├── AutoEncoder ├── .ipynb_checkpoints │ └── AutoEncoder-checkpoint.ipynb └── AutoEncoder.ipynb ├── CNN ├── .ipynb_checkpoints │ ├── CNN_text-checkpoint.ipynb │ └── Tradition_cnn_image-checkpoint.ipynb ├── CNN_text.ipynb └── Tradition_cnn_image.ipynb ├── EDEN ├── .ipynb_checkpoints │ └── EDEN-DEBUG-checkpoint.ipynb └── EDEN-DEBUG.ipynb ├── GAN ├── .ipynb_checkpoints │ └── GAN-checkpoint.ipynb └── GAN.ipynb ├── LSTM └── LSTM_poem_robot.ipynb ├── MLP ├── .ipynb_checkpoints │ ├── MLP-checkpoint.ipynb │ └── MLP_Text-checkpoint.ipynb ├── MLP.ipynb └── MLP_Text.ipynb ├── Neat ├── .ipynb_checkpoints │ └── xor-checkpoint.ipynb ├── Digraph.gv ├── Digraph.gv.svg ├── __pycache__ │ ├── visualize.cpython-36.pyc │ └── visualize.cpython-37.pyc ├── avg_fitness.svg ├── config-feedforward ├── neat-checkpoint-104 ├── neat-checkpoint-109 ├── neat-checkpoint-114 ├── neat-checkpoint-119 ├── neat-checkpoint-124 ├── neat-checkpoint-14 ├── neat-checkpoint-19 ├── neat-checkpoint-24 ├── neat-checkpoint-29 ├── neat-checkpoint-34 ├── neat-checkpoint-39 ├── neat-checkpoint-4 ├── neat-checkpoint-44 ├── neat-checkpoint-49 ├── neat-checkpoint-54 ├── neat-checkpoint-59 ├── neat-checkpoint-64 ├── neat-checkpoint-69 ├── neat-checkpoint-74 ├── neat-checkpoint-79 ├── neat-checkpoint-84 ├── neat-checkpoint-89 ├── neat-checkpoint-9 ├── neat-checkpoint-94 ├── neat-checkpoint-99 ├── speciation.svg ├── visualize.py ├── xor.ipynb └── xor.py ├── Python_Basis ├── .ipynb_checkpoints │ ├── Basis-checkpoint.ipynb │ ├── Basis_Advance-checkpoint.ipynb │ └── Basis_high_ranking-checkpoint.ipynb ├── Basis.ipynb ├── Basis_Advance.ipynb ├── Basis_high_ranking.ipynb ├── __pycache__ │ └── test.cpython-37.pyc ├── test.py ├── text.txt └── tx.txt └── Transfer_learning ├── .ipynb_checkpoints └── Transfer_learning_cnn_image-checkpoint.ipynb ├── Transfer_learning_cnn_image.ipynb └── Trasnfer_learning_cnn_regression.ipynb /EDEN/.ipynb_checkpoints/EDEN-DEBUG-checkpoint.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 1, 6 | "metadata": {}, 7 | "outputs": [ 8 | { 9 | "name": "stderr", 10 | "output_type": "stream", 11 | "text": [ 12 | "C:\\Users\\SZU\\Anaconda3\\lib\\site-packages\\h5py\\__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n", 13 | " from ._conv import register_converters as _register_converters\n", 14 | "Using TensorFlow backend.\n" 15 | ] 16 | }, 17 | { 18 | "name": "stdout", 19 | "output_type": "stream", 20 | "text": [ 21 | "Load data.....\n" 22 | ] 23 | } 24 | ], 25 | "source": [ 26 | "import os\n", 27 | "import sys\n", 28 | "import numpy as np\n", 29 | "from sklearn.model_selection import train_test_split\n", 30 | "from keras.preprocessing import image\n", 31 | "print('Load data.....')\n", 32 | "parapath = './dataset/Parasitized/'\n", 33 | "uninpath = './dataset/Uninfected/'\n", 34 | "parastized = os.listdir(parapath)\n", 35 | "uninfected = os.listdir(uninpath)\n", 36 | "data = []\n", 37 | "label = []\n", 38 | "for para in parastized:\n", 39 | " try:\n", 40 | " img = image.load_img(parapath+para,target_size=(128,128))\n", 41 | " x = image.img_to_array(img)\n", 42 | " data.append(x)\n", 43 | " label.append(1)\n", 44 | " except:\n", 45 | " print(\"Can't add \"+para+\" in the dataset\")\n", 46 | "for unin in uninfected:\n", 47 | " try:\n", 48 | " img = image.load_img(uninpath+unin,target_size=(128,128))\n", 49 | " data.append(x)\n", 50 | " label.append(0)\n", 51 | " except:\n", 52 | " print(\"Can't add \"+unin+\" in the dataset\") \n", 53 | "data = np.array(data)\n", 54 | "label = np.array(label)\n", 55 | "data = data/255\n", 56 | "x_train, x_test, y_train, y_test = train_test_split(data,label,test_size = 0.2,random_state=0)" 57 | ] 58 | }, 59 | { 60 | "cell_type": "code", 61 | "execution_count": 14, 62 | "metadata": {}, 63 | "outputs": [], 64 | "source": [ 65 | "#!/usr/bin/env python\n", 66 | "# -*- coding: utf-8 -*-\n", 67 | "\"\"\"\n", 68 | "@Author:ChileWang\n", 69 | "@Created On 2019-06-21\n", 70 | "@Coding Environment: Anaconda Python 3.7\n", 71 | "\"\"\"\n", 72 | "import random\n", 73 | "import matplotlib.pyplot as plt\n", 74 | "import pandas as pd\n", 75 | "import keras\n", 76 | "from keras.preprocessing.image import ImageDataGenerator\n", 77 | "from keras.models import Sequential, Model\n", 78 | "from keras.layers import Dense, Dropout, Activation, Flatten\n", 79 | "from keras.layers import Conv2D, MaxPooling2D\n", 80 | "import os\n", 81 | "from keras.layers import LeakyReLU\n", 82 | "from keras.layers import PReLU\n", 83 | "import operator\n", 84 | "from keras.models import load_model\n", 85 | "import copy\n", 86 | "from keras.utils import multi_gpu_model\n", 87 | "from keras.layers.normalization import BatchNormalization\n", 88 | "# os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"-1\" # 占用1， 2，3块GPU\n", 89 | "# Save model and weights\n", 90 | "save_dir = os.path.join(os.getcwd(), 'saved_models_cnn')\n", 91 | "if not os.path.isdir(save_dir):\n", 92 | " os.makedirs(save_dir)\n", 93 | "\n", 94 | "class GeneticAlgorithm:\n", 95 | " # -----------初始数据定义---------------------\n", 96 | " # 定义一些遗传算法需要的参数\n", 97 | " # JCL = 0.9 # 遗传时的交叉率\n", 98 | " # BYL = 0.09 # 遗传时的变异率\n", 99 | "\n", 100 | " def __init__(self, rows, times, num_classes, kill_num):\n", 101 | " self.rows = rows # 染色体个数\n", 102 | " self.times = times # 迭代次数\n", 103 | " self.accuracy = 0 # 模型准确率\n", 104 | " self.layer_list = ['Conv2D', 'Dense'] # 算法使用的网络层\n", 105 | " self.cnn_activation_function = ['linear', 'leaky relu', 'prelu', 'relu'] # CNN层用到激励函数\n", 106 | " self.dense_activation_function = ['linear', 'sigmoid', 'softmax', 'relu'] # 中间全连接层用到的激励函数\n", 107 | " self.last_dense_activation_function = ['sigmoid', 'softmax'] # 最后一个全连接层用到的激励函数\n", 108 | " self.unit_num_list = [64, 128, 256] # 神经元数目选择\n", 109 | " self.filter_num_list = [4, 8, 16] # 卷积核数目选择\n", 110 | " self.pooling_size = range(2, 4) # max_pooling的选择范围\n", 111 | " self.filter_size = range(2, 4) # 卷积核的选择范围\n", 112 | " self.layer_num_list = range(2, 4) # 网络层次的选择范围 \n", 113 | " self.max_size = 10 # 层数最大值\n", 114 | " self.threshold = 3 # 层数临界值\n", 115 | " self.batch_size = 512\n", 116 | " self.num_classes = num_classes # 2分类\n", 117 | " self.kill_num = kill_num # 每次杀掉的网络个数\n", 118 | "\n", 119 | " # -------------遗传函数开始执行---------------------\n", 120 | " def run(self):\n", 121 | "\n", 122 | " print(\"开始迭代\")\n", 123 | " # 初始化种群\n", 124 | " lines = [self.create_network(self.create_chromosome()) for i in range(self.rows)]\n", 125 | "\n", 126 | " # 初始化种群适应度\n", 127 | " fit = [0 for i in range(self.rows)]\n", 128 | "\n", 129 | " epochs = 1\n", 130 | " # 计算每个染色体(网络)的适应度\n", 131 | " for i in range(0, self.rows):\n", 132 | " lines[i]['is_saved'] = False\n", 133 | " lines[i]['model_name'] = 'model_%s' % str(i)\n", 134 | " lines[i] = self.cal_fitness(lines[i], epochs)\n", 135 | " fit[i] = lines[i]['fitness']\n", 136 | "\n", 137 | " # 开始迭代\n", 138 | " t = 0\n", 139 | " while t < self.times:\n", 140 | " print('迭代次数:', t)\n", 141 | " random_fit = [0 for i in range(self.rows)]\n", 142 | " total_fit = 0\n", 143 | " tmp_fit = 0\n", 144 | "\n", 145 | " # 开始遗传\n", 146 | " # 根据轮盘赌选择复代\n", 147 | " # 计算原有种群的总适应度\n", 148 | " for i in range(self.rows):\n", 149 | " total_fit += fit[i]\n", 150 | " # 通过适应度占总适应度的比例生成随机适应度\n", 151 | " for i in range(self.rows):\n", 152 | " random_fit[i] = tmp_fit + fit[i] / total_fit\n", 153 | " tmp_fit += random_fit[i]\n", 154 | " r = int(self.random_select(random_fit))\n", 155 | " line = lines[r]\n", 156 | "\n", 157 | " # 不需要交叉的，直接变异，然后遗传到下一代\n", 158 | " # 基因变异, 生成两个子代\n", 159 | " print('*******变异******')\n", 160 | " offspring1 = self.mutation(line, 'offspring1')\n", 161 | " offspring2 = self.mutation(offspring1, 'offspring2')\n", 162 | " best_chromosome = self.get_best_chromosome(line, offspring1, offspring2, epochs)\n", 163 | " # 替换原先的父代\n", 164 | " father_model_name = lines[r]['model_name']\n", 165 | " lines[r] = best_chromosome\n", 166 | " print('保存最佳变异个体。。。。')\n", 167 | " # 保存模型\n", 168 | " model_path = os.path.join(save_dir, father_model_name)\n", 169 | " lines[r]['model_path'] = model_path # 每一个模型的路径\n", 170 | " lines[r]['is_saved'] = True # 是否保存\n", 171 | " best_chromosome_model = lines[r]['model'] \n", 172 | " best_chromosome_model.save(model_path)\n", 173 | " \n", 174 | " epochs += 1\n", 175 | " # 杀掉最差的self.kill_num个网络\n", 176 | " kill_index = 1\n", 177 | " sorted_lines = sorted(lines, key=operator.itemgetter('fitness')) # 按适应度从小到大排序\n", 178 | " if len(sorted_lines) > self.kill_num:\n", 179 | " # 第一次迭代杀死适应度小于0.55的网络\n", 180 | " for i in range(len(sorted_lines)):\n", 181 | " if sorted_lines[i]['fitness'] < 0.55:\n", 182 | " kill_index = i\n", 183 | " else:\n", 184 | " break\n", 185 | " if t == 0:\n", 186 | " new_lines = sorted_lines[kill_index:]\n", 187 | " self.rows -= kill_index\n", 188 | " else:\n", 189 | " new_lines = sorted_lines[self.kill_num:]\n", 190 | " self.rows -= self.kill_num\n", 191 | " lines = new_lines # 更新种群\n", 192 | " next_fit = [line['fitness'] for line in lines] # 更新种群\n", 193 | " fit = next_fit \n", 194 | " # self.rows -= self.kill_num\n", 195 | " print('..........Population size:%d .........' % self.rows)\n", 196 | " \n", 197 | " # 进行新的一次epochs，计算种群的适应度\n", 198 | " # 计算每个染色体(网络)的适应度\n", 199 | " for i in range(0, self.rows):\n", 200 | " lines[i] = self.cal_fitness(lines[i], 1)\n", 201 | " fit[i] = lines[i]['fitness']\n", 202 | " print('***************************************************')\n", 203 | " print()\n", 204 | " t += 1 # 代数+1\n", 205 | "\n", 206 | " # 提取适应度最高的\n", 207 | " m = fit[0]\n", 208 | " ml = 0\n", 209 | " for i in range(self.rows):\n", 210 | " if m < fit[i]:\n", 211 | " m = fit[i]\n", 212 | " ml = i\n", 213 | "\n", 214 | " print(\"迭代完成\")\n", 215 | " # 输出结果:\n", 216 | " excellent_chromosome = self.cal_fitness(lines[ml], 0)\n", 217 | " print('The best network:')\n", 218 | " print(excellent_chromosome['model'].summary())\n", 219 | " print('Fitness: ', excellent_chromosome['fitness'])\n", 220 | " print('Accuracy', excellent_chromosome['accuracy'])\n", 221 | " \n", 222 | " best_model_save_dir = os.path.join(os.getcwd(), 'best_model')\n", 223 | " if not os.path.isdir(best_model_save_dir):\n", 224 | " os.makedirs(best_model_save_dir)\n", 225 | " best_model_path = os.path.join(best_model_save_dir, 'excellent_model')\n", 226 | " excellent_chromosome['model'].save(best_model_path)\n", 227 | " print(excellent_chromosome['layer_list'])\n", 228 | " with open('best_network_layer_list.txt', 'w') as fw:\n", 229 | " for layer in excellent_chromosome['layer_list']:\n", 230 | " fw.write(str(layer) + '\\n')\n", 231 | " \n", 232 | "\n", 233 | " # -----------------遗传函数执行完成--------------------\n", 234 | "\n", 235 | " # -----------------各种辅助计算函数--------------------\n", 236 | " def create_network(self, chromsome_dict):\n", 237 | " \"\"\"\n", 238 | " :param chromosome:\n", 239 | " :return:\n", 240 | " \"\"\"\n", 241 | " layer_list = chromsome_dict['layer_list']\n", 242 | " layer_num = chromsome_dict['layer_num'] + 2 # 包括输入输出\n", 243 | " unit_num_sum = 0 # 统计Dense神经元的个数\n", 244 | " model = Sequential()\n", 245 | " for i in range(len(layer_list) - 1):\n", 246 | " if i == 0:\n", 247 | " model.add(Conv2D(layer_list[i]['conv_kernel_num'],\n", 248 | " layer_list[i]['conv_kernel_size'],\n", 249 | " padding=layer_list[i]['padding'],\n", 250 | " input_shape=layer_list[i]['input_shape'],\n", 251 | " kernel_initializer='he_normal'\n", 252 | " )\n", 253 | " )\n", 254 | " model = self.add_activation_funtion(model, layer_list[i]['layer_activation_function'])\n", 255 | "\n", 256 | " else:\n", 257 | " if layer_list[i]['layer_name'] == 'Conv2D':\n", 258 | " model.add(Conv2D(layer_list[i]['conv_kernel_num'],\n", 259 | " layer_list[i]['conv_kernel_size'],\n", 260 | " padding=layer_list[i]['padding'],\n", 261 | " )\n", 262 | " )\n", 263 | " model = self.add_activation_funtion(model, layer_list[i]['layer_activation_function'])\n", 264 | "\n", 265 | " if layer_list[i]['pooling_choice']: # 是否创建Pooling层次\n", 266 | " try:\n", 267 | " model.add(MaxPooling2D(pool_size=layer_list[i]['pool_size'], dim_ordering=\"tf\"))\n", 268 | " except Exception as error:\n", 269 | " print('MaxPooling大于输入的矩阵, 用pool_size=(1, 1)代替')\n", 270 | " model.add(MaxPooling2D(pool_size=(1, 1), strides = (2, 2)))\n", 271 | " layer_num += 1\n", 272 | " model.add(BatchNormalization())\n", 273 | "\n", 274 | " # Dropout层\n", 275 | " model.add(Dropout(layer_list[i]['dropout_rate']))\n", 276 | "\n", 277 | " else: # Dense层\n", 278 | " unit_num_sum += layer_list[i]['unit_num']\n", 279 | " model.add(Dense(layer_list[i]['unit_num'],\n", 280 | " )\n", 281 | " )\n", 282 | " model = self.add_activation_funtion(model, layer_list[i]['layer_activation_function'])\n", 283 | " # Dropout层\n", 284 | " model.add(Dropout(layer_list[i]['dropout_rate']))\n", 285 | "\n", 286 | " # 最后一层\n", 287 | " model.add(Flatten())\n", 288 | "\n", 289 | " if layer_list[-1]['layer_activation_function'] == 'sigmoid':\n", 290 | " model.add(Dense(1))\n", 291 | " unit_num_sum += 1\n", 292 | " else:\n", 293 | " model.add(Dense(self.num_classes))\n", 294 | " unit_num_sum += self.num_classes\n", 295 | " model = self.add_activation_funtion(model, layer_list[-1]['layer_activation_function'])\n", 296 | " chromsome_dict['model'] = model\n", 297 | " chromsome_dict['punish_factor'] = (1 / layer_num) + (1 / unit_num_sum) # 惩罚因子\n", 298 | " return chromsome_dict\n", 299 | "\n", 300 | " def add_activation_funtion(self, model, activation_name):\n", 301 | " \"\"\"\n", 302 | " 添加激活函数\n", 303 | " :param model:\n", 304 | " :param activation_name:\n", 305 | " :return:\n", 306 | " \"\"\"\n", 307 | " if activation_name == 'leaky relu':\n", 308 | " model.add(LeakyReLU())\n", 309 | " elif activation_name == 'prelu':\n", 310 | " model.add(PReLU())\n", 311 | " else:\n", 312 | " model.add(Activation(activation_name))\n", 313 | " return model\n", 314 | "\n", 315 | " def create_chromosome(self):\n", 316 | " \"\"\"\n", 317 | " 创建染色体\n", 318 | " \"\"\"\n", 319 | " chromsome_dict = dict() # 用字典装载染色体的所有属性\n", 320 | " chromsome_dict['learning_rate'] = self.random_learning_rate() # 学习率\n", 321 | " layer_num = random.choice(self.layer_num_list) # 创建的网络层次， 输入层和输出层不计算在内\n", 322 | " chromsome_dict['layer_num'] = layer_num\n", 323 | "\n", 324 | " layer_list = list() # 网络层次顺序表\n", 325 | " # 第一层必须是卷积层\n", 326 | " layer_list.append({'layer_name': 'Conv2D',\n", 327 | " 'conv_kernel_num': 32,\n", 328 | " 'conv_kernel_size': (3, 3),\n", 329 | " 'padding': 'same',\n", 330 | " 'input_shape': (128, 128, 3),\n", 331 | " 'layer_activation_function': random.choice(self.cnn_activation_function)}\n", 332 | " )\n", 333 | " # 每一层的属性\n", 334 | " for i in range(layer_num):\n", 335 | " # 选择层次类型\n", 336 | "# layer_name = self.layer_list[random.randint(0, 1)]\n", 337 | " layer_name = 'Conv2D'\n", 338 | " if i == 0: # 第一层dropout_rate 必须为0,即不存在\n", 339 | " layer_dict = self.create_layer(layer_name)\n", 340 | " layer_dict['dropout_rate'] = 0\n", 341 | "\n", 342 | " else:\n", 343 | " layer_dict = self.create_layer(layer_name)\n", 344 | " layer_list.append(layer_dict) # 添加至层次列表\n", 345 | "\n", 346 | " # 最后一层必须是Dense层\n", 347 | " layer_list.append({'layer_name': 'Dense',\n", 348 | " 'layer_activation_function': random.choice(self.last_dense_activation_function)\n", 349 | " }\n", 350 | " )\n", 351 | "\n", 352 | " # 将网络层次顺序表添加至染色体\n", 353 | " chromsome_dict['layer_list'] = layer_list\n", 354 | "\n", 355 | " return chromsome_dict\n", 356 | "\n", 357 | " def create_layer(self, layer_name):\n", 358 | " \"\"\"\n", 359 | " 创建网络层次属性\n", 360 | " \"\"\"\n", 361 | " layer_dict = dict()\n", 362 | " layer_dict['layer_name'] = layer_name\n", 363 | " if layer_name == 'Conv2D':\n", 364 | " # 激励函数\n", 365 | " layer_activation_function = random.choice(self.cnn_activation_function)\n", 366 | " # 卷积核数量和大小\n", 367 | " conv_kernel_num = random.choice(self.filter_num_list)\n", 368 | " random_size = random.choice(self.filter_size)\n", 369 | " conv_kernel_size = (random_size, random_size)\n", 370 | " # 是否加入Pooling层\n", 371 | " pooling_choice = [True, False]\n", 372 | " if pooling_choice[random.randint(0, 1)]:\n", 373 | " layer_dict['pooling_choice'] = True\n", 374 | " random_size = random.choice(self.pooling_size)\n", 375 | " pool_size = (random_size, random_size)\n", 376 | " layer_dict['pool_size'] = pool_size\n", 377 | " else:\n", 378 | " layer_dict['pooling_choice'] = False\n", 379 | "\n", 380 | " layer_dict['layer_activation_function'] = layer_activation_function\n", 381 | " layer_dict['conv_kernel_num'] = conv_kernel_num\n", 382 | " layer_dict['conv_kernel_size'] = conv_kernel_size\n", 383 | " layer_dict['padding'] = 'same'\n", 384 | "\n", 385 | " else: # Dense层\n", 386 | " # 激励函数\n", 387 | " layer_activation_function = random.choice(self.dense_activation_function)\n", 388 | " # 神经元个数\n", 389 | " unit_num = random.choice(self.unit_num_list)\n", 390 | " layer_dict['layer_activation_function'] = layer_activation_function\n", 391 | " layer_dict['unit_num'] = unit_num\n", 392 | " layer_dict['dropout_rate'] = round(random.uniform(0, 1), 3)\n", 393 | " return layer_dict\n", 394 | "\n", 395 | " def cal_fitness(self, line, epochs):\n", 396 | " \"\"\"\n", 397 | " :param line: 染色体（网络）\n", 398 | " :param epochs: 迭代次数\n", 399 | " :return:\n", 400 | " \"\"\"\n", 401 | " if epochs == 0:\n", 402 | " return line\n", 403 | " line = self.train_process(line, epochs)\n", 404 | " # 适应度函数，表示准确率 + 训练参数个数的倒数， 适应度越大，说明模型越好。\n", 405 | " # fitness = line['accuracy'] + line['punish_factor']\n", 406 | " fitness = line['accuracy'] \n", 407 | " line['fitness'] = fitness\n", 408 | " return line\n", 409 | "\n", 410 | " def train_process(self, line, epochs):\n", 411 | " \"\"\"\n", 412 | " 训练\n", 413 | " :param line: 染色体\n", 414 | " :param epochs: 迭代次数\n", 415 | " :return:\n", 416 | " \"\"\"\n", 417 | " \n", 418 | " print('learning_rate:', line['learning_rate'])\n", 419 | " print('layer_num:', len(line['layer_list']))\n", 420 | " \n", 421 | " if line['is_saved']: # 若保存，则直接读入训练即可\n", 422 | " print('读取原有模型训练..............')\n", 423 | " model_path = line['model_path']\n", 424 | " model = load_model(model_path)\n", 425 | " accuracy = model.evaluate(x = x_test,y = y_test)[1]\n", 426 | " print('former accuracy:', accuracy)\n", 427 | " else:\n", 428 | " print('重新训练.......')\n", 429 | " model = line['model']\n", 430 | " learning_rate = line['learning_rate']\n", 431 | " # 初始化adam优化器\n", 432 | " opt = keras.optimizers.Adam(lr=learning_rate, beta_1=0.9, beta_2=0.999,\n", 433 | " epsilon=None, decay=1e-6, amsgrad=False)\n", 434 | " # 编译模型\n", 435 | " layer_list = line['layer_list']\n", 436 | " if layer_list[-1]['layer_activation_function'] == 'softmax':\n", 437 | " loss = 'sparse_categorical_crossentropy'\n", 438 | " else:\n", 439 | " loss = 'binary_crossentropy'\n", 440 | " print(loss)\n", 441 | " model.compile(loss=loss,\n", 442 | " optimizer=opt,\n", 443 | " metrics=['accuracy'])\n", 444 | "\n", 445 | " history = model.fit(x_train, y_train,epochs=epochs, batch_size=self.batch_size)\n", 446 | " # Score trained model.