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@@ -0,0 +1,34 @@
+# 遗失宠物的智能寻找
+
+## 1.作品介绍：
+
+伴随着人们物质生活水平的提高，越来越多的家庭选择饲养小动物，它们不仅是家庭的宠物，很多人也将他们视作家庭成员之一，给予百般宠爱和呵护。然而即便如此，意外还是难免发生——宠物的意外走失给很多宠物家庭带来了困扰，满大街发传单、贴广告不仅浪费时间，成效也甚微。
+作品使用猫脸识别对遗失宠物智能寻找。
+
+## 2.作品截图
+
+### 2.1 训练模型
+
+![model.png](./model.png)
+
+### 2.2 训练结果
+
+![ACCnLOSS.png](./ACCnLOSS.png)
+
+## 3.安装、编译指南
+
+使用Anaconda3集成开发环境，直接运行.ipynb文件即可。
+
+## 4.团队介绍
+
+范玉兴 
+联系方式：17852835289
+
+## 5.使用到的 AWS 技术
+
+使用了Amazon SageMaker 服务
+
+
+```python
+
+```
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@@ -0,0 +1,478 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "64\n",
+      "(64, 128, 128, 3)\n",
+      "Epoch 1/150\n",
+      "1/1 [==============================] - 0s 208ms/step - loss: 21.1519 - acc: 0.5091 - val_loss: 1676.4619 - val_acc: 0.5556\n",
+      "Epoch 2/150\n",
+      "1/1 [==============================] - 0s 52ms/step - loss: 1953.9664 - acc: 0.4909 - val_loss: 45.7814 - val_acc: 0.4444\n",
+      "Epoch 3/150\n",
+      "1/1 [==============================] - 0s 53ms/step - loss: 40.2835 - acc: 0.5091 - val_loss: 27.6262 - val_acc: 0.5556\n",
+      "Epoch 4/150\n",
+      "1/1 [==============================] - 0s 55ms/step - loss: 31.1680 - acc: 0.4909 - val_loss: 14.6957 - val_acc: 0.4444\n",
+      "Epoch 5/150\n",
+      "1/1 [==============================] - 0s 53ms/step - loss: 13.0709 - acc: 0.5091 - val_loss: 14.7450 - val_acc: 0.5556\n",
+      "Epoch 6/150\n",
+      "1/1 [==============================] - 0s 53ms/step - loss: 16.8352 - acc: 0.4909 - val_loss: 7.6060 - val_acc: 0.4444\n",
+      "Epoch 7/150\n",
+      "1/1 [==============================] - 0s 53ms/step - loss: 6.7195 - acc: 0.5091 - val_loss: 5.3022 - val_acc: 0.5556\n",
+      "Epoch 8/150\n",
+      "1/1 [==============================] - 0s 52ms/step - loss: 6.2027 - acc: 0.4909 - val_loss: 8.7675 - val_acc: 0.4444\n",
+      "Epoch 9/150\n",
+      "1/1 [==============================] - 0s 52ms/step - loss: 7.6343 - acc: 0.5091 - val_loss: 1.5749 - val_acc: 0.5556\n",
+      "Epoch 10/150\n",
+      "1/1 [==============================] - 0s 51ms/step - loss: 1.9666 - acc: 0.4909 - val_loss: 1.8943 - val_acc: 0.4444\n",
+      "Epoch 11/150\n",
+      "1/1 [==============================] - 0s 51ms/step - loss: 1.6665 - acc: 0.5091 - val_loss: 0.7083 - val_acc: 0.5556\n",
+      "Epoch 12/150\n",
+      "1/1 [==============================] - 0s 54ms/step - loss: 0.9720 - acc: 0.4909 - val_loss: 1.2660 - val_acc: 0.4444\n",
+      "Epoch 13/150\n",
+      "1/1 [==============================] - 0s 51ms/step - loss: 1.1073 - acc: 0.5091 - val_loss: 0.6564 - val_acc: 0.5556\n",
+      "Epoch 14/150\n",
+      "1/1 [==============================] - 0s 51ms/step - loss: 0.9676 - acc: 0.4909 - val_loss: 0.5349 - val_acc: 0.4444\n",
