模型训练

02_classification.ipynb

@@ -14,10 +14,74 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 16,
    "outputs": [],
    "source": [
-    "from model import AnonymousColorDetector"
+    "import numpy as np\n",
+    "\n",
+    "from models import AnonymousColorDetector\n",
+    "from utils import read_labeled_img"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## 读取数据与构建数据集"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "outputs": [],
+   "source": [
+    "data_dir = \"data/dataset\"\n",
+    "color_dict = {(0, 0, 255): \"yangeng\"}\n",
+    "dataset = read_labeled_img(data_dir, color_dict=color_dict, is_ps_color_space=False)"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## 模型训练"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "outputs": [],
+   "source": [
+    "# 定义一些常量\n",
+    "threshold = 5\n",
+    "node_num = 20\n",
+    "negative_sample_num = None   # None或者一个数字\n",
+    "world_boundary = np.array([0, 0, 0, 255, 255, 255])\n",
+    "# 对数据进行预处理\n",
+    "x = np.concatenate([v for k, v in dataset.items()], axis=0)\n",
+    "negative_sample_num = x.shape[0] if negative_sample_num is None else negative_sample_num"
    ],
    "metadata": {
     "collapsed": false,
@@ -28,9 +92,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 19,
    "outputs": [],
-   "source": [],
+   "source": [
+    "model = AnonymousColorDetector()"
+   ],
    "metadata": {
     "collapsed": false,
     "pycharm": {
@@ -40,21 +106,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 20,
-   "outputs": [],
+   "outputs": [
-   "source": [],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   }
-  },
     {
-   "cell_type": "code",
+     "ename": "KeyboardInterrupt",
-   "execution_count": null,
+     "evalue": "",
-   "outputs": [],
+     "output_type": "error",
-   "source": [],
+     "traceback": [
+      "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m",
+      "\u001B[1;31mKeyboardInterrupt\u001B[0m                         Traceback (most recent call last)",
+      "Input \u001B[1;32mIn [20]\u001B[0m, in \u001B[0;36m<cell line: 1>\u001B[1;34m()\u001B[0m\n\u001B[1;32m----> 1\u001B[0m \u001B[43mmodel\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mfit\u001B[49m\u001B[43m(\u001B[49m\u001B[43mx\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mworld_boundary\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mthreshold\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mnegative_sample_size\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mnegative_sample_num\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mtrain_size\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;241;43m0.7\u001B[39;49m\u001B[43m)\u001B[49m\n",
+      "File \u001B[1;32m~\\PycharmProjects\\tobacco_color\\models.py:34\u001B[0m, in \u001B[0;36mAnonymousColorDetector.fit\u001B[1;34m(self, x, world_boundary, threshold, model_selected, negative_sample_size, train_size, **kwargs)\u001B[0m\n\u001B[0;32m     32\u001B[0m     node_num \u001B[38;5;241m=\u001B[39m kwargs\u001B[38;5;241m.\u001B[39mget(\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mnode_num\u001B[39m\u001B[38;5;124m'\u001B[39m, \u001B[38;5;241m10\u001B[39m)\n\u001B[0;32m     33\u001B[0m     \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mmodel \u001B[38;5;241m=\u001B[39m ELM(input_size\u001B[38;5;241m=\u001B[39mx\u001B[38;5;241m.\u001B[39mshape[\u001B[38;5;241m1\u001B[39m], node_num\u001B[38;5;241m=\u001B[39mnode_num, output_num\u001B[38;5;241m=\u001B[39m\u001B[38;5;241m2\u001B[39m, \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mkwargs)\n\u001B[1;32m---> 34\u001B[0m negative_samples \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mgenerate_negative_samples\u001B[49m\u001B[43m(\u001B[49m\u001B[43mx\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mworld_boundary\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mthreshold\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m     35\u001B[0m \u001B[43m                                                  \u001B[49m\u001B[43msample_size\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mnegative_sample_size\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m     36\u001B[0m data_x, data_y \u001B[38;5;241m=\u001B[39m np\u001B[38;5;241m.\u001B[39mconcatenate([x, negative_samples], axis\u001B[38;5;241m=\u001B[39m\u001B[38;5;241m0\u001B[39m), \\\n\u001B[0;32m     37\u001B[0m                  np\u001B[38;5;241m.\u001B[39mconcatenate([np\u001B[38;5;241m.\u001B[39mones(x\u001B[38;5;241m.\u001B[39mshape[\u001B[38;5;241m0\u001B[39m], dtype\u001B[38;5;241m=\u001B[39m\u001B[38;5;28mint\u001B[39m),\n\u001B[0;32m     38\u001B[0m                                  np\u001B[38;5;241m.\u001B[39mzeros(negative_samples\u001B[38;5;241m.\u001B[39mshape[\u001B[38;5;241m0\u001B[39m], dtype\u001B[38;5;241m=\u001B[39m\u001B[38;5;28mint\u001B[39m)], axis\u001B[38;5;241m=\u001B[39m\u001B[38;5;241m0\u001B[39m)\n\u001B[0;32m     39\u001B[0m x_train, x_val, y_train, y_val \u001B[38;5;241m=\u001B[39m train_test_split(data_x, data_y, train_size\u001B[38;5;241m=\u001B[39mtrain_size, shuffle\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mTrue\u001B[39;00m)\n",
+      "File \u001B[1;32m~\\PycharmProjects\\tobacco_color\\models.py:66\u001B[0m, in \u001B[0;36mAnonymousColorDetector.generate_negative_samples\u001B[1;34m(x, world_boundary, threshold, sample_size)\u001B[0m\n\u001B[0;32m     64\u001B[0m \u001B[38;5;28;01mfor\u001B[39;00m sample_idx \u001B[38;5;129;01min\u001B[39;00m \u001B[38;5;28mrange\u001B[39m(generated_data\u001B[38;5;241m.\u001B[39mshape[\u001B[38;5;241m0\u001B[39m]):\n\u001B[0;32m     65\u001B[0m     sample \u001B[38;5;241m=\u001B[39m generated_data[sample_idx, :]\n\u001B[1;32m---> 66\u001B[0m     in_threshold \u001B[38;5;241m=\u001B[39m np\u001B[38;5;241m.\u001B[39many(np\u001B[38;5;241m.\u001B[39msum(\u001B[43mnp\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mpower\u001B[49m\u001B[43m(\u001B[49m\u001B[43msample\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;241;43m-\u001B[39;49m\u001B[43m \u001B[49m\u001B[43mx\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;241;43m2\u001B[39;49m\u001B[43m)\u001B[49m, axis\u001B[38;5;241m=\u001B[39m\u001B[38;5;241m1\u001B[39m) \u001B[38;5;241m<\u001B[39m threshold)\n\u001B[0;32m     67\u001B[0m     \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m in_threshold:\n\u001B[0;32m     68\u001B[0m         negative_samples[sample_idx, :] \u001B[38;5;241m=\u001B[39m sample\n",
+      "\u001B[1;31mKeyboardInterrupt\u001B[0m: "
+     ]
+    }
+   ],
+   "source": [
+    "model.fit(x, world_boundary, threshold, negative_sample_size=negative_sample_num, train_size=0.7)"
+   ],
    "metadata": {
     "collapsed": false,
     "pycharm": {

models.py

@@ -1,7 +1,7 @@
 # -*- codeing = utf-8 -*-
 # Time : 2022/7/18 14:03
 # @Auther : zhouchao
-# @File: model.py
+# @File: models.py
 # @Software:PyCharm、
 import numpy as np
 from sklearn.metrics import classification_report