\n", 447 | " accuracy = model.evaluate(x = x_test,y = y_test)[1]\n", 448 | " line['accuracy'] = accuracy\n", 449 | " line['history'] = history # 训练历史\n", 450 | " print('accuracy:', accuracy)\n", 451 | " # 保存模型\n", 452 | " model_name = line['model_name']\n", 453 | " model_path = os.path.join(save_dir, model_name)\n", 454 | " line['model_path'] = model_path # 每一个模型的路径\n", 455 | " line['is_saved'] = True # 是否保存\n", 456 | " line['model'] = model\n", 457 | " model.save(model_path)\n", 458 | " \n", 459 | " return line\n", 460 | "\n", 461 | " def mutation(self, line, name):\n", 462 | " \"\"\"\n", 463 | " 基因变异\n", 464 | " :param line:\n", 465 | " :return:\n", 466 | " \"\"\"\n", 467 | " offspring1 = copy.deepcopy(line) # 深拷贝！！！！子代1\n", 468 | " offspring1['model_name'] = name\n", 469 | " offspring1['is_saved'] = False\n", 470 | " mutation_choice = [True, False]\n", 471 | " # 子代1变异\n", 472 | " if mutation_choice[random.randint(0, 1)]: # 改变学习率\n", 473 | " print('Mutation Operation: Change learning rate....')\n", 474 | " offspring1['learning_rate'] = self.random_learning_rate() # 学习率\n", 475 | " else: # 改变网络结构\n", 476 | " offspring1 = self.layer_mutation_operation(offspring1)\n", 477 | " offspring1 = self.create_network(offspring1)\n", 478 | " return offspring1\n", 479 | "\n", 480 | " def layer_mutation_operation(self, offspring):\n", 481 | " \"\"\"\n", 482 | " :param offspring: 子代染色体\n", 483 | " :return:\n", 484 | " \"\"\"\n", 485 | " mutation_layer_choice = [0, 1, 2] # 添加，替换，删除\n", 486 | " mutation_layer_choice_name = ['Add', 'Replace', 'Delete']\n", 487 | " layer_name = self.layer_list[random.randint(0, 1)]\n", 488 | " layer_dict = self.create_layer(layer_name)\n", 489 | " choice_index = -1\n", 490 | " if self.threshold < offspring['layer_num'] < self.max_size: # 层数小于最大值且大于临界值，则可以添加和替换和删除\n", 491 | " choice_index = random.randint(0, 2)\n", 492 | " if mutation_layer_choice[choice_index] == 0: # 添加\n", 493 | " insert_index = random.randint(1, len(offspring['layer_list']) - 1) # 插入位置\n", 494 | " offspring['layer_list'].insert(insert_index, layer_dict)\n", 495 | " offspring['layer_num'] += 1\n", 496 | "\n", 497 | " elif mutation_layer_choice[choice_index] == 1: # 替换\n", 498 | " replace_index = random.randint(1, len(offspring['layer_list']) - 1) # 替换位置\n", 499 | " offspring['layer_list'][replace_index] = layer_dict\n", 500 | "\n", 501 | " else: # 删除层\n", 502 | " delete_index = random.randint(1, len(offspring['layer_list']) - 1) # 删除位置\n", 503 | " del offspring['layer_list'][delete_index]\n", 504 | " offspring['layer_num'] -= 1\n", 505 | "\n", 506 | " elif offspring['layer_num'] <= self.threshold: # 小于等于临界值，只能添加或者替换\n", 507 | " choice_index = random.randint(0, 1)\n", 508 | " if mutation_layer_choice[choice_index] == 0: # 添加\n", 509 | " insert_index = random.randint(1, len(offspring['layer_list']) - 1) # 插入位置\n", 510 | " offspring['layer_list'].insert(insert_index, layer_dict)\n", 511 | " offspring['layer_num'] += 1\n", 512 | " else:\n", 513 | " replace_index = random.randint(1, len(offspring['layer_list']) - 1) # 替换位置\n", 514 | " offspring['layer_list'][replace_index] = layer_dict\n", 515 | "\n", 516 | " else: # 层数到达最大值，则只能替换和删除\n", 517 | " choice_index = random.randint(1, 2)\n", 518 | " if mutation_layer_choice[choice_index] == 1: # 替换层\n", 519 | " replace_index = random.randint(1, len(offspring['layer_list']) - 1) # 替换位置\n", 520 | " offspring['layer_list'][replace_index] = layer_dict\n", 521 | "\n", 522 | " else: # 删除层\n", 523 | " delete_index = random.randint(1, len(offspring['layer_list']) - 1) # 删除位置\n", 524 | " del offspring['layer_list'][delete_index]\n", 525 | " offspring['layer_num'] -= 1\n", 526 | " print('Mutation Operation:', mutation_layer_choice_name[choice_index])\n", 527 | " return offspring\n", 528 | "\n", 529 | " def get_best_chromosome(self, father, offspring1, offspring2, epochs):\n", 530 | " \"\"\"\n", 531 | " 比较父代，子代1，子代2的适应度，返回适应度最大的染色体\n", 532 | " :param father:\n", 533 | " :param offspring1:\n", 534 | " :param offspring2:\n", 535 | " :param epochs:\n", 536 | " :return: 返回适应度最高的染色体\n", 537 | " \"\"\"\n", 538 | " print('子代1训练：', epochs)\n", 539 | " offspring1 = self.cal_fitness(offspring1, epochs)\n", 540 | " \n", 541 | " print('子代2训练：', epochs)\n", 542 | " offspring2 = self.cal_fitness(offspring2, epochs)\n", 543 | " \n", 544 | " tmp_lines = [father, offspring1, offspring2]\n", 545 | " sorted_lines = sorted(tmp_lines, key=operator.itemgetter('fitness')) # 按适应度从小到大排序\n", 546 | " return sorted_lines[-1]\n", 547 | "\n", 548 | " def random_learning_rate(self):\n", 549 | " return random.uniform(0.01, 0.02) # 学习率\n", 550 | "\n", 551 | " def random_select(self, ran_fit):\n", 552 | " \"\"\"\n", 553 | " 轮盘赌选择\n", 554 | " 根据概率随机选择的染色体\n", 555 | " :param ran_fit:\n", 556 | " :return:\n", 557 | " \"\"\"\n", 558 | " ran = random.random()\n", 559 | " for i in range(self.rows):\n", 560 | " if ran < ran_fit[i]:\n", 561 | " return i\n", 562 | " \n", 563 | " \n" 564 | ] 565 | }, 566 | { 567 | "cell_type": "code", 568 | "execution_count": null, 569 | "metadata": { 570 | "scrolled": false 571 | }, 572 | "outputs": [ 573 | { 574 | "name": "stdout", 575 | "output_type": "stream", 576 | "text": [ 577 | "开始迭代\n" 578 | ] 579 | }, 580 | { 581 | "name": "stderr", 582 | "output_type": "stream", 583 | "text": [ 584 | "C:\\Users\\SZU\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:203: UserWarning: Update your `MaxPooling2D` call to the Keras 2 API: `MaxPooling2D(pool_size=(2, 2), data_format=\"channels_last\")`\n", 585 | "C:\\Users\\SZU\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:203: UserWarning: Update your `MaxPooling2D` call to the Keras 2 API: `MaxPooling2D(pool_size=(3, 3), data_format=\"channels_last\")`\n" 586 | ] 587 | }, 588 | { 589 | "name": "stdout", 590 | "output_type": "stream", 591 | "text": [ 592 | "learning_rate: 0.012029467024930782\n", 593 | "layer_num: 4\n", 594 | "重新训练.......\n", 595 | "sparse_categorical_crossentropy\n", 596 | "Epoch 1/1\n", 597 | "22046/22046 [==============================] - 28s 1ms/step - loss: 7.5838 - acc: 0.5212\n", 598 | "5512/5512 [==============================] - 6s 1ms/step\n", 599 | "accuracy: 0.49038461538461536\n", 600 | "learning_rate: 0.015437717269751258\n", 601 | "layer_num: 4\n", 602 | "重新训练.......\n", 603 | "sparse_categorical_crossentropy\n", 604 | "Epoch 1/1\n", 605 | "22046/22046 [==============================] - 22s 1ms/step - loss: 7.7997 - acc: 0.5026\n", 606 | "5512/5512 [==============================] - 6s 1ms/step\n", 607 | "accuracy: 0.49038461538461536\n", 608 | "learning_rate: 0.01007904573168161\n", 609 | "layer_num: 5\n", 610 | "重新训练.......\n", 611 | "binary_crossentropy\n", 612 | "Epoch 1/1\n" 613 | ] 614 | } 615 | ], 616 | "source": [ 617 | "# -------------入口函数，开始执行-----------------------------\n", 618 | "\"\"\"\n", 619 | "输入参数的的意义依次为\n", 620 | " self.rows = rows # 染色体个数(即种群大小：101个网络)\n", 621 | " self.times = times # 迭代次数\n", 622 | " self.num_classes = num_classes # 几分类\n", 623 | " self.kill_num = kill_num # 每次迭代杀死的网络\n", 624 | "\"\"\"\n", 625 | "if __name__ == '__main__':\n", 626 | " ga = GeneticAlgorithm(rows=50, times=3, num_classes=2, kill_num=7)\n", 627 | " ga.run()\n" 628 | ] 629 | } 630 | ], 631 | "metadata": { 632 | "kernelspec": { 633 | "display_name": "Python 3", 634 | "language": "python", 635 | "name": "python3" 636 | }, 637 | "language_info": { 638 | "codemirror_mode": { 639 | "name": "ipython", 640 | "version": 3 641 | }, 642 | "file_extension": ".py", 643 | "mimetype": "text/x-python", 644 | "name": "python", 645 | "nbconvert_exporter": "python", 646 | "pygments_lexer": "ipython3", 647 | "version": "3.7.3" 648 | } 649 | }, 650 | "nbformat": 4, 651 | "nbformat_minor": 2 652 | } 653 | -------------------------------------------------------------------------------- /EDEN/EDEN-DEBUG.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 1, 6 | "metadata": {}, 7 | "outputs": [ 8 | { 9 | "name": "stderr", 10 | "output_type": "stream", 11 | "text": [ 12 | "C:\\Users\\SZU\\Anaconda3\\lib\\site-packages\\h5py\\__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n", 13 | " from ._conv import register_converters as _register_converters\n", 14 | "Using TensorFlow backend.\n" 15 | ] 16 | }, 17 | { 18 | "name": "stdout", 19 | "output_type": "stream", 20 | "text": [ 21 | "Load data.....\n" 22 | ] 23 | } 24 | ], 25 | "source": [ 26 | "import os\n", 27 | "import sys\n", 28 | "import numpy as np\n", 29 | "from sklearn.model_selection import train_test_split\n", 30 | "from keras.preprocessing import image\n", 31 | "print('Load data.....')\n", 32 | "parapath = './dataset/Parasitized/'\n", 33 | "uninpath = './dataset/Uninfected/'\n", 34 | "parastized = os.listdir(parapath)\n", 35 | "uninfected = os.listdir(uninpath)\n", 36 | "data = []\n", 37 | "label = []\n", 38 | "for para in parastized:\n", 39 | " try:\n", 40 | " img = image.load_img(parapath+para,target_size=(128,128))\n", 41 | " x = image.img_to_array(img)\n", 42 | " data.append(x)\n", 43 | " label.append(1)\n", 44 | " except:\n", 45 | " print(\"Can't add \"+para+\" in the dataset\")\n", 46 | "for unin in uninfected:\n", 47 | " try:\n", 48 | " img = image.load_img(uninpath+unin,target_size=(128,128))\n", 49 | " data.append(x)\n", 50 | " label.append(0)\n", 51 | " except:\n", 52 | " print(\"Can't add \"+unin+\" in the dataset\") \n", 53 | "data = np.array(data)\n", 54 | "label = np.array(label)\n", 55 | "data = data/255\n", 56 | "x_train, x_test, y_train, y_test = train_test_split(data,label,test_size = 0.2,random_state=0)" 57 | ] 58 | }, 59 | { 60 | "cell_type": "code", 61 | "execution_count": 14, 62 | "metadata": {}, 63 | "outputs": [], 64 | "source": [ 65 | "#!/usr/bin/env python\n", 66 | "# -*- coding: utf-8 -*-\n", 67 | "\"\"\"\n", 68 | "@Author:ChileWang\n", 69 | "@Created On 2019-06-21\n", 70 | "@Coding Environment: Anaconda Python 3.7\n", 71 | "\"\"\"\n", 72 | "import random\n", 73 | "import matplotlib.pyplot as plt\n", 74 | "import pandas as pd\n", 75 | "import keras\n", 76 | "from keras.preprocessing.image import ImageDataGenerator\n", 77 | "from keras.models import Sequential, Model\n", 78 | "from keras.layers import Dense, Dropout, Activation, Flatten\n", 79 | "from keras.layers import Conv2D, MaxPooling2D\n", 80 | "import os\n", 81 | "from keras.layers import LeakyReLU\n", 82 | "from keras.layers import PReLU\n", 83 | "import operator\n", 84 | "from keras.models import load_model\n", 85 | "import copy\n", 86 | "from keras.utils import multi_gpu_model\n", 87 | "from keras.layers.normalization import BatchNormalization\n", 88 | "# os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"-1\" # 占用1， 2，3块GPU\n", 89 | "# Save model and weights\n", 90 | "save_dir = os.path.join(os.getcwd(), 'saved_models_cnn')\n", 91 | "if not os.path.isdir(save_dir):\n", 92 | " os.makedirs(save_dir)\n", 93 | "\n", 94 | "class GeneticAlgorithm:\n", 95 | " # -----------初始数据定义---------------------\n", 96 | " # 定义一些遗传算法需要的参数\n", 97 | " # JCL = 0.9 # 遗传时的交叉率\n", 98 | " # BYL = 0.09 # 遗传时的变异率\n", 99 | "\n", 100 | " def __init__(self, rows, times, num_classes, kill_num):\n", 101 | " self.rows = rows # 染色体个数\n", 102 | " self.times = times # 迭代次数\n", 103 | " self.accuracy = 0 # 模型准确率\n", 104 | " self.layer_list = ['Conv2D', 'Dense'] # 算法使用的网络层\n", 105 | " self.cnn_activation_function = ['linear', 'leaky relu', 'prelu', 'relu'] # CNN层用到激励函数\n", 106 | " self.dense_activation_function = ['linear', 'sigmoid', 'softmax', 'relu'] # 中间全连接层用到的激励函数\n", 107 | " self.last_dense_activation_function = ['sigmoid', 'softmax'] # 最后一个全连接层用到的激励函数\n", 108 | " self.unit_num_list = [64, 128, 256] # 神经元数目选择\n", 109 | " self.filter_num_list = [4, 8, 16] # 卷积核数目选择\n", 110 | " self.pooling_size = range(2, 4) # max_pooling的选择范围\n", 111 | " self.filter_size = range(2, 4) # 卷积核的选择范围\n", 112 | " self.layer_num_list = range(2, 4) # 网络层次的选择范围 \n", 113 | " self.max_size = 10 # 层数最大值\n", 114 | " self.threshold = 3 # 层数临界值\n", 115 | " self.batch_size = 512\n", 116 | " self.num_classes = num_classes # 2分类\n", 117 | " self.kill_num = kill_num # 每次杀掉的网络个数\n", 118 | "\n", 119 | " # -------------遗传函数开始执行---------------------\n", 120 | " def run(self):\n", 121 | "\n", 122 | " print(\"开始迭代\")\n", 123 | " # 初始化种群\n", 124 | " lines = [self.create_network(self.create_chromosome()) for i in range(self.rows)]\n", 125 | "\n", 126 | " # 初始化种群适应度\n", 127 | " fit = [0 for i in range(self.rows)]\n", 128 | "\n", 129 | " epochs = 1\n", 130 | " # 计算每个染色体(网络)的适应度\n", 131 | " for i in range(0, self.rows):\n", 132 | " lines[i]['is_saved'] = False\n", 133 | " lines[i]['model_name'] = 'model_%s' % str(i)\n", 134 | " lines[i] = self.cal_fitness(lines[i], epochs)\n", 135 | " fit[i] = lines[i]['fitness']\n", 136 | "\n", 137 | " # 开始迭代\n", 138 | " t = 0\n", 139 | " while t < self.times:\n", 140 | " print('迭代次数:', t)\n", 141 | " random_fit = [0 for i in range(self.rows)]\n", 142 | " total_fit = 0\n", 143 | " tmp_fit = 0\n", 144 | "\n", 145 | " # 开始遗传\n", 146 | " # 根据轮盘赌选择复代\n", 147 | " # 计算原有种群的总适应度\n", 148 | " for i in range(self.rows):\n", 149 | " total_fit += fit[i]\n", 150 | " # 通过适应度占总适应度的比例生成随机适应度\n", 151 | " for i in range(self.rows):\n", 152 | " random_fit[i] = tmp_fit + fit[i] / total_fit\n", 153 | " tmp_fit += random_fit[i]\n", 154 | " r = int(self.random_select(random_fit))\n", 155 | " line = lines[r]\n", 156 | "\n", 157 | " # 不需要交叉的，直接变异，然后遗传到下一代\n", 158 | " # 基因变异, 生成两个子代\n", 159 | " print('*******变异******')\n", 160 | " offspring1 = self.mutation(line, 'offspring1')\n", 161 | " offspring2 = self.mutation(offspring1, 'offspring2')\n", 162 | " best_chromosome = self.get_best_chromosome(line, offspring1, offspring2, epochs)\n", 163 | " # 替换原先的父代\n", 164 | " father_model_name = lines[r]['model_name']\n", 165 | " lines[r] = best_chromosome\n", 166 | " print('保存最佳变异个体。。。。')\n", 167 | " # 保存模型\n", 168 | " model_path = os.path.join(save_dir, father_model_name)\n", 169 | " lines[r]['model_path'] = model_path # 每一个模型的路径\n", 170 | " lines[r]['is_saved'] = True # 是否保存\n", 171 | " best_chromosome_model = lines[r]['model'] \n", 172 | " best_chromosome_model.save(model_path)\n", 173 | " \n", 174 | " epochs += 1\n", 175 | " # 杀掉最差的self.kill_num个网络\n", 176 | " kill_index = 1\n", 177 | " sorted_lines = sorted(lines, key=operator.itemgetter('fitness')) # 按适应度从小到大排序\n", 178 | " if len(sorted_lines) > self.kill_num:\n", 179 | " # 第一次迭代杀死适应度小于0.55的网络\n", 180 | " for i in range(len(sorted_lines)):\n", 181 | " if sorted_lines[i]['fitness'] < 0.55:\n", 182 | " kill_index = i\n", 183 | " else:\n", 184 | " break\n", 185 | " if t == 0:\n", 186 | " new_lines = sorted_lines[kill_index:]\n", 187 | " self.rows -= kill_index\n", 188 | " else:\n", 189 | " new_lines = sorted_lines[self.kill_num:]\n", 190 | " self.rows -= self.kill_num\n", 191 | " lines = new_lines # 更新种群\n", 192 | " next_fit = [line['fitness'] for line in lines] # 更新种群\n", 193 | " fit = next_fit \n", 194 | " # self.rows -= self.kill_num\n", 195 | " print('..........Population size:%d .........' % self.rows)\n", 196 | " \n", 197 | " # 进行新的一次epochs，计算种群的适应度\n", 198 | " # 计算每个染色体(网络)的适应度\n", 199 | " for i in range(0, self.rows):\n", 200 | " lines[i] = self.cal_fitness(lines[i], 1)\n", 201 | " fit[i] = lines[i]['fitness']\n", 202 | " print('***************************************************')\n", 203 | " print()\n", 204 | " t += 1 # 代数+1\n", 205 | "\n", 206 | " # 提取适应度最高的\n", 207 | " m = fit[0]\n", 208 | " ml = 0\n", 209 | " for i in range(self.rows):\n", 210 | " if m < fit[i]:\n", 211 | " m = fit[i]\n", 212 | " ml = i\n", 213 | "\n", 214 | " print(\"迭代完成\")\n", 215 | " # 输出结果:\n", 216 | " excellent_chromosome = self.cal_fitness(lines[ml], 0)\n", 217 | " print('The best network:')\n", 218 | " print(excellent_chromosome['model'].summary())\n", 219 | " print('Fitness: ', excellent_chromosome['fitness'])\n", 220 | " print('Accuracy', excellent_chromosome['accuracy'])\n", 221 | " \n", 222 | " best_model_save_dir = os.path.join(os.getcwd(), 'best_model')\n", 223 | " if not os.path.isdir(best_model_save_dir):\n", 224 | " os.makedirs(best_model_save_dir)\n", 225 | " best_model_path = os.path.join(best_model_save_dir, 'excellent_model')\n", 226 | " excellent_chromosome['model'].save(best_model_path)\n", 227 | " print(excellent_chromosome['layer_list'])\n", 228 | " with open('best_network_layer_list.txt', 'w') as fw:\n", 229 | " for layer in excellent_chromosome['layer_list']:\n", 230 | " fw.write(str(layer) + '\\n')\n", 231 | " \n", 232 | "\n", 233 | " # -----------------遗传函数执行完成--------------------\n", 234 | "\n", 235 | " # -----------------各种辅助计算函数--------------------\n", 236 | " def create_network(self, chromsome_dict):\n", 237 | " \"\"\"\n", 238 | " :param chromosome:\n", 239 | " :return:\n", 240 | " \"\"\"\n", 241 | " layer_list = chromsome_dict['layer_list']\n", 242 | " layer_num = chromsome_dict['layer_num'] + 2 # 包括输入输出\n", 243 | " unit_num_sum = 0 # 统计Dense神经元的个数\n", 244 | " model = Sequential()\n", 245 | " for i in range(len(layer_list) - 1):\n", 246 | " if i == 0:\n", 247 | " model.add(Conv2D(layer_list[i]['conv_kernel_num'],\n", 248 | " layer_list[i]['conv_kernel_size'],\n", 249 | " padding=layer_list[i]['padding'],\n", 250 | " input_shape=layer_list[i]['input_shape'],\n", 251 | " kernel_initializer='he_normal'\n", 252 | " )\n", 253 | " )\n", 254 | " model = self.add_activation_funtion(model, layer_list[i]['layer_activation_function'])\n", 255 | "\n", 256 | " else:\n", 257 | " if layer_list[i]['layer_name'] == 'Conv2D':\n", 258 | " model.add(Conv2D(layer_list[i]['conv_kernel_num'],\n", 259 | " layer_list[i]['conv_kernel_size'],\n", 260 | " padding=layer_list[i]['padding'],\n", 261 | " )\n", 262 | " )\n", 263 | " model = self.add_activation_funtion(model, layer_list[i]['layer_activation_function'])\n", 264 | "\n", 265 | " if layer_list[i]['pooling_choice']: # 是否创建Pooling层次\n", 266 | " try:\n", 267 | " model.add(MaxPooling2D(pool_size=layer_list[i]['pool_size'], dim_ordering=\"tf\"))\n", 268 | " except Exception as error:\n", 269 | " print('MaxPooling大于输入的矩阵, 用pool_size=(1, 1)代替')\n", 270 | " model.add(MaxPooling2D(pool_size=(1, 1), strides = (2, 2)))\n", 271 | " layer_num += 1\n", 272 | " model.add(BatchNormalization())\n", 273 | "\n", 274 | " # Dropout层\n", 275 | " model.add(Dropout(layer_list[i]['dropout_rate']))\n", 276 | "\n", 277 | " else: # Dense层\n", 278 | " unit_num_sum += layer_list[i]['unit_num']\n", 279 | " model.add(Dense(layer_list[i]['unit_num'],\n", 280 | " )\n", 281 | " )\n", 282 | " model = self.add_activation_funtion(model, layer_list[i]['layer_activation_function'])\n", 283 | " # Dropout层\n", 284 | " model.add(Dropout(layer_list[i]['dropout_rate']))\n", 285 | "\n", 286 | " # 最后一层\n", 287 | " model.add(Flatten())\n", 288 | "\n", 289 | " if layer_list[-1]['layer_activation_function'] == 'sigmoid':\n", 290 | " model.add(Dense(1))\n", 291 | " unit_num_sum += 1\n", 292 | " else:\n", 293 | " model.add(Dense(self.num_classes))\n", 294 | " unit_num_sum += self.num_classes\n", 295 | " model = self.add_activation_funtion(model, layer_list[-1]['layer_activation_function'])\n", 296 | " chromsome_dict['model'] = model\n", 297 | " chromsome_dict['punish_factor'] = (1 / layer_num) + (1 / unit_num_sum) # 惩罚因子\n", 298 | " return chromsome_dict\n", 299 | "\n", 300 | " def add_activation_funtion(self, model, activation_name):\n", 301 | " \"\"\"\n", 302 | " 添加激活函数\n", 303 | " :param model:\n", 304 | " :param activation_name:\n", 305 | " :return:\n", 306 | " \"\"\"\n", 