+      "Epoch 15/150\n",
+      "1/1 [==============================] - 0s 52ms/step - loss: 0.4790 - acc: 0.5636 - val_loss: 0.8749 - val_acc: 0.5556\n",
+      "Epoch 16/150\n",
+      "1/1 [==============================] - 0s 51ms/step - loss: 1.1516 - acc: 0.4909 - val_loss: 2.5633 - val_acc: 0.4444\n",
+      "Epoch 17/150\n",
+      "1/1 [==============================] - 0s 53ms/step - loss: 2.3623 - acc: 0.5091 - val_loss: 0.5353 - val_acc: 0.8889\n",
+      "Epoch 18/150\n",
+      "1/1 [==============================] - 0s 55ms/step - loss: 0.5722 - acc: 0.8364 - val_loss: 0.3638 - val_acc: 1.0000\n",
+      "Epoch 19/150\n",
+      "1/1 [==============================] - 0s 53ms/step - loss: 0.3822 - acc: 1.0000 - val_loss: 0.3629 - val_acc: 0.6667\n",
+      "Epoch 20/150\n",
+      "1/1 [==============================] - 0s 51ms/step - loss: 0.5244 - acc: 0.5818 - val_loss: 4.8037 - val_acc: 0.4444\n",
+      "Epoch 21/150\n",
+      "1/1 [==============================] - 0s 52ms/step - loss: 4.1065 - acc: 0.5091 - val_loss: 0.6516 - val_acc: 0.5556\n",
+      "Epoch 22/150\n",
+      "1/1 [==============================] - 0s 54ms/step - loss: 0.8332 - acc: 0.4909 - val_loss: 0.9121 - val_acc: 0.4444\n",
+      "Epoch 23/150\n",
+      "1/1 [==============================] - 0s 52ms/step - loss: 0.8005 - acc: 0.5091 - val_loss: 0.5352 - val_acc: 0.5556\n",
+      "Epoch 24/150\n",
+      "1/1 [==============================] - 0s 57ms/step - loss: 0.5947 - acc: 0.5091 - val_loss: 0.5429 - val_acc: 0.4444\n",
+      "Epoch 25/150\n",
+      "1/1 [==============================] - 0s 57ms/step - loss: 0.4989 - acc: 0.5636 - val_loss: 0.3001 - val_acc: 1.0000\n",
+      "Epoch 26/150\n",
+      "1/1 [==============================] - 0s 56ms/step - loss: 0.3227 - acc: 1.0000 - val_loss: 0.4027 - val_acc: 0.6667\n",
+      "Epoch 27/150\n",
+      "1/1 [==============================] - 0s 54ms/step - loss: 0.3357 - acc: 0.8182 - val_loss: 1.0084 - val_acc: 0.5556\n",
+      "Epoch 28/150\n",
+      "1/1 [==============================] - 0s 52ms/step - loss: 1.3471 - acc: 0.4909 - val_loss: 1.1498 - val_acc: 0.4444\n",
+      "Epoch 29/150\n",
+      "1/1 [==============================] - 0s 56ms/step - loss: 0.9479 - acc: 0.5091 - val_loss: 0.2077 - val_acc: 1.0000\n",
+      "Epoch 30/150\n",
+      "1/1 [==============================] - 0s 56ms/step - loss: 0.2095 - acc: 1.0000 - val_loss: 0.0204 - val_acc: 1.0000\n",
+      "Epoch 31/150\n",
+      "1/1 [==============================] - 0s 53ms/step - loss: 0.1478 - acc: 0.9455 - val_loss: 18.3753 - val_acc: 0.4444\n",
+      "Epoch 32/150\n",
+      "1/1 [==============================] - 0s 59ms/step - loss: 16.1089 - acc: 0.5091 - val_loss: 0.4808 - val_acc: 0.6667\n",
