50/100\n",
      "2/2 [==============================] - 0s 7ms/step - loss: 0.4228\n",
      "Epoch 51/100\n",
      "2/2 [==============================] - 0s 7ms/step - loss: 0.4228\n",
      "Epoch 52/100\n",
      "2/2 [==============================] - 0s 7ms/step - loss: 0.4228\n",
      "Epoch 53/100\n",
      "2/2 [==============================] - 0s 7ms/step - loss: 0.4227\n",
      "Epoch 54/100\n",
      "2/2 [==============================] - 0s 7ms/step - loss: 0.4227\n",
      "Epoch 55/100\n",
      "2/2 [==============================] - 0s 7ms/step - loss: 0.4227\n",
      "Epoch 56/100\n",
      "2/2 [==============================] - 0s 7ms/step - loss: 0.4227\n",
      "Epoch 57/100\n",
      "2/2 [==============================] - 0s 7ms/step - loss: 0.4228\n",
      "Epoch 58/100\n",
      "2/2 [==============================] - 0s 7ms/step - loss: 0.4227\n",
      "Epoch 59/100\n",
      "2/2 [==============================] - 0s 7ms/step - loss: 0.4228\n",
      "Epoch 60/100\n",
      "2/2 [==============================] - 0s 7ms/step - loss: 0.4228\n",
      "Epoch 61/100\n",
      "2/2 [==============================] - 0s 7ms/step - loss: 0.4227\n",
      "Epoch 62/100\n",
      "2/2 [==============================] - 0s 7ms/step - loss: 0.4227\n",
      "Epoch 63/100\n",
      "2/2 [==============================] - 0s 7ms/step - loss: 0.4227\n",
      "Epoch 64/100\n",
      "2/2 [==============================] - 0s 7ms/step - loss: 0.4227\n",
      "Epoch 65/100\n",
      "2/2 [==============================] - 0s 7ms/step - loss: 0.4227\n",
      "Epoch 66/100\n",
      "2/2 [==============================] - 0s 7ms/step - loss: 0.4228\n",
      "Epoch 67/100\n",
      "2/2 [==============================] - 0s 7ms/step - loss: 0.4227\n",
      "Epoch 68/100\n",
      "2/2 [==============================] - 0s 7ms/step - loss: 0.4227\n",
      "Epoch 69/100\n",
      "2/2 [==============================] - 0s 7ms/step - loss: 0.4227\n",
      "Epoch 70/100\n",
      "2/2 [==============================] - 0s 7ms/step - loss: 0.4227\n",
      "Epoch 71/100\n",
      "2/2 [==============================] - 0s 7ms/step - loss: 0.4227\n",
      "Epoch 72/100\n",
      "2/2 [==============================] - 0s 7ms/step - loss: 0.4228\n",
      "Epoch 73/100\n",
      "2/2 [==============================] - 0s 7ms/step - loss: 0.4227\n",
      "Epoch 74/100\n",
      "2/2 [==============================] - 0s 7ms/step - loss: 0.4227\n",
      "Epoch 75/100\n",
      "2/2 [==============================] - 0s 7ms/step - loss: 0.4228\n",
      "Epoch 76/100\n",
      "2/2 [==============================] - 0s 7ms/step - loss: 0.4227\n",
      "Epoch 77/100\n",
      "2/2 [==============================] - 0s 7ms/step - loss: 0.4227\n",
      "Epoch 78/100\n",
      "2/2 [==============================] - 0s 7ms/step - loss: 0.4227\n",
      "Epoch 79/100\n",
      "2/2 [==============================] - 0s 7ms/step - loss: 0.4227\n",
      "Epoch 80/100\n",
      "2/2 [==============================] - 0s 7ms/step - loss: 0.4227\n",
      "Epoch 81/100\n",
      "2/2 [==============================] - 0s 7ms/step - loss: 0.4228\n",
      "Epoch 82/100\n",
      "2/2 [==============================] - 0s 7ms/step - loss: 0.4227\n",
      "Epoch 83/100\n",
      "2/2 [==============================] - 0s 7ms/step - loss: 0.4227\n",
      "Epoch 84/100\n",
      "2/2 [==============================] - 0s 7ms/step - loss: 0.4228\n",
      "Epoch 85/100\n",
      "2/2 [==============================] - 0s 7ms/step - loss: 0.4227\n",
      "Epoch 86/100\n",
      "2/2 [==============================] - 0s 7ms/step - loss: 0.4227\n",
      "Epoch 87/100\n",
      "2/2 [==============================] - 0s 7ms/step - loss: 0.4227\n",
      "Epoch 88/100\n",
      "2/2 [==============================] - 0s 7ms/step - loss: 0.4228\n",
      "Epoch 89/100\n",
      "2/2 [==============================] - 0s 7ms/step - loss: 0.4227\n",
      "Epoch 90/100\n",
      "2/2 [==============================] - 0s 7ms/step - loss: 0.4227\n",
      "Epoch 91/100\n",
      "2/2 [==============================] - 0s 7ms/step - loss: 0.4227\n",
      "Epoch 92/100\n",
      "2/2 [==============================] - 0s 7ms/step - loss: 0.4227\n",
      "Epoch 93/100\n",
      "2/2 [==============================] - 0s 7ms/step - loss: 0.4227\n",
      "Epoch 94/100\n",
      "2/2 [==============================] - 0s 7ms/step - loss: 0.4228\n",
      "Epoch 95/100\n",
      "2/2 [==============================] - 0s 7ms/step - loss: 0.4227\n",
      "Epoch 96/100\n",
      "2/2 [==============================] - 0s 7ms/step - loss: 0.4227\n",
      "Epoch 97/100\n",
      "2/2 [==============================] - 0s 7ms/step - loss: 0.4228\n",
      "Epoch 98/100\n",
      "2/2 [==============================] - 0s 7ms/step - loss: 0.4227\n",
      "Epoch 99/100\n",
      "2/2 [==============================] - 0s 7ms/step - loss: 0.4227\n",
      "Epoch 100/100\n",
      "2/2 [==============================] - 0s 7ms/step - loss: 0.4227\n",
      "2/2 [==============================] - 0s 2ms/step\n",
      "模型的精准度: 0.85\n"
     ]
    }
   ],
   "source": [
    "\n",
    "\n",
    "model = tf.keras.Sequential()\n",
    "layer_1 = Dense(units=3,activation='sigmoid')\n",
    "layer_2 = Dense(units=1,activation='sigmoid')\n",
    "model.add(layer_1)\n",
    "model.add(layer_2)\n",
    "model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=0.01),loss='binary_crossentropy')\n",
    "model.fit(x,y,epochs=100)\n",
    "a = model.predict(x)\n",
    "\n",
    "# 计算并输出模型的精准度\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "# 将预测结果转换为二进制标签\n",
    "predicted_labels = (a > 0.5).astype(int)\n",
    "\n",
    "# 计算精准度\n",
    "accuracy = accuracy_score(y, predicted_labels)\n",
    "print(f\"模型的精准度: {accuracy:.2f}\")\n",
    "\n",
    "\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}