307 | " if activation_name == 'leaky relu':\n", 308 | " model.add(LeakyReLU())\n", 309 | " elif activation_name == 'prelu':\n", 310 | " model.add(PReLU())\n", 311 | " else:\n", 312 | " model.add(Activation(activation_name))\n", 313 | " return model\n", 314 | "\n", 315 | " def create_chromosome(self):\n", 316 | " \"\"\"\n", 317 | " 创建染色体\n", 318 | " \"\"\"\n", 319 | " chromsome_dict = dict() # 用字典装载染色体的所有属性\n", 320 | " chromsome_dict['learning_rate'] = self.random_learning_rate() # 学习率\n", 321 | " layer_num = random.choice(self.layer_num_list) # 创建的网络层次， 输入层和输出层不计算在内\n", 322 | " chromsome_dict['layer_num'] = layer_num\n", 323 | "\n", 324 | " layer_list = list() # 网络层次顺序表\n", 325 | " # 第一层必须是卷积层\n", 326 | " layer_list.append({'layer_name': 'Conv2D',\n", 327 | " 'conv_kernel_num': 32,\n", 328 | " 'conv_kernel_size': (3, 3),\n", 329 | " 'padding': 'same',\n", 330 | " 'input_shape': (128, 128, 3),\n", 331 | " 'layer_activation_function': random.choice(self.cnn_activation_function)}\n", 332 | " )\n", 333 | " # 每一层的属性\n", 334 | " for i in range(layer_num):\n", 335 | " # 选择层次类型\n", 336 | "# layer_name = self.layer_list[random.randint(0, 1)]\n", 337 | " layer_name = 'Conv2D'\n", 338 | " if i == 0: # 第一层dropout_rate 必须为0,即不存在\n", 339 | " layer_dict = self.create_layer(layer_name)\n", 340 | " layer_dict['dropout_rate'] = 0\n", 341 | "\n", 342 | " else:\n", 343 | " layer_dict = self.create_layer(layer_name)\n", 344 | " layer_list.append(layer_dict) # 添加至层次列表\n", 345 | "\n", 346 | " # 最后一层必须是Dense层\n", 347 | " layer_list.append({'layer_name': 'Dense',\n", 348 | " 'layer_activation_function': random.choice(self.last_dense_activation_function)\n", 349 | " }\n", 350 | " )\n", 351 | "\n", 352 | " # 将网络层次顺序表添加至染色体\n", 353 | " chromsome_dict['layer_list'] = layer_list\n", 354 | "\n", 355 | " return chromsome_dict\n", 356 | "\n", 357 | " def create_layer(self, layer_name):\n", 358 | " \"\"\"\n", 359 | " 创建网络层次属性\n", 360 | " \"\"\"\n", 361 | " layer_dict = dict()\n", 362 | " layer_dict['layer_name'] = layer_name\n", 363 | " if layer_name == 'Conv2D':\n", 364 | " # 激励函数\n", 365 | " layer_activation_function = random.choice(self.cnn_activation_function)\n", 366 | " # 卷积核数量和大小\n", 367 | " conv_kernel_num = random.choice(self.filter_num_list)\n", 368 | " random_size = random.choice(self.filter_size)\n", 369 | " conv_kernel_size = (random_size, random_size)\n", 370 | " # 是否加入Pooling层\n", 371 | " pooling_choice = [True, False]\n", 372 | " if pooling_choice[random.randint(0, 1)]:\n", 373 | " layer_dict['pooling_choice'] = True\n", 374 | " random_size = random.choice(self.pooling_size)\n", 375 | " pool_size = (random_size, random_size)\n", 376 | " layer_dict['pool_size'] = pool_size\n", 377 | " else:\n", 378 | " layer_dict['pooling_choice'] = False\n", 379 | "\n", 380 | " layer_dict['layer_activation_function'] = layer_activation_function\n", 381 | " layer_dict['conv_kernel_num'] = conv_kernel_num\n", 382 | " layer_dict['conv_kernel_size'] = conv_kernel_size\n", 383 | " layer_dict['padding'] = 'same'\n", 384 | "\n", 385 | " else: # Dense层\n", 386 | " # 激励函数\n", 387 | " layer_activation_function = random.choice(self.dense_activation_function)\n", 388 | " # 神经元个数\n", 389 | " unit_num = random.choice(self.unit_num_list)\n", 390 | " layer_dict['layer_activation_function'] = layer_activation_function\n", 391 | " layer_dict['unit_num'] = unit_num\n", 392 | " layer_dict['dropout_rate'] = round(random.uniform(0, 1), 3)\n", 393 | " return layer_dict\n", 394 | "\n", 395 | " def cal_fitness(self, line, epochs):\n", 396 | " \"\"\"\n", 397 | " :param line: 染色体（网络）\n", 398 | " :param epochs: 迭代次数\n", 399 | " :return:\n", 400 | " \"\"\"\n", 401 | " if epochs == 0:\n", 402 | " return line\n", 403 | " line = self.train_process(line, epochs)\n", 404 | " # 适应度函数，表示准确率 + 训练参数个数的倒数， 适应度越大，说明模型越好。\n", 405 | " # fitness = line['accuracy'] + line['punish_factor']\n", 406 | " fitness = line['accuracy'] \n", 407 | " line['fitness'] = fitness\n", 408 | " return line\n", 409 | "\n", 410 | " def train_process(self, line, epochs):\n", 411 | " \"\"\"\n", 412 | " 训练\n", 413 | " :param line: 染色体\n", 414 | " :param epochs: 迭代次数\n", 415 | " :return:\n", 416 | " \"\"\"\n", 417 | " \n", 418 | " print('learning_rate:', line['learning_rate'])\n", 419 | " print('layer_num:', len(line['layer_list']))\n", 420 | " \n", 421 | " if line['is_saved']: # 若保存，则直接读入训练即可\n", 422 | " print('读取原有模型训练..............')\n", 423 | " model_path = line['model_path']\n", 424 | " model = load_model(model_path)\n", 425 | " accuracy = model.evaluate(x = x_test,y = y_test)[1]\n", 426 | " print('former accuracy:', accuracy)\n", 427 | " else:\n", 428 | " print('重新训练.......')\n", 429 | " model = line['model']\n", 430 | " learning_rate = line['learning_rate']\n", 431 | " # 初始化adam优化器\n", 432 | " opt = keras.optimizers.Adam(lr=learning_rate, beta_1=0.9, beta_2=0.999,\n", 433 | " epsilon=None, decay=1e-6, amsgrad=False)\n", 434 | " # 编译模型\n", 435 | " layer_list = line['layer_list']\n", 436 | " if layer_list[-1]['layer_activation_function'] == 'softmax':\n", 437 | " loss = 'sparse_categorical_crossentropy'\n", 438 | " else:\n", 439 | " loss = 'binary_crossentropy'\n", 440 | " print(loss)\n", 441 | " model.compile(loss=loss,\n", 442 | " optimizer=opt,\n", 443 | " metrics=['accuracy'])\n", 444 | "\n", 445 | " history = model.fit(x_train, y_train,epochs=epochs, batch_size=self.batch_size)\n", 446 | " # Score trained model.\n", 447 | " accuracy = model.evaluate(x = x_test,y = y_test)[1]\n", 448 | " line['accuracy'] = accuracy\n", 449 | " line['history'] = history # 训练历史\n", 450 | " print('accuracy:', accuracy)\n", 451 | " # 保存模型\n", 452 | " model_name = line['model_name']\n", 453 | " model_path = os.path.join(save_dir, model_name)\n", 454 | " line['model_path'] = model_path # 每一个模型的路径\n", 455 | " line['is_saved'] = True # 是否保存\n", 456 | " line['model'] = model\n", 457 | " model.save(model_path)\n", 458 | " \n", 459 | " return line\n", 460 | "\n", 461 | " def mutation(self, line, name):\n", 462 | " \"\"\"\n", 463 | " 基因变异\n", 464 | " :param line:\n", 465 | " :return:\n", 466 | " \"\"\"\n", 467 | " offspring1 = copy.deepcopy(line) # 深拷贝！！！！子代1\n", 468 | " offspring1['model_name'] = name\n", 469 | " offspring1['is_saved'] = False\n", 470 | " mutation_choice = [True, False]\n", 471 | " # 子代1变异\n", 472 | " if mutation_choice[random.randint(0, 1)]: # 改变学习率\n", 473 | " print('Mutation Operation: Change learning rate....')\n", 474 | " offspring1['learning_rate'] = self.random_learning_rate() # 学习率\n", 475 | " else: # 改变网络结构\n", 476 | " offspring1 = self.layer_mutation_operation(offspring1)\n", 477 | " offspring1 = self.create_network(offspring1)\n", 478 | " return offspring1\n", 479 | "\n", 480 | " def layer_mutation_operation(self, offspring):\n", 481 | " \"\"\"\n", 482 | " :param offspring: 子代染色体\n", 483 | " :return:\n", 484 | " \"\"\"\n", 485 | " mutation_layer_choice = [0, 1, 2] # 添加，替换，删除\n", 486 | " mutation_layer_choice_name = ['Add', 'Replace', 'Delete']\n", 487 | " layer_name = self.layer_list[random.randint(0, 1)]\n", 488 | " layer_dict = self.create_layer(layer_name)\n", 489 | " choice_index = -1\n", 490 | " if self.threshold < offspring['layer_num'] < self.max_size: # 层数小于最大值且大于临界值，则可以添加和替换和删除\n", 491 | " choice_index = random.randint(0, 2)\n", 492 | " if mutation_layer_choice[choice_index] == 0: # 添加\n", 493 | " insert_index = random.randint(1, len(offspring['layer_list']) - 1) # 插入位置\n", 494 | " offspring['layer_list'].insert(insert_index, layer_dict)\n", 495 | " offspring['layer_num'] += 1\n", 496 | "\n", 497 | " elif mutation_layer_choice[choice_index] == 1: # 替换\n", 498 | " replace_index = random.randint(1, len(offspring['layer_list']) - 1) # 替换位置\n", 499 | " offspring['layer_list'][replace_index] = layer_dict\n", 500 | "\n", 501 | " else: # 删除层\n", 502 | " delete_index = random.randint(1, len(offspring['layer_list']) - 1) # 删除位置\n", 503 | " del offspring['layer_list'][delete_index]\n", 504 | " offspring['layer_num'] -= 1\n", 505 | "\n", 506 | " elif offspring['layer_num'] <= self.threshold: # 小于等于临界值，只能添加或者替换\n", 507 | " choice_index = random.randint(0, 1)\n", 508 | " if mutation_layer_choice[choice_index] == 0: # 添加\n", 509 | " insert_index = random.randint(1, len(offspring['layer_list']) - 1) # 插入位置\n", 510 | " offspring['layer_list'].insert(insert_index, layer_dict)\n", 511 | " offspring['layer_num'] += 1\n", 512 | " else:\n", 513 | " replace_index = random.randint(1, len(offspring['layer_list']) - 1) # 替换位置\n", 514 | " offspring['layer_list'][replace_index] = layer_dict\n", 515 | "\n", 516 | " else: # 层数到达最大值，则只能替换和删除\n", 517 | " choice_index = random.randint(1, 2)\n", 518 | " if mutation_layer_choice[choice_index] == 1: # 替换层\n", 519 | " replace_index = random.randint(1, len(offspring['layer_list']) - 1) # 替换位置\n", 520 | " offspring['layer_list'][replace_index] = layer_dict\n", 521 | "\n", 522 | " else: # 删除层\n", 523 | " delete_index = random.randint(1, len(offspring['layer_list']) - 1) # 删除位置\n", 524 | " del offspring['layer_list'][delete_index]\n", 525 | " offspring['layer_num'] -= 1\n", 526 | " print('Mutation Operation:', mutation_layer_choice_name[choice_index])\n", 527 | " return offspring\n", 528 | "\n", 529 | " def get_best_chromosome(self, father, offspring1, offspring2, epochs):\n", 530 | " \"\"\"\n", 531 | " 比较父代，子代1，子代2的适应度，返回适应度最大的染色体\n", 532 | " :param father:\n", 533 | " :param offspring1:\n", 534 | " :param offspring2:\n", 535 | " :param epochs:\n", 536 | " :return: 返回适应度最高的染色体\n", 537 | " \"\"\"\n", 538 | " print('子代1训练：', epochs)\n", 539 | " offspring1 = self.cal_fitness(offspring1, epochs)\n", 540 | " \n", 541 | " print('子代2训练：', epochs)\n", 542 | " offspring2 = self.cal_fitness(offspring2, epochs)\n", 543 | " \n", 544 | " tmp_lines = [father, offspring1, offspring2]\n", 545 | " sorted_lines = sorted(tmp_lines, key=operator.itemgetter('fitness')) # 按适应度从小到大排序\n", 546 | " return sorted_lines[-1]\n", 547 | "\n", 548 | " def random_learning_rate(self):\n", 549 | " return random.uniform(0.01, 0.02) # 学习率\n", 550 | "\n", 551 | " def random_select(self, ran_fit):\n", 552 | " \"\"\"\n", 553 | " 轮盘赌选择\n", 554 | " 根据概率随机选择的染色体\n", 555 | " :param ran_fit:\n", 556 | " :return:\n", 557 | " \"\"\"\n", 558 | " ran = random.random()\n", 559 | " for i in range(self.rows):\n", 560 | " if ran < ran_fit[i]:\n", 561 | " return i\n", 562 | " \n", 563 | " \n" 564 | ] 565 | }, 566 | { 567 | "cell_type": "code", 568 | "execution_count": null, 569 | "metadata": { 570 | "scrolled": false 571 | }, 572 | "outputs": [ 573 | { 574 | "name": "stdout", 575 | "output_type": "stream", 576 | "text": [ 577 | "开始迭代\n" 578 | ] 579 | }, 580 | { 581 | "name": "stderr", 582 | "output_type": "stream", 583 | "text": [ 584 | "C:\\Users\\SZU\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:203: UserWarning: Update your `MaxPooling2D` call to the Keras 2 API: `MaxPooling2D(pool_size=(2, 2), data_format=\"channels_last\")`\n", 585 | "C:\\Users\\SZU\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:203: UserWarning: Update your `MaxPooling2D` call to the Keras 2 API: `MaxPooling2D(pool_size=(3, 3), data_format=\"channels_last\")`\n" 586 | ] 587 | }, 588 | { 589 | "name": "stdout", 590 | "output_type": "stream", 591 | "text": [ 592 | "learning_rate: 0.012029467024930782\n", 593 | "layer_num: 4\n", 594 | "重新训练.......\n", 595 | "sparse_categorical_crossentropy\n", 596 | "Epoch 1/1\n", 597 | "22046/22046 [==============================] - 28s 1ms/step - loss: 7.5838 - acc: 0.5212\n", 598 | "5512/5512 [==============================] - 6s 1ms/step\n", 599 | "accuracy: 0.49038461538461536\n", 600 | "learning_rate: 0.015437717269751258\n", 601 | "layer_num: 4\n", 602 | "重新训练.......\n", 603 | "sparse_categorical_crossentropy\n", 604 | "Epoch 1/1\n", 605 | "22046/22046 [==============================] - 22s 1ms/step - loss: 7.7997 - acc: 0.5026\n", 606 | "5512/5512 [==============================] - 6s 1ms/step\n", 607 | "accuracy: 0.49038461538461536\n", 608 | "learning_rate: 0.01007904573168161\n", 609 | "layer_num: 5\n", 610 | "重新训练.......\n", 611 | "binary_crossentropy\n", 612 | "Epoch 1/1\n" 613 | ] 614 | } 615 | ], 616 | "source": [ 617 | "# -------------入口函数，开始执行-----------------------------\n", 618 | "\"\"\"\n", 619 | "输入参数的的意义依次为\n", 620 | " self.rows = rows # 染色体个数(即种群大小：101个网络)\n", 621 | " self.times = times # 迭代次数\n", 622 | " self.num_classes = num_classes # 几分类\n", 623 | " self.kill_num = kill_num # 每次迭代杀死的网络\n", 624 | "\"\"\"\n", 625 | "if __name__ == '__main__':\n", 626 | " ga = GeneticAlgorithm(rows=50, times=3, num_classes=2, kill_num=7)\n", 627 | " ga.run()\n" 628 | ] 629 | } 630 | ], 631 | "metadata": { 632 | "kernelspec": { 633 | "display_name": "Python 3", 634 | "language": "python", 635 | "name": "python3" 636 | }, 637 | "language_info": { 638 | "codemirror_mode": { 639 | "name": "ipython", 640 | "version": 3 641 | }, 642 | "file_extension": ".py", 643 | "mimetype": "text/x-python", 644 | "name": "python", 645 | "nbconvert_exporter": "python", 646 | "pygments_lexer": "ipython3", 647 | "version": "3.7.3" 648 | } 649 | }, 650 | "nbformat": 4, 651 | "nbformat_minor": 2 652 | } 653 | -------------------------------------------------------------------------------- /GAN/.ipynb_checkpoints/GAN-checkpoint.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [], 3 | "metadata": {}, 4 | "nbformat": 4, 5 | "nbformat_minor": 2 6 | } 7 | -------------------------------------------------------------------------------- /MLP/.ipynb_checkpoints/MLP_Text-checkpoint.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 5, 6 | "metadata": {}, 7 | "outputs": [ 8 | { 9 | "name": "stderr", 10 | "output_type": "stream", 11 | "text": [ 12 | "Using TensorFlow backend.\n" 13 | ] 14 | } 15 | ], 16 | "source": [ 17 | "import pandas as pd\n", 18 | "import jieba\n", 19 | "import jieba.analyse as analyse\n", 20 | "from keras.preprocessing.text import Tokenizer\n", 21 | "from keras.preprocessing import sequence\n", 22 | "from keras.models import Sequential\n", 23 | "from keras.layers import Dense, Dropout, Activation, Flatten, MaxPool1D, Conv1D\n", 24 | "from keras.layers.embeddings import Embedding\n", 25 | "from keras.utils import multi_gpu_model\n", 26 | "from keras.models import load_model\n", 27 | "from keras import regularizers # 正则化\n", 28 | "import matplotlib.pyplot as plt\n", 29 | "import numpy as np\n", 30 | "from keras.utils import plot_model\n", 31 | "from sklearn.model_selection import train_test_split\n", 32 | "from keras.utils.np_utils import to_categorical\n", 33 | "from sklearn.preprocessing import LabelEncoder\n", 34 | "from keras.layers import BatchNormalization" 35 | ] 36 | }, 37 | { 38 | "cell_type": "code", 39 | "execution_count": 8, 40 | "metadata": {}, 41 | "outputs": [ 42 | { 43 | "name": "stdout", 44 | "output_type": "stream", 45 | "text": [ 46 | " PositionType Job_Description\n", 47 | "0 项目管理 \\r\\n 岗位职责： \\r\\n 1 、熟练使用 axure,visio ，熟悉竞品分析，...\n", 48 | "1 项目管理 \\r\\n 岗位职责： \\r\\n 1 、熟练使用 axure,visio ，熟悉竞品分析，...\n", 49 | "2 移动开发 \\r\\n 岗位职责： \\r\\n 1.负责安卓客户端应用的框架设计； \\r\\n 2.负责安卓客...\n", 50 | "3 移动开发 \\r\\n 现诚招资深iOS高级软件开发工程师一枚！ 【你的工作职责】 1、负责iPhone手...\n", 51 | "4 后端开发 \\r\\n 岗位职责： \\r\\n 1、基于海量交通信息数据的数据仓库建设、数据应用开发。 2、...\n", 52 | "['项目管理', '移动开发', '后端开发', '前端开发', '测试', '高端技术职位', '硬件开发', 'dba', '运维', '企业软件']\n" 53 | ] 54 | } 55 | ], 56 | "source": [ 57 | "job_detail_pd = pd.read_csv('job_detail_dataset.csv', encoding='UTF-8')\n", 58 | "print(job_detail_pd.head(5))\n", 59 | "label = list(job_detail_pd['PositionType'].unique()) # 标签\n", 60 | "print(label)" 61 | ] 62 | }, 63 | { 64 | "cell_type": "code", 65 | "execution_count": 9, 66 | "metadata": {}, 67 | "outputs": [ 68 | { 69 | "data": { 70 | "text/html": [ 71 | "
\n", 72 | "\n", 85 | "\n", 86 | " \n", 87 | " \n", 88 | " \n", 89 | " \n", 90 | " \n", 91 | " \n", 92 | " \n", 93 | " \n", 94 | " \n", 95 | " \n", 96 | " \n", 97 | " \n", 98 | " \n", 99 | " \n", 100 | " \n", 101 | " \n", 102 | " \n", 103 | " \n", 104 | " \n", 105 | " \n", 106 | " \n", 107 | " \n", 108 | " \n", 109 | " \n", 110 | " \n", 111 | " \n", 112 | " \n", 113 | " \n", 114 | " \n", 115 | " \n", 116 | " \n", 117 | " \n", 118 | " \n", 119 | " \n", 120 | " \n", 121 | " \n", 122 | " \n", 123 | " \n", 124 | " \n", 125 | " \n", 126 | "
PositionTypeJob_Descriptionlabel
0项目管理\\r\\n 岗位职责： \\r\\n 1 、熟练使用 axure,visio ，熟悉竞品分析，...0
1项目管理\\r\\n 岗位职责： \\r\\n 1 、熟练使用 axure,visio ，熟悉竞品分析，...0
2移动开发\\r\\n 岗位职责： \\r\\n 1.负责安卓客户端应用的框架设计； \\r\\n 2.负责安卓客...1
3移动开发\\r\\n 现诚招资深iOS高级软件开发工程师一枚！ 【你的工作职责】 1、负责iPhone手...1
4后端开发\\r\\n 岗位职责： \\r\\n 1、基于海量交通信息数据的数据仓库建设、数据应用开发。 2、...2
\n", 127 | "
" 128 | ], 129 | "text/plain": [ 130 | " PositionType Job_Description label\n", 131 | "0 项目管理 \\r\\n 岗位职责： \\r\\n 1 、熟练使用 axure,visio ，熟悉竞品分析，... 0\n", 132 | "1 项目管理 \\r\\n 岗位职责： \\r\\n 1 、熟练使用 axure,visio ，熟悉竞品分析，... 0\n", 133 | "2 移动开发 \\r\\n 岗位职责： \\r\\n 1.负责安卓客户端应用的框架设计； \\r\\n 2.负责安卓客... 1\n", 134 | "3 移动开发 \\r\\n 现诚招资深iOS高级软件开发工程师一枚！ 【你的工作职责】 1、负责iPhone手... 1\n", 135 | "4 后端开发 \\r\\n 岗位职责： \\r\\n 1、基于海量交通信息数据的数据仓库建设、数据应用开发。 2、... 2" 136 | ] 137 | }, 138 | "execution_count": 9, 139 | "metadata": {}, 140 | "output_type": "execute_result" 141 | } 142 | ], 143 | "source": [ 144 | "# 上标签\n", 145 | "def label_dataset(row):\n", 146 | " num_label = label.index(row) # 返回label列表对应值的索引\n", 147 | " return num_label\n", 148 | "\n", 149 | "job_detail_pd['label'] = job_detail_pd['PositionType'].apply(label_dataset)\n", 150 | "job_detail_pd = job_detail_pd.dropna() # 删除空行\n", 151 | "job_detail_pd.head(5)" 152 | ] 153 | }, 154 | { 155 | "cell_type": "code", 156 | "execution_count": 10, 157 | "metadata": {}, 158 | "outputs": [ 159 | { 160 | "name": "stderr", 161 | "output_type": "stream", 162 | "text": [ 163 | "Building prefix dict from the default dictionary ...\n", 164 | "Loading model from cache /tmp/jieba.cache\n", 165 | "Loading model cost 1.418 seconds.\n", 166 | "Prefix dict has been built succesfully.\n" 167 | ] 168 | }, 169 | { 170 | "data": { 171 | "text/html": [ 172 | "
\n", 173 | "\n", 186 | "\n", 187 | " \n", 188 | " \n", 189 | " \n", 190 | " \n", 191 | " \n", 192 | " \n", 193 | " \n", 194 | " \n", 195 | " \n", 196 | " \n", 197 | " \n", 198 | " \n", 199 | " \n", 200 | " \n", 201 | " \n", 202 | " \n", 203 | " \n", 204 | " \n", 205 | " \n", 206 | " \n", 207 | " \n", 208 | " \n", 209 | " \n", 210 | " \n", 211 | " \n", 212 | " \n", 213 | " \n", 214 | " \n", 215 | " \n", 216 | " \n", 217 | " \n", 218 | " \n", 219 | " \n", 220 | " \n", 221 | " \n", 222 | " \n", 223 | " \n", 224 | " \n", 225 | " \n", 226 | " \n", 227 | " \n", 228 | " \n", 229 | " \n", 230 | " \n", 231 | " \n", 232 | " \n", 233 | "
PositionTypeJob_DescriptionlabelJob_Description_jieba_cut
0项目管理\\r\\n 岗位职责： \\r\\n 1 、熟练使用 axure,visio ，熟悉竞品分析，...0\\r\\n 岗位职责 ： \\r\\n 1 、 熟练 使用 axure...
1项目管理\\r\\n 岗位职责： \\r\\n 1 、熟练使用 axure,visio ，熟悉竞品分析，...0\\r\\n 岗位职责 ： \\r\\n 1 、 熟练 使用 axure...
2移动开发\\r\\n 岗位职责： \\r\\n 1.负责安卓客户端应用的框架设计； \\r\\n 2.负责安卓客...1\\r\\n 岗位职责 ： \\r\\n 1 . 负责 安卓 客户端 应用 的 框架 设...
3移动开发\\r\\n 现诚招资深iOS高级软件开发工程师一枚！ 【你的工作职责】 1、负责iPhone手...1\\r\\n 现 诚招 资深 iOS 高级 软件开发 工程师 一枚 ！ 【 你 的 工作...
4后端开发\\r\\n 岗位职责： \\r\\n 1、基于海量交通信息数据的数据仓库建设、数据应用开发。 2、...2\\r\\n 岗位职责 ： \\r\\n 1 、 基于 海量 交通 信息 数据 的 数据...
\n", 234 | "
" 235 | ], 236 | "text/plain": [ 237 | " PositionType Job_Description label \\\n", 238 | "0 项目管理 \\r\\n 岗位职责： \\r\\n 1 、熟练使用 axure,visio ，熟悉竞品分析，... 0 \n", 239 | "1 项目管理 \\r\\n 岗位职责： \\r\\n 1 、熟练使用 axure,visio ，熟悉竞品分析，... 0 \n", 240 | "2 移动开发 \\r\\n 岗位职责： \\r\\n 1.负责安卓客户端应用的框架设计； \\r\\n 2.负责安卓客... 1 \n", 241 | "3 移动开发 \\r\\n 现诚招资深iOS高级软件开发工程师一枚！ 【你的工作职责】 1、负责iPhone手... 1 \n", 242 | "4 后端开发 \\r\\n 岗位职责： \\r\\n 1、基于海量交通信息数据的数据仓库建设、数据应用开发。 2、... 2 \n", 243 | "\n", 244 | " Job_Description_jieba_cut \n", 245 | "0 \\r\\n 岗位职责 ： \\r\\n 1 、 熟练 使用 axure... \n", 246 | "1 \\r\\n 岗位职责 ： \\r\\n 1 、 熟练 使用 axure... \n", 247 | "2 \\r\\n 岗位职责 ： \\r\\n 1 . 负责 安卓 客户端 应用 的 框架 设... \n", 248 | "3 \\r\\n 现 诚招 资深 iOS 高级 软件开发 工程师 一枚 ！ 【 你 的 工作... \n", 249 | "4 \\r\\n 岗位职责 ： \\r\\n 1 、 基于 海量 交通 信息 数据 的 数据... " 250 | ] 251 | }, 252 | "execution_count": 10, 253 | "metadata": {}, 254 | "output_type": "execute_result" 255 | } 256 | ], 257 | "source": [ 258 | "# 中文分词\n", 259 | "def chinese_word_cut(row):\n", 260 | " return \" \".join(jieba.cut(row))\n", 261 | "\n", 262 | "job_detail_pd['Job_Description_jieba_cut'] = job_detail_pd.Job_Description.apply(chinese_word_cut)\n", 263 | "job_detail_pd.head(5)" 264 | ] 265 | }, 266 | { 267 | "cell_type": "code", 268 | "execution_count": 11, 269 | "metadata": {}, 270 | "outputs": [ 271 | { 272 | "data": { 273 | "text/html": [ 274 | "
\n", 275 | "\n", 288 | "\n", 289 | " \n", 290 | " \n", 291 | " \n", 292 | " \n", 293 | " \n", 294 | " \n", 295 | " \n", 296 | " \n", 297 | " \n", 298 | " \n", 299 | " \n", 300 | " \n", 301 | " \n", 302 | " \n", 303 | " \n", 304 | " \n", 305 | " \n", 306 | " \n", 307 | " \n", 308 | " \n", 309 | " \n", 310 | " \n", 311 | " \n", 312 | " \n", 313 | " \n", 314 | " \n", 315 | " \n", 316 | " \n", 317 | " \n", 318 | " \n", 319 | " \n", 320 | " \n", 321 | " \n", 322 | " \n", 323 | " \n", 324 | " \n", 325 | " \n", 326 | " \n", 327 | " \n", 328 | " \n", 329 | " \n", 330 | " \n", 331 | " \n", 332 | " \n", 333 | " \n", 334 | " \n", 335 | " \n", 336 | " \n", 337 | " \n", 338 | " \n", 339 | " \n", 340 | " \n", 341 | "
PositionTypeJob_DescriptionlabelJob_Description_jieba_cutJob_Description_key_word
0项目管理\\r\\n 岗位职责： \\r\\n 1 、熟练使用 axure,visio ，熟悉竞品分析，...0\\r\\n 岗位职责 ： \\r\\n 1 、 熟练 使用 axure...互联网 体验 用户 产品 优先 运营 熟悉 电商 axure visio 竞品 O2O 岗位...
1项目管理\\r\\n 岗位职责： \\r\\n 1 、熟练使用 axure,visio ，熟悉竞品分析，...0\\r\\n 岗位职责 ： \\r\\n 1 、 熟练 使用 axure...互联网 体验 用户 产品 优先 运营 熟悉 电商 axure visio 竞品 O2O 岗位...
2移动开发\\r\\n 岗位职责： \\r\\n 1.负责安卓客户端应用的框架设计； \\r\\n 2.负责安卓客...1\\r\\n 岗位职责 ： \\r\\n 1 . 负责 安卓 客户端 应用 的 框架 设...Android 安卓 java 客户端 能力 编程 具备 应用 良好 开发 优先 测试人员 ...
3移动开发\\r\\n 现诚招资深iOS高级软件开发工程师一枚！ 【你的工作职责】 1、负责iPhone手...1\\r\\n 现 诚招 资深 iOS 高级 软件开发 工程师 一枚 ！ 【 你 的 工作...iOS 开发 应用 技术 素质 用户 适配 平台 iPhone iPad 专业本科 编写程序...
4后端开发\\r\\n 岗位职责： \\r\\n 1、基于海量交通信息数据的数据仓库建设、数据应用开发。 2、...2\\r\\n 岗位职责 ： \\r\\n 1 、 基于 海量 交通 信息 数据 的 数据...数据仓库 Hadoop Hive Hbase 开发 数据 优先 交通 经验 应用 相关 智能...
\n", 342 | "
" 343 | ], 344 | "text/plain": [ 345 | " PositionType Job_Description label \\\n", 346 | "0 项目管理 \\r\\n 岗位职责： \\r\\n 1 、熟练使用 axure,visio ，熟悉竞品分析，... 0 \n", 347 | "1 项目管理 \\r\\n 岗位职责： \\r\\n 1 、熟练使用 axure,visio ，熟悉竞品分析，... 0 \n", 348 | "2 移动开发 \\r\\n 岗位职责： \\r\\n 1.负责安卓客户端应用的框架设计； \\r\\n 2.负责安卓客... 1 \n", 349 | "3 移动开发 \\r\\n 现诚招资深iOS高级软件开发工程师一枚！ 【你的工作职责】 1、负责iPhone手... 1 \n", 350 | "4 后端开发 \\r\\n 岗位职责： \\r\\n 1、基于海量交通信息数据的数据仓库建设、数据应用开发。 2、... 2 \n", 351 | "\n", 352 | " Job_Description_jieba_cut \\\n", 353 | "0 \\r\\n 岗位职责 ： \\r\\n 1 、 熟练 使用 axure... \n", 354 | "1 \\r\\n 岗位职责 ： \\r\\n 1 、 熟练 使用 axure... \n", 355 | "2 \\r\\n 岗位职责 ： \\r\\n 1 . 负责 安卓 客户端 应用 的 框架 设... \n", 356 | "3 \\r\\n 现 诚招 资深 iOS 高级 软件开发 工程师 一枚 ！ 【 你 的 工作... \n", 357 | "4 \\r\\n 岗位职责 ： \\r\\n 1 、 基于 海量 交通 信息 数据 的 数据... \n", 358 | "\n", 359 | " Job_Description_key_word \n", 360 | "0 互联网 体验 用户 产品 优先 运营 熟悉 电商 axure visio 竞品 O2O 岗位... \n", 361 | "1 互联网 体验 用户 产品 优先 运营 熟悉 电商 axure visio 竞品 O2O 岗位... \n", 362 | "2 Android 安卓 java 客户端 能力 编程 具备 应用 良好 开发 优先 测试人员 ... \n", 363 | "3 iOS 开发 应用 技术 素质 用户 适配 平台 iPhone iPad 专业本科 编写程序... \n", 364 | "4 数据仓库 Hadoop Hive Hbase 开发 数据 优先 交通 经验 应用 相关 智能... " 365 | ] 366 | }, 367 | "execution_count": 11, 368 | "metadata": {}, 369 | "output_type": "execute_result" 370 | } 371 | ], 372 | "source": [ 373 | "# 提取关键词\n", 374 | "def key_word_extract(texts):\n", 375 | " return \" \".join(analyse.extract_tags(texts, topK=50, withWeight=False, allowPOS=()))\n", 376 | "job_detail_pd['Job_Description_key_word'] = job_detail_pd.Job_Description.apply(key_word_extract)\n", 377 | "job_detail_pd.head(5)" 378 | ] 379 | }, 380 | { 381 | "cell_type": "markdown", 382 | "metadata": {}, 383 | "source": [ 384 | "## 训练全过程\n", 385 | "步骤1：读取数据集；\n", 386 | "\n", 387 | "步骤2：建立token字典；\n", 388 | "\n", 389 | "步骤3：使用token字典将“文字”转化为“数字列表”\n", 390 | "\n", 391 | "步骤4：截长补短让所有“数字列表”长度都是100\n", 392 | "\n", 393 | "步骤5：Embedding层将“数字列表”转化为\"向量列表\"；\n", 394 | "\n", 395 | "步骤6：将向量列表送入深度学习模型进行训练" 396 | ] 397 | }, 398 | { 399 | "cell_type": "code", 400 | "execution_count": null, 401 | "metadata": {}, 402 | "outputs": [], 403 | "source": [ 404 | "# 建立2000个词的字典\n", 405 | "token = Tokenizer(num_words = 2000) \n", 406 | "token.fit_on_texts(job_detail_pd['Job_Description_key_word']) #按单词出现次数排序，排序前2000的单词会列入词典中" 407 | ] 408 | }, 409 | { 410 | "cell_type": "code", 411 | "execution_count": null, 412 | "metadata": {}, 413 | "outputs": [], 414 | "source": [ 415 | "print(token.document_count) # 查看token读了多少文章\n", 416 | "print(token.word_index) # 查看token读了多少文章" 417 | ] 418 | }, 419 | { 420 | "cell_type": "code", 421 | "execution_count": null, 422 | "metadata": {}, 423 | "outputs": [], 424 | "source": [ 425 | "# 使用token字典将“文字”转化为“数字列表”\n", 426 | "Job_Description_Seq = token.texts_to_sequences(job_detail_pd['Job_Description_key_word'])" 427 | ] 428 | }, 429 | { 430 | "cell_type": "code", 431 | "execution_count": null, 432 | "metadata": {}, 433 | "outputs": [], 434 | "source": [ 435 | "# 截长补短让所有“数字列表”长度都是50\n", 436 | "Job_Description_Seq_Padding = sequence.pad_sequences(Job_Description_Seq, maxlen=50)\n", 437 | "\n", 438 | "x_train = Job_Description_Seq_Padding\n", 439 | "y_train = job_detail_pd['label'].tolist()" 440 | ] 441 | }, 442 | { 443 | "cell_type": "code", 444 | "execution_count": null, 445 | "metadata": {}, 446 | "outputs": [], 447 | "source": [ 448 | "y_train_one_hot_encoded = pd.get_dummies(job_detail_pd['label'], sparse = True)\n", 449 | "print(y_train_one_hot_encoded)" 450 | ] 451 | }, 452 | { 453 | "cell_type": "markdown", 454 | "metadata": {}, 455 | "source": [ 456 | "## 开始训练MLP" 457 | ] 458 | }, 459 | { 460 | "cell_type": "code", 461 | "execution_count": null, 462 | "metadata": {}, 463 | "outputs": [], 464 | "source": [ 465 | "batch_size = 256\n", 466 | "epochs = 5\n", 467 | "model = Sequential()\n", 468 | "model.add(Embedding(output_dim = 32, # 词向量的维度\n", 469 | " input_dim = 2000, # Size of the vocabulary 字典大小\n", 470 | " input_length = 50 # 每个数字列表的长度\n", 471 | " ) \n", 472 | " )\n", 473 | "\n", 474 | "model.add(Dropout(0.2)) \n", 475 | "model.add(Flatten()) # 平展\n", 476 | "model.add(Dense(units = 256,\n", 477 | " activation = \"relu\"))\n", 478 | "model.add(Dropout(0.25))\n", 479 | "model.add(Dense(units = 10,\n", 480 | " activation = \"softmax\"))\n", 481 | "\n", 482 | "print(model.summary()) # 打印模型\n", 483 | "# CPU版本\n", 484 | "model.compile(loss = \"sparse_categorical_crossentropy\", # 多分类\n", 485 | " optimizer = \"adam\",\n", 486 | " metrics = [\"accuracy\"]\n", 487 | " )\n", 488 | "\n", 489 | "history = model.fit(\n", 490 | " x_train, \n", 491 | " y_train, \n", 492 | " batch_size = batch_size,\n", 493 | " epochs = epochs,\n", 494 | " verbose = 2,\n", 495 | " validation_split = 0.2 # 训练集的20%用作验证集\n", 496 | ")\n", 497 | "\n", 498 | "# GPU版本\n", 499 | "\"\"\"\n", 500 | " 如果你的 label 是 one-hot 编码，用 categorical_crossentropy\n", 501 | " one-hot 编码：[0, 0, 1], [1, 0, 0], [0, 1, 0]\n", 502 | " 如果你的 tagets 是 数字编码 ，用 sparse_categorical_crossentropy\n", 503 | " 数字编码：2, 0, 1\n", 504 | " \"\"\"\n", 505 | "# parallel_model = multi_gpu_model(model, gpus=4)\n", 506 | "# parallel_model.compile(loss='sparse_categorical_crossentropy', # 多分类 \n", 507 | "# optimizer='adam',\n", 508 | "# metrics=['accuracy']\n", 509 | "# )\n", 510 | "\n", 511 | "# This `fit` call will be distributed on 4 GPUs.\n", 512 | "# Since the batch size is 50, each GPU will process 32 samples.\n", 513 | "# batch_size = 512\n", 514 | "# epochs = 2\n", 515 | "# history = parallel_model.fit(\n", 516 | "# x_train, \n", 517 | "# y_train,\n", 518 | "# batch_size=batch_size,\n", 519 | "# epochs=epochs,\n", 520 | "# validation_split = 0.2\n", 521 | "# )\n", 522 | "\n" 523 | ] 524 | }, 525 | { 526 | "cell_type": "markdown", 527 | "metadata": {}, 528 | "source": [ 529 | "## 保存模型 & 模型可视化" 530 | ] 531 | }, 532 | { 533 | "cell_type": "code", 534 | "execution_count": null, 535 | "metadata": {}, 536 | "outputs": [], 537 | "source": [ 538 | "from keras.utils import plot_model\n", 539 | "# 保存模型\n", 540 | "model.save('model_MLP_text.h5') # creates a HDF5 file 'my_model.h5'\n", 541 | "# 模型可视化\n", 542 | "plot_model(model, to_file='model_MLP_text.png', show_shapes=True)" 543 | ] 544 | }, 545 | { 546 | "cell_type": "markdown", 547 | "metadata": {}, 548 | "source": [ 549 | "## 模型的预测功能" 550 | ] 551 | }, 552 | { 553 | "cell_type": "code", 554 | "execution_count": null, 555 | "metadata": {}, 556 | "outputs": [], 557 | "source": [ 558 | "from keras.models import load_model\n", 559 | "# 加载模型\n", 560 | "model = load_model('model_MLP_text.h5')\n", 561 | "y_new = model.predict(x_train[0])\n", 562 | "print(y_new)\n", 563 | "print(y_train[0])" 564 | ] 565 | }, 566 | { 567 | "cell_type": "markdown", 568 | "metadata": {}, 569 | "source": [ 570 | "## 训练可视化" 571 | ] 572 | }, 573 | { 574 | "cell_type": "code", 575 | "execution_count": null, 576 | "metadata": {}, 577 | "outputs": [], 578 | "source": [ 579 | "import matplotlib.pyplot as plt\n", 580 | "# 绘制训练 & 验证的准确率值\n", 581 | "plt.plot(history.history['acc'])\n", 582 | "plt.plot(history.history['val_acc'])\n", 583 | "plt.title('Model accuracy')\n", 584 | "plt.ylabel('Accuracy')\n", 585 | "plt.xlabel('Epoch')\n", 586 | "plt.legend(['Train', 'Valid'], loc='upper left')\n", 587 | "plt.show()\n", 588 | "\n", 589 | "# 绘制训练 & 验证的损失值\n", 590 | "plt.plot(history.history['loss'])\n", 591 | "plt.plot(history.history['val_loss'])\n", 592 | "plt.title('Model loss')\n", 593 | "plt.ylabel('Loss')\n", 594 | "plt.xlabel('Epoch')\n", 595 | "plt.legend(['Train', 'Valid'], loc='upper left')\n", 596 | "plt.show()" 597 | ] 598 | } 599 | ], 600 | "metadata": { 601 | "kernelspec": { 602 | "display_name": "Python 3", 603 | "language": "python", 604 | "name": "python3" 605 | }, 606 | "language_info": { 607 | "codemirror_mode": { 608 | "name": "ipython", 609 | "version": 3 610 | }, 611 | "file_extension": ".py", 612 | "mimetype": "text/x-python", 613 | "name": "python", 614 | "nbconvert_exporter": "python", 615 | "pygments_lexer": "ipython3", 616 | "version": "3.7.2" 617 | } 618 | }, 619 | "nbformat": 4, 620 | "nbformat_minor": 2 621 | } 622 | -------------------------------------------------------------------------------- /Neat/.ipynb_checkpoints/xor-checkpoint.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [], 3 | "metadata": {}, 4 | "nbformat": 4, 5 | "nbformat_minor": 2 6 | } 7 | -------------------------------------------------------------------------------- /Neat/Digraph.gv: -------------------------------------------------------------------------------- 1 | digraph { 2 | node [fontsize=9 height=0.2 shape=circle width=0.2] 3 | A [fillcolor=lightgray shape=box style=filled] 4 | B [fillcolor=lightgray shape=box style=filled] 5 | "A XOR B" [fillcolor=lightblue style=filled] 6 | 101 [fillcolor=white style=filled] 7 | 102 [fillcolor=white style=filled] 8 | 518 [fillcolor=white style=filled] 9 | 1806 [fillcolor=white style=filled] 10 | 1103 [fillcolor=white style=filled] 11 | 344 [fillcolor=white style=filled] 12 | A -> 101 [color=red penwidth=1.014475964605062 style=solid] 13 | B -> 101 [color=red penwidth=0.6039459171968576 style=solid] 14 | 101 -> "A XOR B" [color=red penwidth=0.684020074349466 style=solid] 15 | 102 -> 344 [color=red penwidth=0.39568527424792477 style=dotted] 16 | 1103 -> 102 [color=red penwidth=0.1543632724567388 style=dotted] 17 | B -> "A XOR B" [color=red penwidth=0.29503754516838065 style=solid] 18 | B -> 1806 [color=green penwidth=0.45344458118601894 style=solid] 19 | 1806 -> "A XOR B" [color=green penwidth=0.1072748034717091 style=solid] 20 | A -> "A XOR B" [color=red penwidth=0.2789419808374101 style=solid] 21 | } 22 | -------------------------------------------------------------------------------- /Neat/Digraph.gv.svg: -------------------------------------------------------------------------------- 1 | 2 | 4 | 6 | 7 | 9 | 10 | %3 11 | 12 | 13 | A 14 | 15 | A 16 | 17 | 18 | A XOR B 19 | 20 | A XOR B 21 | 22 | 23 | A->A XOR B 24 | 25 | 26 | 27 | 28 | 101 29 | 30 | 101 31 | 32 | 33 | A->101 34 | 35 | 36 | 37 | 38 | B 39 | 40 | B 41 | 42 | 43 | B->A XOR B 44 | 45 | 46 | 47 | 48 | B->101 49 | 50 | 51 | 52 | 53 | 1806 54 | 55 | 1806 56 | 57 | 58 | B->1806 59 | 60 | 61 | 62 | 63 | 101->A XOR B 64 | 65 | 66 | 67 | 68 | 102 69 | 70 | 102 71 | 72 | 73 | 344 74 | 75 | 344 76 | 77 | 78 | 102->344 79 | 80 | 81 | 82 | 83 | 518 84 | 85 | 518 86 | 87 | 88 | 1806->A XOR B 89 | 90 | 91 | 92 | 93 | 1103 94 | 95 | 1103 96 | 97 | 98 | 1103->102 99 | 100 | 101 | 102 | 103 | 104 | -------------------------------------------------------------------------------- /Neat/__pycache__/visualize.cpython-36.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ChileWang0228/DeepLearningTutorial/81a68bdba63c6a08f5c0c8c6289364ebdbc0b0c6/Neat/__pycache__/visualize.cpython-36.pyc -------------------------------------------------------------------------------- /Neat/__pycache__/visualize.cpython-37.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ChileWang0228/DeepLearningTutorial/81a68bdba63c6a08f5c0c8c6289364ebdbc0b0c6/Neat/__pycache__/visualize.cpython-37.pyc -------------------------------------------------------------------------------- /Neat/config-feedforward: -------------------------------------------------------------------------------- 1 | #--- parameters for the XOR-2 experiment ---# 2 | 3 | [NEAT] 4 | fitness_criterion = max 5 | fitness_threshold = 3.9 6 | pop_size = 150 7 | reset_on_extinction = False 8 | 9 | [DefaultGenome] 10 | # node activation options 11 | activation_default = sigmoid 12 | activation_mutate_rate = 0.0 13 | activation_options = sigmoid 14 | 15 | # node aggregation options 16 | aggregation_default = sum 17 | aggregation_mutate_rate = 0.0 18 | aggregation_options = sum 19 | 20 | # node bias options 21 | bias_init_mean = 0.0 22 | bias_init_stdev = 1.0 23 | bias_max_value = 30.0 24 | bias_min_value = -30.0 25 | bias_mutate_power = 0.5 26 | bias_mutate_rate = 0.7 27 | bias_replace_rate = 0.1 28 | 29 | # genome compatibility options 30 | compatibility_disjoint_coefficient = 1.0 31 | compatibility_weight_coefficient = 0.5 32 | 33 | # connection add/remove rates 34 | conn_add_prob = 0.5 35 | conn_delete_prob = 0.5 36 | 37 | # connection enable options 38 | enabled_default = True 39 | enabled_mutate_rate = 0.01 40 | 41 | feed_forward = True 42 | initial_connection = full 43 | 44 | # node add/remove rates 45 | node_add_prob = 0.2 46 | node_delete_prob = 0.2 47 | 48 | # network parameters 49 | num_hidden = 2 50 | num_inputs = 2 51 | num_outputs = 1 52 | 53 | # node response options 54 | response_init_mean = 1.0 55 | response_init_stdev = 0.0 56 | response_max_value = 30.0 57 | response_min_value = -30.0 58 | response_mutate_power = 0.0 59 | response_mutate_rate = 0.0 60 | response_replace_rate = 0.0 61 | 62 | # connection weight options 63 | weight_init_mean = 0.0 64 | weight_init_stdev = 1.0 65 | weight_max_value = 30 66 | weight_min_value = -30 67 | weight_mutate_power = 0.5 68 | weight_mutate_rate = 0.8 69 | weight_replace_rate = 0.1 70 | 71 | [DefaultSpeciesSet] 72 | compatibility_threshold = 3.0 73 | 74 | [DefaultStagnation] 75 | species_fitness_func = max 76 | max_stagnation = 20 77 | species_elitism = 2 78 | 79 | [DefaultReproduction] 80 | elitism = 2 81 | survival_threshold = 0.2 82 | -------------------------------------------------------------------------------- /Neat/neat-checkpoint-104: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ChileWang0228/DeepLearningTutorial/81a68bdba63c6a08f5c0c8c6289364ebdbc0b0c6/Neat/neat-checkpoint-104 -------------------------------------------------------------------------------- /Neat/neat-checkpoint-109: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ChileWang0228/DeepLearningTutorial/81a68bdba63c6a08f5c0c8c6289364ebdbc0b0c6/Neat/neat-checkpoint-109 -------------------------------------------------------------------------------- /Neat/neat-checkpoint-114: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ChileWang0228/DeepLearningTutorial/81a68bdba63c6a08f5c0c8c6289364ebdbc0b0c6/Neat/neat-checkpoint-114 -------------------------------------------------------------------------------- /Neat/neat-checkpoint-119: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ChileWang0228/DeepLearningTutorial/81a68bdba63c6a08f5c0c8c6289364ebdbc0b0c6/Neat/neat-checkpoint-119 -------------------------------------------------------------------------------- /Neat/neat-checkpoint-124: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ChileWang0228/DeepLearningTutorial/81a68bdba63c6a08f5c0c8c6289364ebdbc0b0c6/Neat/neat-checkpoint-124 -------------------------------------------------------------------------------- /Neat/neat-checkpoint-14: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ChileWang0228/DeepLearningTutorial/81a68bdba63c6a08f5c0c8c6289364ebdbc0b0c6/Neat/neat-checkpoint-14 -------------------------------------------------------------------------------- /Neat/neat-checkpoint-19: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ChileWang0228/DeepLearningTutorial/81a68bdba63c6a08f5c0c8c6289364ebdbc0b0c6/Neat/neat-checkpoint-19 -------------------------------------------------------------------------------- /Neat/neat-checkpoint-24: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ChileWang0228/DeepLearningTutorial/81a68bdba63c6a08f5c0c8c6289364ebdbc0b0c6/Neat/neat-checkpoint-24 -------------------------------------------------------------------------------- /Neat/neat-checkpoint-29: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ChileWang0228/DeepLearningTutorial/81a68bdba63c6a08f5c0c8c6289364ebdbc0b0c6/Neat/neat-checkpoint-29 -------------------------------------------------------------------------------- /Neat/neat-checkpoint-34: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ChileWang0228/DeepLearningTutorial/81a68bdba63c6a08f5c0c8c6289364ebdbc0b0c6/Neat/neat-checkpoint-34 -------------------------------------------------------------------------------- /Neat/neat-checkpoint-39: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ChileWang0228/DeepLearningTutorial/81a68bdba63c6a08f5c0c8c6289364ebdbc0b0c6/Neat/neat-checkpoint-39 -------------------------------------------------------------------------------- /Neat/neat-checkpoint-4: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ChileWang0228/DeepLearningTutorial/81a68bdba63c6a08f5c0c8c6289364ebdbc0b0c6/Neat/neat-checkpoint-4 -------------------------------------------------------------------------------- /Neat/neat-checkpoint-44: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ChileWang0228/DeepLearningTutorial/81a68bdba63c6a08f5c0c8c6289364ebdbc0b0c6/Neat/neat-checkpoint-44 -------------------------------------------------------------------------------- /Neat/neat-checkpoint-49: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ChileWang0228/DeepLearningTutorial/81a68bdba63c6a08f5c0c8c6289364ebdbc0b0c6/Neat/neat-checkpoint-49 -------------------------------------------------------------------------------- /Neat/neat-checkpoint-54: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ChileWang0228/DeepLearningTutorial/81a68bdba63c6a08f5c0c8c6289364ebdbc0b0c6/Neat/neat-checkpoint-54 -------------------------------------------------------------------------------- /Neat/neat-checkpoint-59: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ChileWang0228/DeepLearningTutorial/81a68bdba63c6a08f5c0c8c6289364ebdbc0b0c6/Neat/neat-checkpoint-59 -------------------------------------------------------------------------------- /Neat/neat-checkpoint-64: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ChileWang0228/DeepLearningTutorial/81a68bdba63c6a08f5c0c8c6289364ebdbc0b0c6/Neat/neat-checkpoint-64 -------------------------------------------------------------------------------- /Neat/neat-checkpoint-69: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ChileWang0228/DeepLearningTutorial/81a68bdba63c6a08f5c0c8c6289364ebdbc0b0c6/Neat/neat-checkpoint-69 -------------------------------------------------------------------------------- /Neat/neat-checkpoint-74: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ChileWang0228/DeepLearningTutorial/81a68bdba63c6a08f5c0c8c6289364ebdbc0b0c6/Neat/neat-checkpoint-74 -------------------------------------------------------------------------------- /Neat/neat-checkpoint-79: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ChileWang0228/DeepLearningTutorial/81a68bdba63c6a08f5c0c8c6289364ebdbc0b0c6/Neat/neat-checkpoint-79 -------------------------------------------------------------------------------- /Neat/neat-checkpoint-84: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ChileWang0228/DeepLearningTutorial/81a68bdba63c6a08f5c0c8c6289364ebdbc0b0c6/Neat/neat-checkpoint-84 -------------------------------------------------------------------------------- /Neat/neat-checkpoint-89: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ChileWang0228/DeepLearningTutorial/81a68bdba63c6a08f5c0c8c6289364ebdbc0b0c6/Neat/neat-checkpoint-89 -------------------------------------------------------------------------------- /Neat/neat-checkpoint-9: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ChileWang0228/DeepLearningTutorial/81a68bdba63c6a08f5c0c8c6289364ebdbc0b0c6/Neat/neat-checkpoint-9 -------------------------------------------------------------------------------- /Neat/neat-checkpoint-94: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ChileWang0228/DeepLearningTutorial/81a68bdba63c6a08f5c0c8c6289364ebdbc0b0c6/Neat/neat-checkpoint-94 -------------------------------------------------------------------------------- /Neat/neat-checkpoint-99: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ChileWang0228/DeepLearningTutorial/81a68bdba63c6a08f5c0c8c6289364ebdbc0b0c6/Neat/neat-checkpoint-99 -------------------------------------------------------------------------------- /Neat/visualize.py: -------------------------------------------------------------------------------- 1 | from __future__ import print_function 2 | 3 | import copy 4 | import warnings 5 | 6 | import graphviz 7 | import matplotlib.pyplot as plt 8 | import numpy as np 9 | 10 | 11 | def plot_stats(statistics, ylog=False, view=False, filename='avg_fitness.svg'): 12 | """ Plots the population's average and best fitness. """ 13 | if plt is None: 14 | warnings.warn("This display is not available due to a missing optional dependency (matplotlib)") 15 | return 16 | 17 | generation = range(len(statistics.most_fit_genomes)) 18 | best_fitness = [c.fitness for c in statistics.most_fit_genomes] 19 | avg_fitness = np.array(statistics.get_fitness_mean()) 20 | stdev_fitness = np.array(statistics.get_fitness_stdev()) 21 | 22 | plt.plot(generation, avg_fitness, 'b-', label="average") 23 | plt.plot(generation, avg_fitness - stdev_fitness, 'g-.', label="-1 sd") 24 | plt.plot(generation, avg_fitness + stdev_fitness, 'g-.', label="+1 sd") 25 | plt.plot(generation, best_fitness, 'r-', label="best") 26 | 27 | plt.title("Population's average and best fitness") 28 | plt.xlabel("Generations") 29 | plt.ylabel("Fitness") 30 | plt.grid() 31 | plt.legend(loc="best") 32 | if ylog: 33 | plt.gca().set_yscale('symlog') 34 | 35 | plt.savefig(filename) 36 | if view: 37 | plt.show() 38 | 39 | plt.close() 40 | 41 | 42 | def plot_spikes(spikes, view=False, filename=None, title=None): 43 | """ Plots the trains for a single spiking neuron. """ 44 | t_values = [t for t, I, v, u, f in spikes] 45 | v_values = [v for t, I, v, u, f in spikes] 46 | u_values = [u for t, I, v, u, f in spikes] 47 | I_values = [I for t, I, v, u, f in spikes] 48 | f_values = [f for t, I, v, u, f in spikes] 49 | 50 | fig = plt.figure() 51 | plt.subplot(4, 1, 1) 52 | plt.ylabel("Potential (mv)") 53 | plt.xlabel("Time (in ms)") 54 | plt.grid() 55 | plt.plot(t_values, v_values, "g-") 56 | 57 | if title is None: 58 | plt.title("Izhikevich's spiking neuron model") 59 | else: 60 | plt.title("Izhikevich's spiking neuron model ({0!s})".format(title)) 61 | 62 | plt.subplot(4, 1, 2) 63 | plt.ylabel("Fired") 64 | plt.xlabel("Time (in ms)") 65 | plt.grid() 66 | plt.plot(t_values, f_values, "r-") 67 | 68 | plt.subplot(4, 1, 3) 69 | plt.ylabel("Recovery (u)") 70 | plt.xlabel("Time (in ms)") 71 | plt.grid() 72 | plt.plot(t_values, u_values, "r-") 73 | 74 | plt.subplot(4, 1, 4) 75 | plt.ylabel("Current (I)") 76 | plt.xlabel("Time (in ms)") 77 | plt.grid() 78 | plt.plot(t_values, I_values, "r-o") 79 | 80 | if filename is not None: 81 | plt.savefig(filename) 82 | 83 | if view: 84 | plt.show() 85 | plt.close() 86 | fig = None 87 | 88 | return fig 89 | 90 | 91 | def plot_species(statistics, view=False, filename='speciation.svg'): 92 | """ Visualizes speciation throughout evolution. """ 93 | if plt is None: 94 | warnings.warn("This display is not available due to a missing optional dependency (matplotlib)") 95 | return 96 | 97 | species_sizes = statistics.get_species_sizes() 98 | num_generations = len(species_sizes) 99 | curves = np.array(species_sizes).T 100 | 101 | fig, ax = plt.subplots() 102 | ax.stackplot(range(num_generations), *curves) 103 | 104 | plt.title("Speciation") 105 | plt.ylabel("Size per Species") 106 | plt.xlabel("Generations") 107 | 108 | plt.savefig(filename) 109 | 110 | if view: 111 | plt.show() 112 | 113 | plt.close() 114 | 115 | 116 | def draw_net(config, genome, view=False, filename=None, node_names=None, show_disabled=True, prune_unused=False, 117 | node_colors=None, fmt='svg'): 118 | """ Receives a genome and draws a neural network with arbitrary topology. """ 119 | # Attributes for network nodes. 120 | if graphviz is None: 121 | warnings.warn("This display is not available due to a missing optional dependency (graphviz)") 122 | return 123 | 124 | if node_names is None: 125 | node_names = {} 126 | 127 | assert type(node_names) is dict 128 | 129 | if node_colors is None: 130 | node_colors = {} 131 | 132 | assert type(node_colors) is dict 133 | 134 | node_attrs = { 135 | 'shape': 'circle', 136 | 'fontsize': '9', 137 | 'height': '0.2', 138 | 'width': '0.2'} 139 | 140 | dot = graphviz.Digraph(format=fmt, node_attr=node_attrs) 141 | 142 | inputs = set() 143 | for k in config.genome_config.input_keys: 144 | inputs.add(k) 145 | name = node_names.get(k, str(k)) 146 | input_attrs = {'style': 'filled', 'shape': 'box', 'fillcolor': node_colors.get(k, 'lightgray')} 147 | dot.node(name, _attributes=input_attrs) 148 | 149 | outputs = set() 150 | for k in config.genome_config.output_keys: 151 | outputs.add(k) 152 | name = node_names.get(k, str(k)) 153 | node_attrs = {'style': 'filled', 'fillcolor': node_colors.get(k, 'lightblue')} 154 | 155 | dot.node(name, _attributes=node_attrs) 156 | 157 | if prune_unused: 158 | connections = set() 159 | for cg in genome.connections.values(): 160 | if cg.enabled or show_disabled: 161 | connections.add((cg.in_node_id, cg.out_node_id)) 162 | 163 | used_nodes = copy.copy(outputs) 164 | pending = copy.copy(outputs) 165 | while pending: 166 | new_pending = set() 167 | for a, b in connections: 168 | if b in pending and a not in used_nodes: 169 | new_pending.add(a) 170 | used_nodes.add(a) 171 | pending = new_pending 172 | else: 173 | used_nodes = set(genome.nodes.keys()) 174 | 175 | for n in used_nodes: 176 | if n in inputs or n in outputs: 177 | continue 178 | 179 | attrs = {'style': 'filled', 180 | 'fillcolor': node_colors.get(n, 'white')} 181 | dot.node(str(n), _attributes=attrs) 182 | 183 | for cg in genome.connections.values(): 184 | if cg.enabled or show_disabled: 185 | #if cg.input not in used_nodes or cg.output not in used_nodes: 186 | # continue 187 | input, output = cg.key 188 | a = node_names.get(input, str(input)) 189 | b = node_names.get(output, str(output)) 190 | style = 'solid' if cg.enabled else 'dotted' 191 | color = 'green' if cg.weight > 0 else 'red' 192 | width = str(0.1 + abs(cg.weight / 5.0)) 193 | dot.edge(a, b, _attributes={'style': style, 'color': color, 'penwidth': width}) 194 | 195 | dot.render(filename, view=view) 196 | 197 | return dot 198 | -------------------------------------------------------------------------------- /Neat/xor.py: -------------------------------------------------------------------------------- 1 | """ 2 | 2-input XOR example -- this is most likely the simplest possible example. 3 | """ 4 | 5 | from __future__ import print_function 6 | import os 7 | import neat 8 | import visualize 9 | 10 | # 2-input XOR inputs and expected outputs. 11 | xor_inputs = [(0.0, 0.0), (0.0, 1.0), (1.0, 0.0), (1.0, 1.0)] 12 | xor_outputs = [ (0.0,), (1.0,), (1.0,), (0.0,)] 13 | 14 | 15 | def eval_genomes(genomes, config): 16 | for genome_id, genome in genomes: 17 | genome.fitness = 4.0 18 | net = neat.nn.FeedForwardNetwork.create(genome, config) 19 | for xi, xo in zip(xor_inputs, xor_outputs): 20 | output = net.activate(xi) 21 | genome.fitness -= (output[0] - xo[0]) ** 2 22 | 23 | 24 | def run(config_file): 25 | # Load configuration. 26 | config = neat.Config(neat.DefaultGenome, neat.DefaultReproduction, 27 | neat.DefaultSpeciesSet, neat.DefaultStagnation, 28 | config_file) 29 | 30 | # Create the population, which is the top-level object for a NEAT run. 31 | p = neat.Population(config) 32 | 33 | # Add a stdout reporter to show progress in the terminal. 34 | p.add_reporter(neat.StdOutReporter(True)) 35 | stats = neat.StatisticsReporter() 36 | p.add_reporter(stats) 37 | p.add_reporter(neat.Checkpointer(5)) 38 | 39 | # Run for up to 300 generations. 40 | winner = p.run(eval_genomes, 300) 41 | 42 | # Display the winning genome. 43 | print('\nBest genome:\n{!s}'.format(winner)) 44 | 45 | # Show output of the most fit genome against training data. 46 | print('\nOutput:') 47 | winner_net = neat.nn.FeedForwardNetwork.create(winner, config) 48 | for xi, xo in zip(xor_inputs, xor_outputs): 49 | output = winner_net.activate(xi) 50 | print("input {!r}, expected output {!r}, got {!r}".format(xi, xo, output)) 51 | 52 | node_names = {-1:'A', -2: 'B', 0:'A XOR B'} 53 | visualize.draw_net(config, winner, True, node_names=node_names) 54 | visualize.plot_stats(stats, ylog=False, view=True) 55 | visualize.plot_species(stats, view=True) 56 | 57 | p = neat.Checkpointer.restore_checkpoint('neat-checkpoint-4') 58 | p.run(eval_genomes, 10) 59 | 60 | 61 | if __name__ == '__main__': 62 | # Determine path to configuration file. This path manipulation is 63 | # here so that the script will run successfully regardless of the 64 | # current working directory. 65 | local_dir = os.path.dirname(__file__) 66 | config_path = os.path.join(local_dir, 'config-feedforward') 67 | run(config_path) 68 | -------------------------------------------------------------------------------- /Python_Basis/.ipynb_checkpoints/Basis-checkpoint.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "## Python基础教学代码---基础篇 \n", 8 | "1.变量赋值 \n", 9 | "2.标准数据类型 \n", 10 | "3.数据转换 \n", 11 | "4.算数运算符 \n", 12 | "5.格式化\n", 13 | "***" 14 | ] 15 | }, 16 | { 17 | "cell_type": "markdown", 18 | "metadata": {}, 19 | "source": [ 20 | "## 1.变量赋值" 21 | ] 22 | }, 23 | { 24 | "cell_type": "code", 25 | "execution_count": 2, 26 | "metadata": {}, 27 | "outputs": [ 28 | { 29 | "name": "stdout", 30 | "output_type": "stream", 31 | "text": [ 32 | "1\n", 33 | "2 2\n" 34 | ] 35 | } 36 | ], 37 | "source": [ 38 | "a = 1 # 单变量赋值\n", 39 | "c = b = 2 # 多变量赋值\n", 40 | "print(a)\n", 41 | "print(b, c)" 42 | ] 43 | }, 44 | { 45 | "cell_type": "code", 46 | "execution_count": 4, 47 | "metadata": {}, 48 | "outputs": [ 49 | { 50 | "name": "stdout", 51 | "output_type": "stream", 52 | "text": [ 53 | "\n", 54 | "\n", 55 | "\n" 56 | ] 57 | } 58 | ], 59 | "source": [ 60 | "# 变量类型\n", 61 | "name = 'Chile' # 字符串\n", 62 | "miles = 1000.0 # 浮点型\n", 63 | "num = 100 # 整形\n", 64 | "# 打印变量类型\n", 65 | "print(type(name))\n", 66 | "print(type(miles))\n", 67 | "print(type(num))" 68 | ] 69 | }, 70 | { 71 | "cell_type": "markdown", 72 | "metadata": {}, 73 | "source": [ 74 | "***\n", 75 | "## 2.标准数据类型 \n", 76 | "\n", 77 | "Python有五个标准的数据类型： \n", 78 | "1.Numbers（数字） \n", 79 | "2.String（字符串） \n", 80 | "3.List（列表） \n", 81 | "4.Tuple（元组） \n", 82 | "5.Dictionary（字典）\n", 83 | "6.Set(集合)\n", 84 | "其中List，Tuple，Dictionary,Set可以放任意数据类型" 85 | ] 86 | }, 87 | { 88 | "cell_type": "markdown", 89 | "metadata": {}, 90 | "source": [ 91 | "### 数字" 92 | ] 93 | }, 94 | { 95 | "cell_type": "code", 96 | "execution_count": 5, 97 | "metadata": {}, 98 | "outputs": [], 99 | "source": [ 100 | "# Numbers: int & float\n", 101 | "a = 1 # int\n", 102 | "b = 1.0 # float" 103 | ] 104 | }, 105 | { 106 | "cell_type": "code", 107 | "execution_count": 6, 108 | "metadata": {}, 109 | "outputs": [ 110 | { 111 | "name": "stdout", 112 | "output_type": "stream", 113 | "text": [ 114 | "C\n", 115 | "hi\n", 116 | "ile\n", 117 | "e\n" 118 | ] 119 | } 120 | ], 121 | "source": [ 122 | "# String\n", 123 | "my_name = 'Chile'\n", 124 | "print(my_name[0]) # 打印第0个字符\n", 125 | "print(my_name[1: 3]) # 打印第1个到第2个的字符\n", 126 | "print(my_name[2:]) # 打印第2个到最后一个的字符\n", 127 | "print(my_name[-1]) # 打印倒数第一个字符" 128 | ] 129 | }, 130 | { 131 | "cell_type": "markdown", 132 | "metadata": {}, 133 | "source": [ 134 | "### 列表" 135 | ] 136 | }, 137 | { 138 | "cell_type": "code", 139 | "execution_count": 7, 140 | "metadata": {}, 141 | "outputs": [ 142 | { 143 | "name": "stdout", 144 | "output_type": "stream", 145 | "text": [ 146 | "[1, 2, 3, 4]\n", 147 | "['Chile', 'b', 'c']\n", 148 | "['a', 1, 1.0, [1, 2, 3, 4], ['Chile', 'b', 'c']]\n" 149 | ] 150 | } 151 | ], 152 | "source": [ 153 | "# List 可以放任意类型的数据类型\n", 154 | "num_list = [1, 2, 3, 4]\n", 155 | "str_list = ['Chile', 'b', 'c']\n", 156 | "mix_list = ['a', 1, 1.0, num_list, str_list]\n", 157 | "print(num_list)\n", 158 | "print(str_list)\n", 159 | "print(mix_list)" 160 | ] 161 | }, 162 | { 163 | "cell_type": "markdown", 164 | "metadata": {}, 165 | "source": [ 166 | "### 元组" 167 | ] 168 | }, 169 | { 170 | "cell_type": "code", 171 | "execution_count": 26, 172 | "metadata": {}, 173 | "outputs": [ 174 | { 175 | "name": "stdout", 176 | "output_type": "stream", 177 | "text": [ 178 | "1\n", 179 | "('chile', 111, 2.2, 'a', [1, 2, 3, 4])\n" 180 | ] 181 | }, 182 | { 183 | "ename": "TypeError", 184 | "evalue": "'tuple' object does not support item assignment", 185 | "output_type": "error", 186 | "traceback": [ 187 | "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", 188 | "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", 189 | "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmix_list\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmix_tuple\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0mmix_tuple\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m1\u001b[0m \u001b[0;31m# 不可赋值，否则报错\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", 190 | "\u001b[0;31mTypeError\u001b[0m: 'tuple' object does not support item assignment" 191 | ] 192 | } 193 | ], 194 | "source": [ 195 | "# Tuple 可以放任意类型的数据类型\n", 196 | "mix_tuple = ('chile', 111, 2.2, 'a', num_list) # 不可赋值\n", 197 | "print(mix_list[1])\n", 198 | "print(mix_tuple)\n", 199 | "mix_tuple[1] = 1 # 不可赋值，否则报错" 200 | ] 201 | }, 202 | { 203 | "cell_type": "markdown", 204 | "metadata": {}, 205 | "source": [ 206 | "### 字典" 207 | ] 208 | }, 209 | { 210 | "cell_type": "code", 211 | "execution_count": 9, 212 | "metadata": {}, 213 | "outputs": [ 214 | { 215 | "name": "stdout", 216 | "output_type": "stream", 217 | "text": [ 218 | "{'name': 'Chile', 'age': 18, 'num_list': [1, 2, 3, 4], 'tuple': ('chile', 111, 2.2, 'a', [1, 2, 3, 4])}\n", 219 | "dict_keys(['name', 'age', 'num_list', 'tuple'])\n", 220 | "dict_values(['Chile', 18, [1, 2, 3, 4], ('chile', 111, 2.2, 'a', [1, 2, 3, 4])])\n", 221 | "Chile\n", 222 | "[1, 2, 3, 4]\n", 223 | "('chile', 111, 2.2, 'a', [1, 2, 3, 4])\n" 224 | ] 225 | } 226 | ], 227 | "source": [ 228 | "# Dictionary 可以放任意类型的数据类型\n", 229 | "test_dict = {'name': 'Chile', 'age': 18, 'num_list': num_list, 'tuple': mix_tuple}\n", 230 | "print(test_dict)\n", 231 | "print(test_dict.keys()) # 打印键\n", 232 | "print(test_dict.values()) # 打印值\n", 233 | "print(test_dict['name'])\n", 234 | "print(test_dict['num_list'])\n", 235 | "print(test_dict['tuple'])" 236 | ] 237 | }, 238 | { 239 | "cell_type": "markdown", 240 | "metadata": {}, 241 | "source": [ 242 | "### 字典的赋值“陷阱”" 243 | ] 244 | }, 245 | { 246 | "cell_type": "code", 247 | "execution_count": 15, 248 | "metadata": {}, 249 | "outputs": [ 250 | { 251 | "name": "stdout", 252 | "output_type": "stream", 253 | "text": [ 254 | "alialili\n", 255 | "alialili\n" 256 | ] 257 | } 258 | ], 259 | "source": [ 260 | "# 直接字典赋值， 被赋值的字典的值改变，原字典也会改变\n", 261 | "test_dict_copy = test_dict\n", 262 | "test_dict_copy['name'] = 'alialili'\n", 263 | "print(test_dict['name']) \n", 264 | "print(test_dict_copy['name'])" 265 | ] 266 | }, 267 | { 268 | "cell_type": "code", 269 | "execution_count": 17, 270 | "metadata": {}, 271 | "outputs": [ 272 | { 273 | "name": "stdout", 274 | "output_type": "stream", 275 | "text": [ 276 | "alialili\n", 277 | "Mary\n" 278 | ] 279 | } 280 | ], 281 | "source": [ 282 | "# 使用深拷贝避免这种情况发生\n", 283 | "from copy import deepcopy\n", 284 | "test_dict_copy = deepcopy(test_dict)\n", 285 | "test_dict_copy['name'] = 'Mary'\n", 286 | "print(test_dict['name']) \n", 287 | "print(test_dict_copy['name'])" 288 | ] 289 | }, 290 | { 291 | "cell_type": "markdown", 292 | "metadata": {}, 293 | "source": [ 294 | "### 集合" 295 | ] 296 | }, 297 | { 298 | "cell_type": "code", 299 | "execution_count": 30, 300 | "metadata": {}, 301 | "outputs": [ 302 | { 303 | "name": "stdout", 304 | "output_type": "stream", 305 | "text": [ 306 | "{1, 'chile', '1', 'abc'}\n" 307 | ] 308 | } 309 | ], 310 | "source": [ 311 | "# 可以放任意类型的基础数据类型\n", 312 | "# Set 集合：与数学意义上的集合意义一致，集合内每一个值都是唯一的\n", 313 | "test_set = {'abc', 1, 1, '1', 'chile'} \n", 314 | "print(test_set) # 打印出来会只有一个数字1，因为集合的去重功能" 315 | ] 316 | }, 317 | { 318 | "cell_type": "markdown", 319 | "metadata": {}, 320 | "source": [ 321 | "***\n", 322 | "## 3.数据类型转换" 323 | ] 324 | }, 325 | { 326 | "cell_type": "code", 327 | "execution_count": 12, 328 | "metadata": {}, 329 | "outputs": [ 330 | { 331 | "name": "stdout", 332 | "output_type": "stream", 333 | "text": [ 334 | "\n", 335 | "\n", 336 | "\n", 337 | "\n", 338 | "---------------\n", 339 | "\n", 340 | "\n", 341 | "\n" 342 | ] 343 | } 344 | ], 345 | "source": [ 346 | "tr_a = '1'\n", 347 | "int_b = int(tr_a) # 字符串转数字\n", 348 | "str_c = str(int_b) # 数字转字符串\n", 349 | "float_d = float(str_c) # 字符串转浮点\n", 350 | "print(type(tr_a))\n", 351 | "print(type(int_b))\n", 352 | "print(type(str_c))\n", 353 | "print(type(float_d))\n", 354 | "print('---------------')\n", 355 | "tr_list = [1, 2, 3]\n", 356 | "set_a = set(tr_list) # 列表转集合\n", 357 | "list_b = list(set_a) # 集合转列表\n", 358 | "print(type(tr_list))\n", 359 | "print(type(set_a))\n", 360 | "print(type(list_b))" 361 | ] 362 | }, 363 | { 364 | "cell_type": "markdown", 365 | "metadata": {}, 366 | "source": [ 367 | "***\n", 368 | "## 4.算数运算符 " 369 | ] 370 | }, 371 | { 372 | "cell_type": "code", 373 | "execution_count": 23, 374 | "metadata": {}, 375 | "outputs": [ 376 | { 377 | "name": "stdout", 378 | "output_type": "stream", 379 | "text": [ 380 | "c: 1\n", 381 | "d: 8\n", 382 | "e: 8.0\n", 383 | "f: 0\n", 384 | "g: 1\n", 385 | "h: 8\n" 386 | ] 387 | } 388 | ], 389 | "source": [ 390 | "# 运算符\n", 391 | "a = 2\n", 392 | "b = a + 2\n", 393 | "c = a - 1\n", 394 | "d = a * b\n", 395 | "e = d / c\n", 396 | "f = d % c # 取余\n", 397 | "g = 3 // 2 # 整除(向下取整)\n", 398 | "h = 2**3 # 求幂\n", 399 | "print('c:', c)\n", 400 | "print('d:', d)\n", 401 | "print('e:', e)\n", 402 | "print('f:', f)\n", 403 | "print('g:', g)\n", 404 | "print('h:', h)" 405 | ] 406 | }, 407 | { 408 | "cell_type": "markdown", 409 | "metadata": {}, 410 | "source": [ 411 | "***\n", 412 | "## 5.格式化\n", 413 | "%s 代表字符串 \n", 414 | "%d 代表整数 \n", 415 | "%f 代表浮点 \n", 416 | "%.2f 代表保留小数点后两位" 417 | ] 418 | }, 419 | { 420 | "cell_type": "code", 421 | "execution_count": 25, 422 | "metadata": {}, 423 | "outputs": [ 424 | { 425 | "name": "stdout", 426 | "output_type": "stream", 427 | "text": [ 428 | "abc 1, dhfjdhfhdh, Chile, sjdhsjhdhs, skdjskjsk1.000000,sdjsdhs\n", 429 | "abc 1, dhfjdhfhdh, Chile, sjdhsjhdhs, skdjskjsk1.00,sdjsdhs\n" 430 | ] 431 | } 432 | ], 433 | "source": [ 434 | "# 格式化\n", 435 | "print('abc %d, dhfjdhfhdh, %s, sjdhsjhdhs, skdjskjsk%f,sdjsdhs' % (1, 'Chile', 1.0))\n", 436 | "# %.2f保留小数点\n", 437 | "print('abc %d, dhfjdhfhdh, %s, sjdhsjhdhs, skdjskjsk%.2f,sdjsdhs' % (1, 'Chile', 1.0))" 438 | ] 439 | } 440 | ], 441 | "metadata": { 442 | "kernelspec": { 443 | "display_name": "Python 3", 444 | "language": "python", 445 | "name": "python3" 446 | }, 447 | "language_info": { 448 | "codemirror_mode": { 449 | "name": "ipython", 450 | "version": 3 451 | }, 452 | "file_extension": ".py", 453 | "mimetype": "text/x-python", 454 | "name": "python", 455 | "nbconvert_exporter": "python", 456 | "pygments_lexer": "ipython3", 457 | "version": "3.7.3" 458 | } 459 | }, 460 | "nbformat": 4, 461 | "nbformat_minor": 2 462 | } 463 | -------------------------------------------------------------------------------- /Python_Basis/.ipynb_checkpoints/Basis_Advance-checkpoint.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [], 3 | "metadata": {}, 4 | "nbformat": 4, 5 | "nbformat_minor": 2 6 | } 7 | -------------------------------------------------------------------------------- /Python_Basis/.ipynb_checkpoints/Basis_high_ranking-checkpoint.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [], 3 | "metadata": {}, 4 | "nbformat": 4, 5 | "nbformat_minor": 2 6 | } 7 | -------------------------------------------------------------------------------- /Python_Basis/Basis.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "# Python基础教学代码---基础篇 \n", 8 | "1.变量赋值 \n", 9 | "2.标准数据类型 \n", 10 | "3.数据转换 \n", 11 | "4.算数运算符 \n", 12 | "5.格式化\n", 13 | "***" 14 | ] 15 | }, 16 | { 17 | "cell_type": "markdown", 18 | "metadata": {}, 19 | "source": [ 20 | "## 1.变量赋值" 21 | ] 22 | }, 23 | { 24 | "cell_type": "code", 25 | "execution_count": 2, 26 | "metadata": {}, 27 | "outputs": [ 28 | { 29 | "name": "stdout", 30 | "output_type": "stream", 31 | "text": [ 32 | "1\n", 33 | "2 2\n" 34 | ] 35 | } 36 | ], 37 | "source": [ 38 | "a = 1 # 单变量赋值\n", 39 | "c = b = 2 # 多变量赋值\n", 40 | "print(a)\n", 41 | "print(b, c)" 42 | ] 43 | }, 44 | { 45 | "cell_type": "code", 46 | "execution_count": 4, 47 | "metadata": {}, 48 | "outputs": [ 49 | { 50 | "name": "stdout", 51 | "output_type": "stream", 52 | "text": [ 53 | "\n", 54 | "\n", 55 | "\n" 56 | ] 57 | } 58 | ], 59 | "source": [ 60 | "# 变量类型\n", 61 | "name = 'Chile' # 字符串\n", 62 | "miles = 1000.0 # 浮点型\n", 63 | "num = 100 # 整形\n", 64 | "# 打印变量类型\n", 65 | "print(type(name))\n", 66 | "print(type(miles))\n", 67 | "print(type(num))" 68 | ] 69 | }, 70 | { 71 | "cell_type": "markdown", 72 | "metadata": {}, 73 | "source": [ 74 | "***\n", 75 | "## 2.标准数据类型 \n", 76 | "\n", 77 | "Python有6个标准的数据类型： \n", 78 | "1.Numbers（数字） \n", 79 | "2.String（字符串） \n", 80 | "3.List（列表） \n", 81 | "4.Tuple（元组） \n", 82 | "5.Dictionary（字典） \n", 83 | "6.Set(集合) \n", 84 | "其中List，Tuple，Dictionary,Set可以放任意数据类型" 85 | ] 86 | }, 87 | { 88 | "cell_type": "markdown", 89 | "metadata": {}, 90 | "source": [ 91 | "### 数字" 92 | ] 93 | }, 94 | { 95 | "cell_type": "code", 96 | "execution_count": 5, 97 | "metadata": {}, 98 | "outputs": [], 99 | "source": [ 100 | "# Numbers: int & float\n", 101 | "a = 1 # int\n", 102 | "b = 1.0 # float" 103 | ] 104 | }, 105 | { 106 | "cell_type": "code", 107 | "execution_count": 6, 108 | "metadata": {}, 109 | "outputs": [ 110 | { 111 | "name": "stdout", 112 | "output_type": "stream", 113 | "text": [ 114 | "C\n", 115 | "hi\n", 116 | "ile\n", 117 | "e\n" 118 | ] 119 | } 120 | ], 121 | "source": [ 122 | "# String\n", 123 | "my_name = 'Chile'\n", 124 | "print(my_name[0]) # 打印第0个字符\n", 125 | "print(my_name[1: 3]) # 打印第1个到第2个的字符\n", 126 | "print(my_name[2:]) # 打印第2个到最后一个的字符\n", 127 | "print(my_name[-1]) # 打印倒数第一个字符" 128 | ] 129 | }, 130 | { 131 | "cell_type": "markdown", 132 | "metadata": {}, 133 | "source": [ 134 | "### 列表" 135 | ] 136 | }, 137 | { 138 | "cell_type": "code", 139 | "execution_count": 7, 140 | "metadata": {}, 141 | "outputs": [ 142 | { 143 | "name": "stdout", 144 | "output_type": "stream", 145 | "text": [ 146 | "[1, 2, 3, 4]\n", 147 | "['Chile', 'b', 'c']\n", 148 | "['a', 1, 1.0, [1, 2, 3, 4], ['Chile', 'b', 'c']]\n" 149 | ] 150 | } 151 | ], 152 | "source": [ 153 | "# List 可以放任意类型的数据类型\n", 154 | "num_list = [1, 2, 3, 4]\n", 155 | "str_list = ['Chile', 'b', 'c']\n", 156 | "mix_list = ['a', 1, 1.0, num_list, str_list]\n", 157 | "print(num_list)\n", 158 | "print(str_list)\n", 159 | "print(mix_list)" 160 | ] 161 | }, 162 | { 163 | "cell_type": "markdown", 164 | "metadata": {}, 165 | "source": [ 166 | "### 元组" 167 | ] 168 | }, 169 | { 170 | "cell_type": "code", 171 | "execution_count": 26, 172 | "metadata": {}, 173 | "outputs": [ 174 | { 175 | "name": "stdout", 176 | "output_type": "stream", 177 | "text": [ 178 | "1\n", 179 | "('chile', 111, 2.2, 'a', [1, 2, 3, 4])\n" 180 | ] 181 | }, 182 | { 183 | "ename": "TypeError", 184 | "evalue": "'tuple' object does not support item assignment", 185 | "output_type": "error", 186 | "traceback": [ 187 | "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", 188 | "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", 189 | "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmix_list\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmix_tuple\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0mmix_tuple\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m1\u001b[0m \u001b[0;31m# 不可赋值，否则报错\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", 190 | "\u001b[0;31mTypeError\u001b[0m: 'tuple' object does not support item assignment" 191 | ] 192 | } 193 | ], 194 | "source": [ 195 | "# Tuple 可以放任意类型的数据类型\n", 196 | "mix_tuple = ('chile', 111, 2.2, 'a', num_list) # 不可赋值\n", 197 | "print(mix_list[1])\n", 198 | "print(mix_tuple)\n", 199 | "mix_tuple[1] = 1 # 不可赋值，否则报错" 200 | ] 201 | }, 202 | { 203 | "cell_type": "markdown", 204 | "metadata": {}, 205 | "source": [ 206 | "### 字典" 207 | ] 208 | }, 209 | { 210 | "cell_type": "code", 211 | "execution_count": 9, 212 | "metadata": {}, 213 | "outputs": [ 214 | { 215 | "name": "stdout", 216 | "output_type": "stream", 217 | "text": [ 218 | "{'name': 'Chile', 'age': 18, 'num_list': [1, 2, 3, 4], 'tuple': ('chile', 111, 2.2, 'a', [1, 2, 3, 4])}\n", 219 | "dict_keys(['name', 'age', 'num_list', 'tuple'])\n", 220 | "dict_values(['Chile', 18, [1, 2, 3, 4], ('chile', 111, 2.2, 'a', [1, 2, 3, 4])])\n", 221 | "Chile\n", 222 | "[1, 2, 3, 4]\n", 223 | "('chile', 111, 2.2, 'a', [1, 2, 3, 4])\n" 224 | ] 225 | } 226 | ], 227 | "source": [ 228 | "# Dictionary 可以放任意类型的数据类型\n", 229 | "test_dict = {'name': 'Chile', 'age': 18, 'num_list': num_list, 'tuple': mix_tuple}\n", 230 | "print(test_dict)\n", 231 | "print(test_dict.keys()) # 打印键\n", 232 | "print(test_dict.values()) # 打印值\n", 233 | "print(test_dict['name'])\n", 234 | "print(test_dict['num_list'])\n", 235 | "print(test_dict['tuple'])" 236 | ] 237 | }, 238 | { 239 | "cell_type": "markdown", 240 | "metadata": {}, 241 | "source": [ 242 | "### 字典的赋值“陷阱”" 243 | ] 244 | }, 245 | { 246 | "cell_type": "code", 247 | "execution_count": 15, 248 | "metadata": {}, 249 | "outputs": [ 250 | { 251 | "name": "stdout", 252 | "output_type": "stream", 253 | "text": [ 254 | "alialili\n", 255 | "alialili\n" 256 | ] 257 | } 258 | ], 259 | "source": [ 260 | "# 直接字典赋值， 被赋值的字典的值改变，原字典也会改变\n", 261 | "test_dict_copy = test_dict\n", 262 | "test_dict_copy['name'] = 'alialili'\n", 263 | "print(test_dict['name']) \n", 264 | "print(test_dict_copy['name'])" 265 | ] 266 | }, 267 | { 268 | "cell_type": "code", 269 | "execution_count": 17, 270 | "metadata": {}, 271 | "outputs": [ 272 | { 273 | "name": "stdout", 274 | "output_type": "stream", 275 | "text": [ 276 | "alialili\n", 277 | "Mary\n" 278 | ] 279 | } 280 | ], 281 | "source": [ 282 | "# 使用深拷贝避免这种情况发生\n", 283 | "from copy import deepcopy\n", 284 | "test_dict_copy = deepcopy(test_dict)\n", 285 | "test_dict_copy['name'] = 'Mary'\n", 286 | "print(test_dict['name']) \n", 287 | "print(test_dict_copy['name'])" 288 | ] 289 | }, 290 | { 291 | "cell_type": "markdown", 292 | "metadata": {}, 293 | "source": [ 294 | "### 集合" 295 | ] 296 | }, 297 | { 298 | "cell_type": "code", 299 | "execution_count": 30, 300 | "metadata": {}, 301 | "outputs": [ 302 | { 303 | "name": "stdout", 304 | "output_type": "stream", 305 | "text": [ 306 | "{1, 'chile', '1', 'abc'}\n" 307 | ] 308 | } 309 | ], 310 | "source": [ 311 | "# 可以放任意类型的基础数据类型\n", 312 | "# Set 集合：与数学意义上的集合意义一致，集合内每一个值都是唯一的\n", 313 | "test_set = {'abc', 1, 1, '1', 'chile'} \n", 314 | "print(test_set) # 打印出来会只有一个数字1，因为集合的去重功能" 315 | ] 316 | }, 317 | { 318 | "cell_type": "markdown", 319 | "metadata": {}, 320 | "source": [ 321 | "***\n", 322 | "## 3.数据类型转换" 323 | ] 324 | }, 325 | { 326 | "cell_type": "code", 327 | "execution_count": 12, 328 | "metadata": {}, 329 | "outputs": [ 330 | { 331 | "name": "stdout", 332 | "output_type": "stream", 333 | "text": [ 334 | "\n", 335 | "\n", 336 | "\n", 337 | "\n", 338 | "---------------\n", 339 | "\n", 340 | "\n", 341 | "\n" 342 | ] 343 | } 344 | ], 345 | "source": [ 346 | "tr_a = '1'\n", 347 | "int_b = int(tr_a) # 字符串转数字\n", 348 | "str_c = str(int_b) # 数字转字符串\n", 349 | "float_d = float(str_c) # 字符串转浮点\n", 350 | "print(type(tr_a))\n", 351 | "print(type(int_b))\n", 352 | "print(type(str_c))\n", 353 | "print(type(float_d))\n", 354 | "print('---------------')\n", 355 | "tr_list = [1, 2, 3]\n", 356 | "set_a = set(tr_list) # 列表转集合\n", 357 | "list_b = list(set_a) # 集合转列表\n", 358 | "print(type(tr_list))\n", 359 | "print(type(set_a))\n", 360 | "print(type(list_b))" 361 | ] 362 | }, 363 | { 364 | "cell_type": "markdown", 365 | "metadata": {}, 366 | "source": [ 367 | "***\n", 368 | "## 4.算数运算符 " 369 | ] 370 | }, 371 | { 372 | "cell_type": "code", 373 | "execution_count": 23, 374 | "metadata": {}, 375 | "outputs": [ 376 | { 377 | "name": "stdout", 378 | "output_type": "stream", 379 | "text": [ 380 | "c: 1\n", 381 | "d: 8\n", 382 | "e: 8.0\n", 383 | "f: 0\n", 384 | "g: 1\n", 385 | "h: 8\n" 386 | ] 387 | } 388 | ], 389 | "source": [ 390 | "# 运算符\n", 391 | "a = 2\n", 392 | "b = a + 2\n", 393 | "c = a - 1\n", 394 | "d = a * b\n", 395 | "e = d / c\n", 396 | "f = d % c # 取余\n", 397 | "g = 3 // 2 # 整除(向下取整)\n", 398 | "h = 2**3 # 求幂\n", 399 | "print('c:', c)\n", 400 | "print('d:', d)\n", 401 | "print('e:', e)\n", 402 | "print('f:', f)\n", 403 | "print('g:', g)\n", 404 | "print('h:', h)" 405 | ] 406 | }, 407 | { 408 | "cell_type": "markdown", 409 | "metadata": {}, 410 | "source": [ 411 | "***\n", 412 | "## 5.格式化\n", 413 | "%s 代表字符串 \n", 414 | "%d 代表整数 \n", 415 | "%f 代表浮点 \n", 416 | "%.2f 代表保留小数点后两位" 417 | ] 418 | }, 419 | { 420 | "cell_type": "code", 421 | "execution_count": 25, 422 | "metadata": {}, 423 | "outputs": [ 424 | { 425 | "name": "stdout", 426 | "output_type": "stream", 427 | "text": [ 428 | "abc 1, dhfjdhfhdh, Chile, sjdhsjhdhs, skdjskjsk1.000000,sdjsdhs\n", 429 | "abc 1, dhfjdhfhdh, Chile, sjdhsjhdhs, skdjskjsk1.00,sdjsdhs\n" 430 | ] 431 | } 432 | ], 433 | "source": [ 434 | "# 格式化\n", 435 | "print('abc %d, dhfjdhfhdh, %s, sjdhsjhdhs, skdjskjsk%f,sdjsdhs' % (1, 'Chile', 1.0))\n", 436 | "# %.2f保留小数点\n", 437 | "print('abc %d, dhfjdhfhdh, %s, sjdhsjhdhs, skdjskjsk%.2f,sdjsdhs' % (1, 'Chile', 1.0))" 438 | ] 439 | } 440 | ], 441 | "metadata": { 442 | "kernelspec": { 443 | "display_name": "Python 3", 444 | "language": "python", 445 | "name": "python3" 446 | }, 447 | "language_info": { 448 | "codemirror_mode": { 449 | "name": "ipython", 450 | "version": 3 451 | }, 452 | "file_extension": ".py", 453 | "mimetype": "text/x-python", 454 | "name": "python", 455 | "nbconvert_exporter": "python", 456 | "pygments_lexer": "ipython3", 457 | "version": "3.7.3" 458 | } 459 | }, 460 | "nbformat": 4, 461 | "nbformat_minor": 2 462 | } 463 | -------------------------------------------------------------------------------- /Python_Basis/Basis_Advance.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "# Python基础教学代码---进阶篇 \n", 8 | "1.循环 \n", 9 | "2.条件语句:if \n", 10 | "3.文件I/O \n", 11 | "4.异常处理 \n", 12 | "5.导包\n", 13 | "***" 14 | ] 15 | }, 16 | { 17 | "cell_type": "markdown", 18 | "metadata": {}, 19 | "source": [ 20 | "## 1.循环 \n", 21 | "两种循环： \n", 22 | "1.1for 循环 \n", 23 | "1.2while 循环 " 24 | ] 25 | }, 26 | { 27 | "cell_type": "markdown", 28 | "metadata": {}, 29 | "source": [ 30 | "### for 循环打印List（列表）" 31 | ] 32 | }, 33 | { 34 | "cell_type": "code", 35 | "execution_count": 5, 36 | "metadata": {}, 37 | "outputs": [ 38 | { 39 | "name": "stdout", 40 | "output_type": "stream", 41 | "text": [ 42 | "第1种循环取值方式:直接取值\n", 43 | "Chile\n", 44 | "b\n", 45 | "c\n", 46 | "--------------------------\n", 47 | "第2种循环取值方式:索引取值\n", 48 | "Chile\n", 49 | "b\n", 50 | "c\n" 51 | ] 52 | } 53 | ], 54 | "source": [ 55 | "str_list = ['Chile', 'b', 'c']\n", 56 | "\n", 57 | "print('第1种循环取值方式:直接取值')\n", 58 | "for sub_str in str_list:\n", 59 | " print(sub_str)\n", 60 | "\n", 61 | "print('--------------------------')\n", 62 | "print('第2种循环取值方式:索引取值') \n", 63 | "for i in range(len(str_list)):\n", 64 | " print(str_list[i])" 65 | ] 66 | }, 67 | { 68 | "cell_type": "markdown", 69 | "metadata": {}, 70 | "source": [ 71 | "### while 循环打印List（列表）" 72 | ] 73 | }, 74 | { 75 | "cell_type": "code", 76 | "execution_count": 8, 77 | "metadata": {}, 78 | "outputs": [ 79 | { 80 | "name": "stdout", 81 | "output_type": "stream", 82 | "text": [ 83 | "Chile\n", 84 | "b\n", 85 | "c\n" 86 | ] 87 | } 88 | ], 89 | "source": [ 90 | "str_list = ['Chile', 'b', 'c']\n", 91 | "i = 0\n", 92 | "while i < len(str_list):\n", 93 | " print(str_list[i])\n", 94 | " i += 1\n" 95 | ] 96 | }, 97 | { 98 | "cell_type": "markdown", 99 | "metadata": {}, 100 | "source": [ 101 | "### for 循环打印Tuple（元组）" 102 | ] 103 | }, 104 | { 105 | "cell_type": "code", 106 | "execution_count": 10, 107 | "metadata": {}, 108 | "outputs": [ 109 | { 110 | "name": "stdout", 111 | "output_type": "stream", 112 | "text": [ 113 | "第1种循环取值方式:直接取值\n", 114 | "chile\n", 115 | "111\n", 116 | "2.2\n", 117 | "a\n", 118 | "['Chile', 'b', 'c']\n", 119 | "--------------------------\n", 120 | "第2种循环取值方式:索引取值\n", 121 | "chile\n", 122 | "111\n", 123 | "2.2\n", 124 | "a\n", 125 | "['Chile', 'b', 'c']\n" 126 | ] 127 | } 128 | ], 129 | "source": [ 130 | "str_list = ['Chile', 'b', 'c']\n", 131 | "mix_tuple = ('chile', 111, 2.2, 'a', str_list) # 不可赋值\n", 132 | "print('第1种循环取值方式:直接取值')\n", 133 | "for sub_tuple in mix_tuple:\n", 134 | " print(sub_tuple)\n", 135 | "\n", 136 | "print('--------------------------')\n", 137 | "print('第2种循环取值方式:索引取值') \n", 138 | "for i in range(len(mix_tuple)):\n", 139 | " print(mix_tuple[i])" 140 | ] 141 | }, 142 | { 143 | "cell_type": "markdown", 144 | "metadata": {}, 145 | "source": [ 146 | "### while 循环打印Tuple（元组）" 147 | ] 148 | }, 149 | { 150 | "cell_type": "code", 151 | "execution_count": 13, 152 | "metadata": {}, 153 | "outputs": [ 154 | { 155 | "name": "stdout", 156 | "output_type": "stream", 157 | "text": [ 158 | "chile\n", 159 | "111\n", 160 | "2.2\n", 161 | "a\n", 162 | "['Chile', 'b', 'c']\n" 163 | ] 164 | } 165 | ], 166 | "source": [ 167 | "str_list = ['Chile', 'b', 'c']\n", 168 | "mix_tuple = ('chile', 111, 2.2, 'a', str_list) # 不可赋值\n", 169 | "i = 0\n", 170 | "while i < len(mix_tuple):\n", 171 | " print(mix_tuple[i])\n", 172 | " i += 1" 173 | ] 174 | }, 175 | { 176 | "cell_type": "markdown", 177 | "metadata": {}, 178 | "source": [ 179 | "### for 循环打印Dictionary（字典）" 180 | ] 181 | }, 182 | { 183 | "cell_type": "code", 184 | "execution_count": 15, 185 | "metadata": {}, 186 | "outputs": [ 187 | { 188 | "name": "stdout", 189 | "output_type": "stream", 190 | "text": [ 191 | "key: name\n", 192 | "value: Chile\n", 193 | "-------------\n", 194 | "key: age\n", 195 | "value: 18\n", 196 | "-------------\n", 197 | "key: num_list\n", 198 | "value: [1, 2, 3, 4]\n", 199 | "-------------\n", 200 | "key: tuple\n", 201 | "value: ('chile', 111, 2.2, 'a', ['Chile', 'b', 'c'])\n", 202 | "-------------\n" 203 | ] 204 | } 205 | ], 206 | "source": [ 207 | "str_list = ['Chile', 'b', 'c']\n", 208 | "mix_tuple = ('chile', 111, 2.2, 'a', str_list) # 不可赋值\n", 209 | "num_list = [1, 2, 3, 4]\n", 210 | "test_dict = {'name': 'Chile', 'age': 18, 'num_list': num_list, 'tuple': mix_tuple}\n", 211 | "for key in test_dict.keys(): # 键值对打印法\n", 212 | " print('key:', key)\n", 213 | " print('value:', test_dict[key])\n", 214 | " print('-------------')\n", 215 | " " 216 | ] 217 | }, 218 | { 219 | "cell_type": "markdown", 220 | "metadata": {}, 221 | "source": [ 222 | "### for 循环打印Set（集合）" 223 | ] 224 | }, 225 | { 226 | "cell_type": "code", 227 | "execution_count": 22, 228 | "metadata": {}, 229 | "outputs": [ 230 | { 231 | "name": "stdout", 232 | "output_type": "stream", 233 | "text": [ 234 | "abc \n", 235 | "1 \n", 236 | "1 \n", 237 | "chile \n" 238 | ] 239 | } 240 | ], 241 | "source": [ 242 | "test_set = {'abc', 1, 1, '1', 'chile'} \n", 243 | "for value in test_set:\n", 244 | " print(value, ' ', type(value))" 245 | ] 246 | }, 247 | { 248 | "cell_type": "markdown", 249 | "metadata": {}, 250 | "source": [ 251 | "## 2.条件语句:if\n", 252 | "2.1 == : 恒等符号 \n", 253 | "2.2 != : 不等符号 \n", 254 | "2.3 > :大于号 \n", 255 | "2.4 < :小于号 \n", 256 | "2.5 >=:大于等于号 \n", 257 | "2.6 <=:小于等于号 \n", 258 | "2.7 and:与 \n", 259 | "2.8 or:或 \n", 260 | "2.9 not:非 " 261 | ] 262 | }, 263 | { 264 | "cell_type": "code", 265 | "execution_count": 16, 266 | "metadata": {}, 267 | "outputs": [ 268 | { 269 | "name": "stdout", 270 | "output_type": "stream", 271 | "text": [ 272 | "a != b\n" 273 | ] 274 | } 275 | ], 276 | "source": [ 277 | "a = 1 # 数字\n", 278 | "b = '1' # 字符串\n", 279 | "if a == b:\n", 280 | " print('a == b')\n", 281 | "else:\n", 282 | " print('a != b')" 283 | ] 284 | }, 285 | { 286 | "cell_type": "code", 287 | "execution_count": 17, 288 | "metadata": {}, 289 | "outputs": [ 290 | { 291 | "name": "stdout", 292 | "output_type": "stream", 293 | "text": [ 294 | "a < b\n" 295 | ] 296 | } 297 | ], 298 | "source": [ 299 | "a = 1\n", 300 | "b = 2\n", 301 | "if a > b:\n", 302 | " print('a > b')\n", 303 | "elif a < b:\n", 304 | " print('a < b')\n", 305 | "else:\n", 306 | " print('a == b')" 307 | ] 308 | }, 309 | { 310 | "cell_type": "code", 311 | "execution_count": 23, 312 | "metadata": {}, 313 | "outputs": [ 314 | { 315 | "name": "stdout", 316 | "output_type": "stream", 317 | "text": [ 318 | "False\n", 319 | "True\n", 320 | "True\n" 321 | ] 322 | } 323 | ], 324 | "source": [ 325 | "a = True\n", 326 | "b = False\n", 327 | "if a and b:\n", 328 | " print('True')\n", 329 | "else:\n", 330 | " print('False')\n", 331 | " \n", 332 | "if a or b:\n", 333 | " print('True')\n", 334 | "else:\n", 335 | " print('False')\n", 336 | "\n", 337 | "if a and (not b):\n", 338 | " print('True')\n", 339 | "else:\n", 340 | " print('False')\n", 341 | " " 342 | ] 343 | }, 344 | { 345 | "cell_type": "markdown", 346 | "metadata": {}, 347 | "source": [ 348 | "# 3.文件I/O" 349 | ] 350 | }, 351 | { 352 | "cell_type": "markdown", 353 | "metadata": {}, 354 | "source": [ 355 | "权限： \n", 356 | "w: 写权限 \n", 357 | "r：读权限 \n", 358 | "a：在原有文本的基础上追加文本的权限 \n", 359 | "\n", 360 | "互联网上的文件有非常多的格式，这里只是举个例子让大家感受下。至于更多格式的读写， \n", 361 | "小伙伴们可以通过互联网去搜索，Python是兼容很多文件格式的读写，代码风格都差不多。" 362 | ] 363 | }, 364 | { 365 | "cell_type": "code", 366 | "execution_count": 17, 367 | "metadata": {}, 368 | "outputs": [], 369 | "source": [ 370 | "with open('text.txt', 'w') as fw: # 只有文件名，默认文件在统计目录\n", 371 | " string = 'I am chile!'\n", 372 | " for i in range(5):\n", 373 | " fw.write(string + '\\n') " 374 | ] 375 | }, 376 | { 377 | "cell_type": "code", 378 | "execution_count": 7, 379 | "metadata": {}, 380 | "outputs": [ 381 | { 382 | "name": "stdout", 383 | "output_type": "stream", 384 | "text": [ 385 | "I am chile!\n", 386 | "\n", 387 | "I am chile!\n", 388 | "\n", 389 | "I am chile!\n", 390 | "\n", 391 | "I am chile!\n", 392 | "\n", 393 | "I am chile!\n", 394 | "\n" 395 | ] 396 | } 397 | ], 398 | "source": [ 399 | "with open('text.txt', 'r') as fr:\n", 400 | " for line in fr:\n", 401 | " print(line)" 402 | ] 403 | }, 404 | { 405 | "cell_type": "code", 406 | "execution_count": 8, 407 | "metadata": {}, 408 | "outputs": [], 409 | "source": [ 410 | "with open('text.txt', 'a') as fw:\n", 411 | " string = 'You are handsome!'\n", 412 | " for i in range(5):\n", 413 | " fw.write(string + '\\n') " 414 | ] 415 | }, 416 | { 417 | "cell_type": "code", 418 | "execution_count": 9, 419 | "metadata": {}, 420 | "outputs": [ 421 | { 422 | "name": "stdout", 423 | "output_type": "stream", 424 | "text": [ 425 | "I am chile!\n", 426 | "\n", 427 | "I am chile!\n", 428 | "\n", 429 | "I am chile!\n", 430 | "\n", 431 | "I am chile!\n", 432 | "\n", 433 | "I am chile!\n", 434 | "\n", 435 | "You are handsome!\n", 436 | "\n", 437 | "You are handsome!\n", 438 | "\n", 439 | "You are handsome!\n", 440 | "\n", 441 | "You are handsome!\n", 442 | "\n", 443 | "You are handsome!\n", 444 | "\n" 445 | ] 446 | } 447 | ], 448 | "source": [ 449 | "with open('text.txt', 'r') as fr:\n", 450 | " for line in fr:\n", 451 | " print(line) " 452 | ] 453 | }, 454 | { 455 | "cell_type": "markdown", 456 | "metadata": {}, 457 | "source": [ 458 | "# 4.异常\n", 459 | "\n", 460 | "try: \n", 461 | " 执行正常代码。 \n", 462 | " \n", 463 | "except: \n", 464 | " 发生异常，执行此处代码 \n", 465 | " \n", 466 | "else:（这段代码可不加） \n", 467 | " 无异常，则执行此处代码 \n" 468 | ] 469 | }, 470 | { 471 | "cell_type": "code", 472 | "execution_count": 12, 473 | "metadata": {}, 474 | "outputs": [ 475 | { 476 | "name": "stdout", 477 | "output_type": "stream", 478 | "text": [ 479 | "The file does not exist!\n" 480 | ] 481 | } 482 | ], 483 | "source": [ 484 | "try:\n", 485 | " with open('txr.txt', 'r') as fr:\n", 486 | " text = fr.read()\n", 487 | "except IOError:\n", 488 | " print('The file does not exist!')\n", 489 | "\n", 490 | "else:\n", 491 | " print('Succeed')\n" 492 | ] 493 | }, 494 | { 495 | "cell_type": "markdown", 496 | "metadata": {}, 497 | "source": [ 498 | "## 异常处理的目的 \n", 499 | "小伙伴们一开始写Python的时候可能并不需要的异常处理机制，因为我们的代码简洁又高效，不过这并不代表你永远不需要。 \n", 500 | "现代软件是非常庞大的，而代码又是人写的，难免会出错，你不知道一个大型软件在运行过程中会在什么时候出现一个bug，这时候 \n", 501 | "异常处理机制就能让你快速定位自己软件的bug，缩短我们调试的时间，这就是异常处理机制的用途。" 502 | ] 503 | }, 504 | { 505 | "cell_type": "markdown", 506 | "metadata": {}, 507 | "source": [ 508 | "# 5.导包 \n", 509 | "1.本地包 \n", 510 | "2.系统包 \n", 511 | " \n", 512 | "导包指令： \n", 513 | "1.import \n", 514 | "2.from ... import ..." 515 | ] 516 | }, 517 | { 518 | "cell_type": "markdown", 519 | "metadata": {}, 520 | "source": [ 521 | "## 导入本地包" 522 | ] 523 | }, 524 | { 525 | "cell_type": "code", 526 | "execution_count": 13, 527 | "metadata": {}, 528 | "outputs": [ 529 | { 530 | "name": "stdout", 531 | "output_type": "stream", 532 | "text": [ 533 | "hello!\n" 534 | ] 535 | } 536 | ], 537 | "source": [ 538 | "from test import hello\n", 539 | "hello()" 540 | ] 541 | }, 542 | { 543 | "cell_type": "code", 544 | "execution_count": 14, 545 | "metadata": {}, 546 | "outputs": [ 547 | { 548 | "name": "stdout", 549 | "output_type": "stream", 550 | "text": [ 551 | "hello!\n" 552 | ] 553 | } 554 | ], 555 | "source": [ 556 | "import test\n", 557 | "test.hello()" 558 | ] 559 | }, 560 | { 561 | "cell_type": "markdown", 562 | "metadata": {}, 563 | "source": [ 564 | "## 导入系统包" 565 | ] 566 | }, 567 | { 568 | "cell_type": "code", 569 | "execution_count": 16, 570 | "metadata": {}, 571 | "outputs": [ 572 | { 573 | "name": "stdout", 574 | "output_type": "stream", 575 | "text": [ 576 | "2019-08-21 09:43:07\n" 577 | ] 578 | } 579 | ], 580 | "source": [ 581 | "import time # 引入时间模块\n", 582 | "# 格式化成year-month-day hour:min:sec形式\n", 583 | "print (time.strftime(\"%Y-%m-%d %H:%M:%S\", time.localtime())) " 584 | ] 585 | }, 586 | { 587 | "cell_type": "markdown", 588 | "metadata": {}, 589 | "source": [ 590 | "## 导包的目的 \n", 591 | "Python之所以被称为胶水语言，是因为它有很多优秀的第三方包，让我们在编程过程中只关注任务的解决，而不拘泥于代码的繁琐， \n", 592 | "提升代码的复用率，加快编程速度。因此，导包是Python不可或缺的重要技能。" 593 | ] 594 | } 595 | ], 596 | "metadata": { 597 | "kernelspec": { 598 | "display_name": "Python 3", 599 | "language": "python", 600 | "name": "python3" 601 | }, 602 | "language_info": { 603 | "codemirror_mode": { 604 | "name": "ipython", 605 | "version": 3 606 | }, 607 | "file_extension": ".py", 608 | "mimetype": "text/x-python", 609 | "name": "python", 610 | "nbconvert_exporter": "python", 611 | "pygments_lexer": "ipython3", 612 | "version": "3.7.3" 613 | } 614 | }, 615 | "nbformat": 4, 616 | "nbformat_minor": 2 617 | } 618 | -------------------------------------------------------------------------------- /Python_Basis/__pycache__/test.cpython-37.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ChileWang0228/DeepLearningTutorial/81a68bdba63c6a08f5c0c8c6289364ebdbc0b0c6/Python_Basis/__pycache__/test.cpython-37.pyc -------------------------------------------------------------------------------- /Python_Basis/test.py: -------------------------------------------------------------------------------- 1 | def hello(): 2 | print('hello!') 3 | 4 | -------------------------------------------------------------------------------- /Python_Basis/text.txt: -------------------------------------------------------------------------------- 1 | I am chile! 2 | I am chile! 3 | I am chile! 4 | I am chile! 5 | I am chile! 6 | -------------------------------------------------------------------------------- /Python_Basis/tx.txt: -------------------------------------------------------------------------------- 1 | Add some words. -------------------------------------------------------------------------------- /Transfer_learning/.ipynb_checkpoints/Transfer_learning_cnn_image-checkpoint.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 1, 6 | "metadata": {}, 7 | "outputs": [ 8 | { 9 | "name": "stderr", 10 | "output_type": "stream", 11 | "text": [ 12 | "Using TensorFlow backend.\n" 13 | ] 14 | } 15 | ], 16 | "source": [ 17 | "from tensorflow.python.keras.utils import get_file\n", 18 | "import gzip\n", 19 | "import numpy as np\n", 20 | "import keras\n", 21 | "from keras.datasets import cifar10\n", 22 | "from keras.preprocessing.image import ImageDataGenerator\n", 23 | "from keras.models import Sequential, Model\n", 24 | "from keras.layers import Dense, Dropout, Activation, Flatten\n", 25 | "from keras.layers import Conv2D, MaxPooling2D\n", 26 | "import os\n", 27 | "from keras import applications\n", 28 | "import cv2\n", 29 | "import functools\n", 30 | "from keras.models import load_model\n", 31 | "# os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"1\" # 使用第2个GPU" 32 | ] 33 | }, 34 | { 35 | "cell_type": "code", 36 | "execution_count": 2, 37 | "metadata": {}, 38 | "outputs": [], 39 | "source": [ 40 | "# 数据集与代码放在一起即可\n", 41 | "def load_data():\n", 42 | " paths = [\n", 43 | " 'train-labels-idx1-ubyte.gz', 'train-images-idx3-ubyte.gz',\n", 44 | " 't10k-labels-idx1-ubyte.gz', 't10k-images-idx3-ubyte.gz'\n", 45 | " ]\n", 46 | "\n", 47 | " with gzip.open(paths[0], 'rb') as lbpath:\n", 48 | " y_train = np.frombuffer(lbpath.read(), np.uint8, offset=8)\n", 49 | "\n", 50 | " with gzip.open(paths[1], 'rb') as imgpath:\n", 51 | " x_train = np.frombuffer(\n", 52 | " imgpath.read(), np.uint8, offset=16).reshape(len(y_train), 28, 28, 1)\n", 53 | "\n", 54 | " with gzip.open(paths[2], 'rb') as lbpath:\n", 55 | " y_test = np.frombuffer(lbpath.read(), np.uint8, offset=8)\n", 56 | "\n", 57 | " with gzip.open(paths[3], 'rb') as imgpath:\n", 58 | " x_test = np.frombuffer(\n", 59 | " imgpath.read(), np.uint8, offset=16).reshape(len(y_test), 28, 28, 1)\n", 60 | "\n", 61 | " return (x_train, y_train), (x_test, y_test)" 62 | ] 63 | }, 64 | { 65 | "cell_type": "markdown", 66 | "metadata": {}, 67 | "source": [ 68 | "## 读取数据与数据预处理" 69 | ] 70 | }, 71 | { 72 | "cell_type": "code", 73 | "execution_count": 6, 74 | "metadata": {}, 75 | "outputs": [], 76 | "source": [ 77 | " # read dataset\n", 78 | "(x_train, y_train), (x_test, y_test) = load_data()\n", 79 | "batch_size = 32\n", 80 | "num_classes = 10\n", 81 | "epochs = 5\n", 82 | "data_augmentation = True # 图像增强\n", 83 | "num_predictions = 20\n", 84 | "save_dir = os.path.join(os.getcwd(), 'saved_models_transfer_learning')\n", 85 | "model_name = 'keras_fashion_transfer_learning_trained_model.h5'\n", 86 | "\n", 87 | "\n", 88 | "# Convert class vectors to binary class matrices. 将类别弄成独热编码\n", 89 | "y_train = keras.utils.to_categorical(y_train, num_classes)\n", 90 | "y_test = keras.utils.to_categorical(y_test, num_classes)\n", 91 | "\n", 92 | "\n", 93 | "# x_train = x_train.astype('float32')\n", 94 | "# x_test = x_test.astype('float32')\n", 95 | "# 由于mist的输入数据维度是(num, 28, 28)，vgg16 需要三维图像,因为扩充一下mnist的最后一维\n", 96 | "X_train = [cv2.cvtColor(cv2.resize(i, (48, 48)), cv2.COLOR_GRAY2RGB) for i in x_train]\n", 97 | "X_test = [cv2.cvtColor(cv2.resize(i, (48, 48)), cv2.COLOR_GRAY2RGB) for i in x_test]\n", 98 | "\n", 99 | "x_train = np.asarray(X_train)\n", 100 | "x_test = np.asarray(X_test)\n", 101 | "\n", 102 | "x_train = x_train.astype('float32')\n", 103 | "x_test = x_test.astype('float32')\n", 104 | "\n", 105 | "x_train /= 255 # 归一化\n", 106 | "x_test /= 255 # 归一化\n" 107 | ] 108 | }, 109 | { 110 | "cell_type": "markdown", 111 | "metadata": {}, 112 | "source": [ 113 | "## 迁移学习建模" 114 | ] 115 | }, 116 | { 117 | "cell_type": "code", 118 | "execution_count": 7, 119 | "metadata": {}, 120 | "outputs": [ 121 | { 122 | "name": "stdout", 123 | "output_type": "stream", 124 | "text": [ 125 | "(48, 48, 3)\n", 126 | "Tensor(\"block5_pool_1/MaxPool:0\", shape=(?, 1, 1, 512), dtype=float32)\n" 127 | ] 128 | } 129 | ], 130 | "source": [ 131 | "# 使用VGG16模型\n", 132 | "base_model = applications.VGG16(include_top=False, weights='imagenet', input_shape=x_train.shape[1:]) # 第一层需要指出图像的大小\n", 133 | "\n", 134 | "# # path to the model weights files.\n", 135 | "# top_model_weights_path = 'bottleneck_fc_model.h5'\n", 136 | "print(x_train.shape[1:])\n", 137 | "model = Sequential()\n", 138 | "print(base_model.output)\n", 139 | "model.add(Flatten(input_shape=base_model.output_shape[1:]))\n", 140 | "model.add(Dense(256, activation='relu'))\n", 141 | "model.add(Dropout(0.5))\n", 142 | "model.add(Dense(num_classes))\n", 143 | "model.add(Activation('softmax'))\n", 144 | "\n", 145 | "# add the model on top of the convolutional base\n", 146 | "model = Model(inputs=base_model.input, outputs=model(base_model.output)) # VGG16模型与自己构建的模型合并\n", 147 | " \n", 148 | "for layer in model.layers[:15]:\n", 149 | " layer.trainable = False\n", 150 | "\n", 151 | "# initiate RMSprop optimizer\n", 152 | "opt = keras.optimizers.rmsprop(lr=0.0001, decay=1e-6)\n", 153 | "\n", 154 | "# Let's train the model using RMSprop\n", 155 | "model.compile(loss='categorical_crossentropy',\n", 156 | " optimizer=opt,\n", 157 | " metrics=['accuracy'])" 158 | ] 159 | }, 160 | { 161 | "cell_type": "markdown", 162 | "metadata": {}, 163 | "source": [ 164 | "## 训练" 165 | ] 166 | }, 167 | { 168 | "cell_type": "code", 169 | "execution_count": 8, 170 | "metadata": { 171 | "scrolled": true 172 | }, 173 | "outputs": [ 174 | { 175 | "name": "stdout", 176 | "output_type": "stream", 177 | "text": [ 178 | "Using real-time data augmentation.\n", 179 | "1875\n", 180 | "1875.0\n", 181 | "Epoch 1/5\n", 182 | "1875/1875 [==============================] - 1144s 610ms/step - loss: 0.4936 - acc: 0.8304 - val_loss: 0.3751 - val_acc: 0.8639\n", 183 | "Epoch 2/5\n", 184 | "1875/1875 [==============================] - 855s 456ms/step - loss: 0.3874 - acc: 0.8690 - val_loss: 0.3440 - val_acc: 0.8810\n", 185 | "Epoch 3/5\n", 186 | "1875/1875 [==============================] - 825s 440ms/step - loss: 0.3633 - acc: 0.8799 - val_loss: 0.3488 - val_acc: 0.8914\n", 187 | "Epoch 4/5\n", 188 | "1875/1875 [==============================] - 1563s 834ms/step - loss: 0.3491 - acc: 0.8855 - val_loss: 0.3238 - val_acc: 0.8998\n", 189 | "Epoch 5/5\n", 190 | "1875/1875 [==============================] - 1929s 1s/step - loss: 0.3443 - acc: 0.8911 - val_loss: 0.3749 - val_acc: 0.8878\n" 191 | ] 192 | } 193 | ], 194 | "source": [ 195 | "if not data_augmentation:\n", 196 | " print('Not using data augmentation.')\n", 197 | " history = model.fit(x_train, y_train,\n", 198 | " batch_size=batch_size,\n", 199 | " epochs=epochs,\n", 200 | " validation_data=(x_test, y_test),\n", 201 | " shuffle=True)\n", 202 | "else:\n", 203 | " print('Using real-time data augmentation.')\n", 204 | " # This will do preprocessing and realtime data augmentation:\n", 205 | " datagen = ImageDataGenerator(\n", 206 | " featurewise_center=False, # set input mean to 0 over the dataset\n", 207 | " samplewise_center=False, # set each sample mean to 0\n", 208 | " featurewise_std_normalization=False, # divide inputs by std of the dataset\n", 209 | " samplewise_std_normalization=False, # divide each input by its std\n", 210 | " zca_whitening=False, # apply ZCA whitening\n", 211 | " zca_epsilon=1e-06, # epsilon for ZCA whitening\n", 212 | " rotation_range=0, # randomly rotate images in the range (degrees, 0 to 180)\n", 213 | " # randomly shift images horizontally (fraction of total width)\n", 214 | " width_shift_range=0.1,\n", 215 | " # randomly shift images vertically (fraction of total height)\n", 216 | " height_shift_range=0.1,\n", 217 | " shear_range=0., # set range for random shear\n", 218 | " zoom_range=0., # set range for random zoom\n", 219 | " channel_shift_range=0., # set range for random channel shifts\n", 220 | " # set mode for filling points outside the input boundaries\n", 221 | " fill_mode='nearest',\n", 222 | " cval=0., # value used for fill_mode = \"constant\"\n", 223 | " horizontal_flip=True, # randomly flip images\n", 224 | " vertical_flip=False, # randomly flip images\n", 225 | " # set rescaling factor (applied before any other transformation)\n", 226 | " rescale=None,\n", 227 | " # set function that will be applied on each input\n", 228 | " preprocessing_function=None,\n", 229 | " # image data format, either \"channels_first\" or \"channels_last\"\n", 230 | " data_format=None,\n", 231 | " # fraction of images reserved for validation (strictly between 0 and 1)\n", 232 | " validation_split=0.0)\n", 233 | "\n", 234 | " # Compute quantities required for feature-wise normalization\n", 235 | " # (std, mean, and principal components if ZCA whitening is applied).\n", 236 | " datagen.fit(x_train)\n", 237 | " print(x_train.shape[0]//batch_size) # 取整\n", 238 | " print(x_train.shape[0]/batch_size) # 保留小数\n", 239 | " # Fit the model on the batches generated by datagen.flow().\n", 240 | " history = model.fit_generator(datagen.flow(x_train, y_train, # 按batch_size大小从x,y生成增强数据\n", 241 | " batch_size=batch_size), \n", 242 | " # flow_from_directory()从路径生成增强数据,和flow方法相比最大的优点在于不用\n", 243 | " # 一次将所有的数据读入内存当中,这样减小内存压力，这样不会发生OOM\n", 244 | " epochs=epochs,\n", 245 | " steps_per_epoch=x_train.shape[0]//batch_size,\n", 246 | " validation_data=(x_test, y_test),\n", 247 | " workers=10 # 在使用基于进程的线程时，最多需要启动的进程数量。\n", 248 | " )" 249 | ] 250 | }, 251 | { 252 | "cell_type": "markdown", 253 | "metadata": {}, 254 | "source": [ 255 | "## 模型可视化与保存模型" 256 | ] 257 | }, 258 | { 259 | "cell_type": "code", 260 | "execution_count": 9, 261 | "metadata": {}, 262 | "outputs": [ 263 | { 264 | "name": "stdout", 265 | "output_type": "stream", 266 | "text": [ 267 | "_________________________________________________________________\n", 268 | "Layer (type) Output Shape Param # \n", 269 | "=================================================================\n", 270 | "input_2 (InputLayer) (None, 48, 48, 3) 0 \n", 271 | "_________________________________________________________________\n", 272 | "block1_conv1 (Conv2D) (None, 48, 48, 64) 1792 \n", 273 | "_________________________________________________________________\n", 274 | "block1_conv2 (Conv2D) (None, 48, 48, 64) 36928 \n", 275 | "_________________________________________________________________\n", 276 | "block1_pool (MaxPooling2D) (None, 24, 24, 64) 0 \n", 277 | "_________________________________________________________________\n", 278 | "block2_conv1 (Conv2D) (None, 24, 24, 128) 73856 \n", 279 | "_________________________________________________________________\n", 280 | "block2_conv2 (Conv2D) (None, 24, 24, 128) 147584 \n", 281 | "_________________________________________________________________\n", 282 | "block2_pool (MaxPooling2D) (None, 12, 12, 128) 0 \n", 283 | "_________________________________________________________________\n", 284 | "block3_conv1 (Conv2D) (None, 12, 12, 256) 295168 \n", 285 | "_________________________________________________________________\n", 286 | "block3_conv2 (Conv2D) (None, 12, 12, 256) 590080 \n", 287 | "_________________________________________________________________\n", 288 | "block3_conv3 (Conv2D) (None, 12, 12, 256) 590080 \n", 289 | "_________________________________________________________________\n", 290 | "block3_pool (MaxPooling2D) (None, 6, 6, 256) 0 \n", 291 | "_________________________________________________________________\n", 292 | "block4_conv1 (Conv2D) (None, 6, 6, 512) 1180160 \n", 293 | "_________________________________________________________________\n", 294 | "block4_conv2 (Conv2D) (None, 6, 6, 512) 2359808 \n", 295 | "_________________________________________________________________\n", 296 | "block4_conv3 (Conv2D) (None, 6, 6, 512) 2359808 \n", 297 | "_________________________________________________________________\n", 298 | "block4_pool (MaxPooling2D) (None, 3, 3, 512) 0 \n", 299 | "_________________________________________________________________\n", 300 | "block5_conv1 (Conv2D) (None, 3, 3, 512) 2359808 \n", 301 | "_________________________________________________________________\n", 302 | "block5_conv2 (Conv2D) (None, 3, 3, 512) 2359808 \n", 303 | "_________________________________________________________________\n", 304 | "block5_conv3 (Conv2D) (None, 3, 3, 512) 2359808 \n", 305 | "_________________________________________________________________\n", 306 | "block5_pool (MaxPooling2D) (None, 1, 1, 512) 0 \n", 307 | "_________________________________________________________________\n", 308 | "sequential_2 (Sequential) (None, 10) 133898 \n", 309 | "=================================================================\n", 310 | "Total params: 14,848,586\n", 311 | "Trainable params: 7,213,322\n", 312 | "Non-trainable params: 7,635,264\n", 313 | "_________________________________________________________________\n", 314 | "Saved trained model at /home/student/ChileWang/machine_learning_homework/question_one/saved_models_transfer_learning/keras_fashion_transfer_learning_trained_model.h5 \n" 315 | ] 316 | } 317 | ], 318 | "source": [ 319 | "model.summary()\n", 320 | "# Save model and weights\n", 321 | "if not os.path.isdir(save_dir):\n", 322 | " os.makedirs(save_dir)\n", 323 | "model_path = os.path.join(save_dir, model_name)\n", 324 | "model.save(model_path)\n", 325 | "print('Saved trained model at %s ' % model_path)" 326 | ] 327 | }, 328 | { 329 | "cell_type": "markdown", 330 | "metadata": {}, 331 | "source": [ 332 | "## 训练过程可视化" 333 | ] 334 | }, 335 | { 336 | "cell_type": "code", 337 | "execution_count": 10, 338 | "metadata": {}, 339 | "outputs": [ 340 | { 341 | "data": { 342 | "text/plain": [ 343 | "
" 344 | ] 345 | }, 346 | "metadata": {}, 347 | "output_type": "display_data" 348 | }, 349 | { 350 | "data": { 351 | "text/plain": [ 352 | "
" 353 | ] 354 | }, 355 | "metadata": {}, 356 | "output_type": "display_data" 357 | } 358 | ], 359 | "source": [ 360 | "import matplotlib.pyplot as plt\n", 361 | "# 绘制训练 & 验证的准确率值\n", 362 | "plt.plot(history.history['acc'])\n", 363 | "plt.plot(history.history['val_acc'])\n", 364 | "plt.title('Model accuracy')\n", 365 | "plt.ylabel('Accuracy')\n", 366 | "plt.xlabel('Epoch')\n", 367 | "plt.legend(['Train', 'Valid'], loc='upper left')\n", 368 | "plt.savefig('tradition_cnn_valid_acc.png')\n", 369 | "plt.show()\n", 370 | "\n", 371 | "# 绘制训练 & 验证的损失值\n", 372 | "plt.plot(history.history['loss'])\n", 373 | "plt.plot(history.history['val_loss'])\n", 374 | "plt.title('Model loss')\n", 375 | "plt.ylabel('Loss')\n", 376 | "plt.xlabel('Epoch')\n", 377 | "plt.legend(['Train', 'Valid'], loc='upper left')\n", 378 | "plt.savefig('tradition_cnn_valid_loss.png')\n", 379 | "plt.show()" 380 | ] 381 | } 382 | ], 383 | "metadata": { 384 | "kernelspec": { 385 | "display_name": "Python 3", 386 | "language": "python", 387 | "name": "python3" 388 | }, 389 | "language_info": { 390 | "codemirror_mode": { 391 | "name": "ipython", 392 | "version": 3 393 | }, 394 | "file_extension": ".py", 395 | "mimetype": "text/x-python", 396 | "name": "python", 397 | "nbconvert_exporter": "python", 398 | "pygments_lexer": "ipython3", 399 | "version": "3.7.3" 400 | } 401 | }, 402 | "nbformat": 4, 403 | "nbformat_minor": 2 404 | } 405 | -------------------------------------------------------------------------------- /Transfer_learning/Trasnfer_learning_cnn_regression.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": null, 6 | "metadata": {}, 7 | "outputs": [], 8 | "source": [ 9 | "from tensorflow.python.keras.utils import get_file\n", 10 | "import gzip\n", 11 | "import numpy as np\n", 12 | "import keras\n", 13 | "from keras.preprocessing.image import ImageDataGenerator\n", 14 | "from keras.models import Sequential, Model\n", 15 | "from keras.layers import Dense, Dropout, Activation, Flatten\n", 16 | "from keras.layers import Conv2D, MaxPooling2D\n", 17 | "import os\n", 18 | "from keras import applications\n", 19 | "import cv2\n", 20 | "import functools\n", 21 | "from keras.models import load_model\n", 22 | "import pandas as pd\n", 23 | "from sklearn.preprocessing import MinMaxScaler\n", 24 | "# os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"1\" # 使用第2个GPU" 25 | ] 26 | }, 27 | { 28 | "cell_type": "code", 29 | "execution_count": 98, 30 | "metadata": {}, 31 | "outputs": [], 32 | "source": [ 33 | "def load_data():\n", 34 | " paths = [\n", 35 | " 'train-labels-idx1-ubyte.gz', 'train-images-idx3-ubyte.gz',\n", 36 | " 't10k-labels-idx1-ubyte.gz', 't10k-images-idx3-ubyte.gz'\n", 37 | " ]\n", 38 | "\n", 39 | " with gzip.open(paths[0], 'rb') as lbpath:\n", 40 | " y_train = np.frombuffer(lbpath.read(), np.uint8, offset=8)\n", 41 | "\n", 42 | " with gzip.open(paths[1], 'rb') as imgpath:\n", 43 | " x_train = np.frombuffer(\n", 44 | " imgpath.read(), np.uint8, offset=16).reshape(len(y_train), 28, 28, 1)\n", 45 | "\n", 46 | " with gzip.open(paths[2], 'rb') as lbpath:\n", 47 | " y_test = np.frombuffer(lbpath.read(), np.uint8, offset=8)\n", 48 | "\n", 49 | " with gzip.open(paths[3], 'rb') as imgpath:\n", 50 | " x_test = np.frombuffer(\n", 51 | " imgpath.read(), np.uint8, offset=16).reshape(len(y_test), 28, 28, 1)\n", 52 | "\n", 53 | " return (x_train, y_train), (x_test, y_test)" 54 | ] 55 | }, 56 | { 57 | "cell_type": "markdown", 58 | "metadata": {}, 59 | "source": [ 60 | "## 读取数据与数据预处理" 61 | ] 62 | }, 63 | { 64 | "cell_type": "code", 65 | "execution_count": 99, 66 | "metadata": {}, 67 | "outputs": [], 68 | "source": [ 69 | " # read dataset\n", 70 | "(x_train, y_train), (x_test, y_test) = load_data()\n", 71 | "batch_size = 32\n", 72 | "epochs = 5\n", 73 | "data_augmentation = True # 图像增强\n", 74 | "num_predictions = 20\n", 75 | "save_dir = os.path.join(os.getcwd(), 'saved_models_transfer_learning')\n", 76 | "model_name = 'keras_fashion_transfer_learning_trained_model.h5'\n", 77 | "\n", 78 | "\n", 79 | "# Convert class vectors to binary class matrices. 将类别弄成独热编码\n", 80 | "# y_train = keras.utils.to_categorical(y_train, num_classes)\n", 81 | "# y_test = keras.utils.to_categorical(y_test, num_classes)\n", 82 | "\n", 83 | "\n", 84 | "# x_train = x_train.astype('float32')\n", 85 | "# x_test = x_test.astype('float32')\n", 86 | "# 由于mist的输入数据维度是(num, 28, 28)，vgg16 需要三维图像,因为扩充一下mnist的最后一维\n", 87 | "X_train = [cv2.cvtColor(cv2.resize(i, (48, 48)), cv2.COLOR_GRAY2RGB) for i in x_train]\n", 88 | "X_test = [cv2.cvtColor(cv2.resize(i, (48, 48)), cv2.COLOR_GRAY2RGB) for i in x_test]\n", 89 | "\n", 90 | "x_train = np.asarray(X_train)\n", 91 | "x_test = np.asarray(X_test)\n", 92 | "\n", 93 | "x_train = x_train.astype('float32')\n", 94 | "x_test = x_test.astype('float32')\n", 95 | "\n", 96 | "x_train /= 255 # 归一化\n", 97 | "x_test /= 255 # 归一化" 98 | ] 99 | }, 100 | { 101 | "cell_type": "markdown", 102 | "metadata": {}, 103 | "source": [ 104 | "## 伪造回归数据\n", 105 | "总共有10类衣服，他们每一类的价格设置规则如下： \n", 106 | "以(45, 57, 85, 99, 125, 27, 180, 152, 225, 33)为每一类衣服的均值，以3为标准差， \n", 107 | "利用正太分布小数点后两位的价格作为他们的衣服价格。" 108 | ] 109 | }, 110 | { 111 | "cell_type": "code", 112 | "execution_count": 100, 113 | "metadata": {}, 114 | "outputs": [ 115 | { 116 | "name": "stdout", 117 | "output_type": "stream", 118 | "text": [ 119 | " label price\n", 120 | "0 9 29.66\n", 121 | "1 0 45.15\n", 122 | "2 0 50.35\n", 123 | "3 3 99.19\n", 124 | "4 0 46.33\n", 125 | "-------------------\n", 126 | " label price\n", 127 | "0 9 32.51\n", 128 | "1 2 80.91\n", 129 | "2 1 56.77\n", 130 | "3 1 56.36\n", 131 | "4 6 181.70\n" 132 | ] 133 | } 134 | ], 135 | "source": [ 136 | "# 转成DataFrame格式方便数据处理\n", 137 | "y_train_pd = pd.DataFrame(y_train)\n", 138 | "y_test_pd = pd.DataFrame(y_test)\n", 139 | "# 设置列名\n", 140 | "y_train_pd.columns = ['label'] \n", 141 | "y_test_pd.columns = ['label']\n", 142 | "\n", 143 | "# 给每一类衣服设置价格\n", 144 | "mean_value_list = [45, 57, 85, 99, 125, 27, 180, 152, 225, 33] # 均值列表\n", 145 | "def setting_clothes_price(row):\n", 146 | " price = sorted(np.random.normal(mean_value_list[int(row)], 3,size=1))[0] #均值mean,标准差std,数量\n", 147 | " return np.round(price, 2)\n", 148 | "y_train_pd['price'] = y_train_pd['label'].apply(setting_clothes_price)\n", 149 | "y_test_pd['price'] = y_test_pd['label'].apply(setting_clothes_price)\n", 150 | "\n", 151 | "print(y_train_pd.head(5))\n", 152 | "print('-------------------')\n", 153 | "print(y_test_pd.head(5))" 154 | ] 155 | }, 156 | { 157 | "cell_type": "markdown", 158 | "metadata": {}, 159 | "source": [ 160 | "## 数据归一化" 161 | ] 162 | }, 163 | { 164 | "cell_type": "code", 165 | "execution_count": 115, 166 | "metadata": {}, 167 | "outputs": [ 168 | { 169 | "name": "stdout", 170 | "output_type": "stream", 171 | "text": [ 172 | "60000\n", 173 | "10000\n" 174 | ] 175 | } 176 | ], 177 | "source": [ 178 | "# y_train_price_pd = y_train_pd['price'].tolist()\n", 179 | "# y_test_price_pd = y_test_pd['price'].tolist()\n", 180 | "# 训练集归一化\n", 181 | "min_max_scaler = MinMaxScaler()\n", 182 | "min_max_scaler.fit(y_train_pd)\n", 183 | "y_train = min_max_scaler.transform(y_train_pd)[:, 1]\n", 184 | "\n", 185 | "# 验证集归一化\n", 186 | "min_max_scaler.fit(y_test_pd)\n", 187 | "y_test = min_max_scaler.transform(y_test_pd)[:, 1]\n", 188 | "y_test_label = min_max_scaler.transform(y_test_pd)[:, 0] # 归一化后的标签\n", 189 | "print(len(y_train))\n", 190 | "print(len(y_test))" 191 | ] 192 | }, 193 | { 194 | "cell_type": "markdown", 195 | "metadata": {}, 196 | "source": [ 197 | "## 迁移学习建模" 198 | ] 199 | }, 200 | { 201 | "cell_type": "code", 202 | "execution_count": 102, 203 | "metadata": {}, 204 | "outputs": [ 205 | { 206 | "name": "stdout", 207 | "output_type": "stream", 208 | "text": [ 209 | "(48, 48, 3)\n", 210 | "Tensor(\"block5_pool_6/MaxPool:0\", shape=(?, 1, 1, 512), dtype=float32)\n" 211 | ] 212 | } 213 | ], 214 | "source": [ 215 | "# 使用VGG16模型\n", 216 | "base_model = applications.VGG16(include_top=False, weights='imagenet', input_shape=x_train.shape[1:]) # 第一层需要指出图像的大小\n", 217 | "\n", 218 | "# # path to the model weights files.\n", 219 | "# top_model_weights_path = 'bottleneck_fc_model.h5'\n", 220 | "print(x_train.shape[1:])\n", 221 | "model = Sequential()\n", 222 | "print(base_model.output)\n", 223 | "model.add(Flatten(input_shape=base_model.output_shape[1:]))\n", 224 | "model.add(Dense(256, activation='relu'))\n", 225 | "model.add(Dropout(0.5))\n", 226 | "model.add(Dense(1))\n", 227 | "model.add(Activation('linear'))\n", 228 | "\n", 229 | "# add the model on top of the convolutional base\n", 230 | "model = Model(inputs=base_model.input, outputs=model(base_model.output)) # VGG16模型与自己构建的模型合并\n", 231 | "\n", 232 | "# 保持VGG16的前15层权值不变，即在训练过程中不训练 \n", 233 | "for layer in model.layers[:15]:\n", 234 | " layer.trainable = False\n", 235 | "\n", 236 | "# initiate RMSprop optimizer\n", 237 | "opt = keras.optimizers.rmsprop(lr=0.0001, decay=1e-6)\n", 238 | "\n", 239 | "# Let's train the model using RMSprop\n", 240 | "model.compile(loss='mse',\n", 241 | " optimizer=opt,\n", 242 | " )" 243 | ] 244 | }, 245 | { 246 | "cell_type": "markdown", 247 | "metadata": {}, 248 | "source": [ 249 | "## 训练" 250 | ] 251 | }, 252 | { 253 | "cell_type": "code", 254 | "execution_count": 103, 255 | "metadata": {}, 256 | "outputs": [ 257 | { 258 | "name": "stdout", 259 | "output_type": "stream", 260 | "text": [ 261 | "Using real-time data augmentation.\n", 262 | "1875\n", 263 | "1875.0\n", 264 | "Epoch 1/5\n", 265 | "1875/1875 [==============================] - 1149s 613ms/step - loss: 0.0336 - val_loss: 0.0178\n", 266 | "Epoch 2/5\n", 267 | "1875/1875 [==============================] - 1805s 963ms/step - loss: 0.0183 - val_loss: 0.0146\n", 268 | "Epoch 3/5\n", 269 | "1875/1875 [==============================] - 2126s 1s/step - loss: 0.0162 - val_loss: 0.0140\n", 270 | "Epoch 4/5\n", 271 | "1875/1875 [==============================] - 2279s 1s/step - loss: 0.0151 - val_loss: 0.0145\n", 272 | "Epoch 5/5\n", 273 | "1875/1875 [==============================] - 2196s 1s/step - loss: 0.0144 - val_loss: 0.0136\n" 274 | ] 275 | } 276 | ], 277 | "source": [ 278 | "if not data_augmentation:\n", 279 | " print('Not using data augmentation.')\n", 280 | " history = model.fit(x_train, y_train,\n", 281 | " batch_size=batch_size,\n", 282 | " epochs=epochs,\n", 283 | " validation_data=(x_test, y_test),\n", 284 | " shuffle=True)\n", 285 | "else:\n", 286 | " print('Using real-time data augmentation.')\n", 287 | " # This will do preprocessing and realtime data augmentation:\n", 288 | " datagen = ImageDataGenerator(\n", 289 | " featurewise_center=False, # set input mean to 0 over the dataset\n", 290 | " samplewise_center=False, # set each sample mean to 0\n", 291 | " featurewise_std_normalization=False, # divide inputs by std of the dataset\n", 292 | " samplewise_std_normalization=False, # divide each input by its std\n", 293 | " zca_whitening=False, # apply ZCA whitening\n", 294 | " zca_epsilon=1e-06, # epsilon for ZCA whitening\n", 295 | " rotation_range=0, # randomly rotate images in the range (degrees, 0 to 180)\n", 296 | " # randomly shift images horizontally (fraction of total width)\n", 297 | " width_shift_range=0.1,\n", 298 | " # randomly shift images vertically (fraction of total height)\n", 299 | " height_shift_range=0.1,\n", 300 | " shear_range=0., # set range for random shear\n", 301 | " zoom_range=0., # set range for random zoom\n", 302 | " channel_shift_range=0., # set range for random channel shifts\n", 303 | " # set mode for filling points outside the input boundaries\n", 304 | " fill_mode='nearest',\n", 305 | " cval=0., # value used for fill_mode = \"constant\"\n", 306 | " horizontal_flip=True, # randomly flip images\n", 307 | " vertical_flip=False, # randomly flip images\n", 308 | " # set rescaling factor (applied before any other transformation)\n", 309 | " rescale=None,\n", 310 | " # set function that will be applied on each input\n", 311 | " preprocessing_function=None,\n", 312 | " # image data format, either \"channels_first\" or \"channels_last\"\n", 313 | " data_format=None,\n", 314 | " # fraction of images reserved for validation (strictly between 0 and 1)\n", 315 | " validation_split=0.0)\n", 316 | "\n", 317 | " # Compute quantities required for feature-wise normalization\n", 318 | " # (std, mean, and principal components if ZCA whitening is applied).\n", 319 | " datagen.fit(x_train)\n", 320 | " print(x_train.shape[0]//batch_size) # 取整\n", 321 | " print(x_train.shape[0]/batch_size) # 保留小数\n", 322 | " # Fit the model on the batches generated by datagen.flow().\n", 323 | " history = model.fit_generator(datagen.flow(x_train, y_train, # 按batch_size大小从x,y生成增强数据\n", 324 | " batch_size=batch_size), \n", 325 | " # flow_from_directory()从路径生成增强数据,和flow方法相比最大的优点在于不用\n", 326 | " # 一次将所有的数据读入内存当中,这样减小内存压，这样不会发生OOM\n", 327 | " epochs=epochs,\n", 328 | " steps_per_epoch=x_train.shape[0]//batch_size,\n", 329 | " validation_data=(x_test, y_test),\n", 330 | " workers=10 # 在使用基于进程的线程时，最多需要启动的进程数量。\n", 331 | " )" 332 | ] 333 | }, 334 | { 335 | "cell_type": "markdown", 336 | "metadata": {}, 337 | "source": [ 338 | "## 模型可视化与保存模型" 339 | ] 340 | }, 341 | { 342 | "cell_type": "code", 343 | "execution_count": 104, 344 | "metadata": {}, 345 | "outputs": [ 346 | { 347 | "name": "stdout", 348 | "output_type": "stream", 349 | "text": [ 350 | "_________________________________________________________________\n", 351 | "Layer (type) Output Shape Param # \n", 352 | "=================================================================\n", 353 | "input_7 (InputLayer) (None, 48, 48, 3) 0 \n", 354 | "_________________________________________________________________\n", 355 | "block1_conv1 (Conv2D) (None, 48, 48, 64) 1792 \n", 356 | "_________________________________________________________________\n", 357 | "block1_conv2 (Conv2D) (None, 48, 48, 64) 36928 \n", 358 | "_________________________________________________________________\n", 359 | "block1_pool (MaxPooling2D) (None, 24, 24, 64) 0 \n", 360 | "_________________________________________________________________\n", 361 | "block2_conv1 (Conv2D) (None, 24, 24, 128) 73856 \n", 362 | "_________________________________________________________________\n", 363 | "block2_conv2 (Conv2D) (None, 24, 24, 128) 147584 \n", 364 | "_________________________________________________________________\n", 365 | "block2_pool (MaxPooling2D) (None, 12, 12, 128) 0 \n", 366 | "_________________________________________________________________\n", 367 | "block3_conv1 (Conv2D) (None, 12, 12, 256) 295168 \n", 368 | "_________________________________________________________________\n", 369 | "block3_conv2 (Conv2D) (None, 12, 12, 256) 590080 \n", 370 | "_________________________________________________________________\n", 371 | "block3_conv3 (Conv2D) (None, 12, 12, 256) 590080 \n", 372 | "_________________________________________________________________\n", 373 | "block3_pool (MaxPooling2D) (None, 6, 6, 256) 0 \n", 374 | "_________________________________________________________________\n", 375 | "block4_conv1 (Conv2D) (None, 6, 6, 512) 1180160 \n", 376 | "_________________________________________________________________\n", 377 | "block4_conv2 (Conv2D) (None, 6, 6, 512) 2359808 \n", 378 | "_________________________________________________________________\n", 379 | "block4_conv3 (Conv2D) (None, 6, 6, 512) 2359808 \n", 380 | "_________________________________________________________________\n", 381 | "block4_pool (MaxPooling2D) (None, 3, 3, 512) 0 \n", 382 | "_________________________________________________________________\n", 383 | "block5_conv1 (Conv2D) (None, 3, 3, 512) 2359808 \n", 384 | "_________________________________________________________________\n", 385 | "block5_conv2 (Conv2D) (None, 3, 3, 512) 2359808 \n", 386 | "_________________________________________________________________\n", 387 | "block5_conv3 (Conv2D) (None, 3, 3, 512) 2359808 \n", 388 | "_________________________________________________________________\n", 389 | "block5_pool (MaxPooling2D) (None, 1, 1, 512) 0 \n", 390 | "_________________________________________________________________\n", 391 | "sequential_7 (Sequential) (None, 1) 131585 \n", 392 | "=================================================================\n", 393 | "Total params: 14,846,273\n", 394 | "Trainable params: 7,211,009\n", 395 | "Non-trainable params: 7,635,264\n", 396 | "_________________________________________________________________\n", 397 | "Saved trained model at /home/student/ChileWang/machine_learning_homework/question_one/saved_models_transfer_learning/keras_fashion_transfer_learning_trained_model.h5 \n" 398 | ] 399 | } 400 | ], 401 | "source": [ 402 | "model.summary()\n", 403 | "# Save model and weights\n", 404 | "if not os.path.isdir(save_dir):\n", 405 | " os.makedirs(save_dir)\n", 406 | "model_path = os.path.join(save_dir, model_name)\n", 407 | "model.save(model_path)\n", 408 | "print('Saved trained model at %s ' % model_path)" 409 | ] 410 | }, 411 | { 412 | "cell_type": "markdown", 413 | "metadata": {}, 414 | "source": [ 415 | "## 训练过程可视化" 416 | ] 417 | }, 418 | { 419 | "cell_type": "code", 420 | "execution_count": 106, 421 | "metadata": {}, 422 | "outputs": [ 423 | { 424 | "data": { 425 | "image/png": "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\n", 426 | "text/plain": [ 427 | "
" 428 | ] 429 | }, 430 | "metadata": { 431 | "needs_background": "light" 432 | }, 433 | "output_type": "display_data" 434 | } 435 | ], 436 | "source": [ 437 | "import matplotlib.pyplot as plt\n", 438 | "# 绘制训练 & 验证的损失值\n", 439 | "plt.plot(history.history['loss'])\n", 440 | "plt.plot(history.history['val_loss'])\n", 441 | "plt.title('Model loss')\n", 442 | "plt.ylabel('Loss')\n", 443 | "plt.xlabel('Epoch')\n", 444 | "plt.legend(['Train', 'Test'], loc='upper left')\n", 445 | "plt.show()" 446 | ] 447 | }, 448 | { 449 | "cell_type": "markdown", 450 | "metadata": {}, 451 | "source": [ 452 | "## 模型的预测功能" 453 | ] 454 | }, 455 | { 456 | "cell_type": "code", 457 | "execution_count": 116, 458 | "metadata": {}, 459 | "outputs": [ 460 | { 461 | "name": "stdout", 462 | "output_type": "stream", 463 | "text": [ 464 | "[[ 9. 36.39809359]\n", 465 | " [ 2. 90.78814453]\n", 466 | " [ 1. 58.21489228]\n", 467 | " ...\n", 468 | " [ 8. 225.04189952]\n", 469 | " [ 1. 58.29625367]\n", 470 | " [ 5. 118.09064091]]\n" 471 | ] 472 | } 473 | ], 474 | "source": [ 475 | "# 预测\n", 476 | "y_new = model.predict(x_test)\n", 477 | "# 反归一化\n", 478 | "min_max_scaler.fit(y_test_pd)\n", 479 | "y_pred_pd = pd.DataFrame({'label':list(y_test_label), 'price':list(y_new)})\n", 480 | "y_new = min_max_scaler.inverse_transform(y_pred_pd)" 481 | ] 482 | }, 483 | { 484 | "cell_type": "markdown", 485 | "metadata": {}, 486 | "source": [ 487 | "## 真实值与预测值" 488 | ] 489 | }, 490 | { 491 | "cell_type": "code", 492 | "execution_count": 120, 493 | "metadata": {}, 494 | "outputs": [ 495 | { 496 | "name": "stdout", 497 | "output_type": "stream", 498 | "text": [ 499 | " label price true_price\n", 500 | "0 9 32.51 32.51\n", 501 | "1 2 80.91 80.91\n", 502 | "2 1 56.77 56.77\n", 503 | "3 1 56.36 56.36\n", 504 | "4 6 181.70 181.70\n", 505 | "5 1 58.06 58.06\n", 506 | "6 4 127.31 127.31\n", 507 | "7 6 180.50 180.50\n", 508 | "8 5 27.21 27.21\n", 509 | "9 7 147.69 147.69\n", 510 | "10 4 123.40 123.40\n", 511 | "11 5 28.70 28.70\n", 512 | "12 7 153.20 153.20\n", 513 | "13 3 99.74 99.74\n", 514 | "14 4 117.12 117.12\n", 515 | "15 1 56.82 56.82\n", 516 | "16 2 84.18 84.18\n", 517 | "17 4 124.43 124.43\n", 518 | "18 8 221.80 221.80\n", 519 | "19 0 50.01 50.01\n" 520 | ] 521 | } 522 | ], 523 | "source": [ 524 | "y_test_pd['true_price'] = pd.DataFrame(y_test_pd['price'])\n", 525 | "print(y_test_pd.head(20))" 526 | ] 527 | } 528 | ], 529 | "metadata": { 530 | "kernelspec": { 531 | "display_name": "Python 3", 532 | "language": "python", 533 | "name": "python3" 534 | }, 535 | "language_info": { 536 | "codemirror_mode": { 537 | "name": "ipython", 538 | "version": 3 539 | }, 540 | "file_extension": ".py", 541 | "mimetype": "text/x-python", 542 | "name": "python", 543 | "nbconvert_exporter": "python", 544 | "pygments_lexer": "ipython3", 545 | "version": "3.7.2" 546 | } 547 | }, 548 | "nbformat": 4, 549 | "nbformat_minor": 2 550 | } 551 | --------------------------------------------------------------------------------