+      "Epoch 33/150\n",
+      "1/1 [==============================] - 0s 46ms/step - loss: 0.8305 - acc: 0.4909 - val_loss: 15.3972 - val_acc: 0.4444\n",
+      "Epoch 34/150\n",
+      "1/1 [==============================] - 0s 48ms/step - loss: 13.2952 - acc: 0.5091 - val_loss: 0.8384 - val_acc: 0.5556\n",
+      "Epoch 35/150\n",
+      "1/1 [==============================] - 0s 46ms/step - loss: 1.0261 - acc: 0.4909 - val_loss: 6.2369 - val_acc: 0.4444\n",
+      "Epoch 36/150\n",
+      "1/1 [==============================] - 0s 51ms/step - loss: 5.2973 - acc: 0.5091 - val_loss: 5.2466 - val_acc: 0.5556\n",
+      "Epoch 37/150\n",
+      "1/1 [==============================] - 0s 50ms/step - loss: 6.3887 - acc: 0.4909 - val_loss: 0.5770 - val_acc: 0.4444\n",
+      "Epoch 38/150\n",
+      "1/1 [==============================] - 0s 51ms/step - loss: 0.5305 - acc: 0.5455 - val_loss: 3.0434 - val_acc: 0.5556\n",
+      "Epoch 39/150\n",
+      "1/1 [==============================] - 0s 47ms/step - loss: 3.7633 - acc: 0.4909 - val_loss: 3.0150 - val_acc: 0.4444\n",
+      "Epoch 40/150\n",
+      "1/1 [==============================] - 0s 51ms/step - loss: 2.5691 - acc: 0.5091 - val_loss: 0.6889 - val_acc: 0.5556\n",
+      "Epoch 41/150\n",
+      "1/1 [==============================] - 0s 49ms/step - loss: 0.7780 - acc: 0.4909 - val_loss: 0.7121 - val_acc: 0.4444\n",
+      "Epoch 42/150\n",
+      "1/1 [==============================] - 0s 56ms/step - loss: 0.6317 - acc: 0.5091 - val_loss: 0.4726 - val_acc: 1.0000\n",
+      "Epoch 43/150\n",
+      "1/1 [==============================] - 0s 53ms/step - loss: 0.4998 - acc: 0.8727 - val_loss: 0.8759 - val_acc: 0.4444\n",
+      "Epoch 44/150\n",
+      "1/1 [==============================] - 0s 49ms/step - loss: 0.7343 - acc: 0.5091 - val_loss: 0.6894 - val_acc: 0.5556\n",
+      "Epoch 45/150\n",
+      "1/1 [==============================] - 0s 48ms/step - loss: 0.8186 - acc: 0.4909 - val_loss: 0.6201 - val_acc: 0.4444\n",
+      "Epoch 46/150\n",
+      "1/1 [==============================] - 0s 48ms/step - loss: 0.5341 - acc: 0.5091 - val_loss: 0.3559 - val_acc: 1.0000\n",
+      "Epoch 47/150\n",
+      "1/1 [==============================] - 0s 48ms/step - loss: 0.3865 - acc: 0.9636 - val_loss: 1.2821 - val_acc: 0.4444\n",
+      "Epoch 48/150\n",
+      "1/1 [==============================] - 0s 48ms/step - loss: 1.1136 - acc: 0.5091 - val_loss: 1.2653 - val_acc: 0.5556\n",
+      "Epoch 49/150\n",
+      "1/1 [==============================] - 0s 56ms/step - loss: 1.4328 - acc: 0.4909 - val_loss: 0.8342 - val_acc: 0.4444\n",
+      "Epoch 50/150\n",
+      "1/1 [==============================] - 0s 51ms/step - loss: 0.7507 - acc: 0.5091 - val_loss: 0.5658 - val_acc: 1.0000\n",
+      "Epoch 51/150\n",
+      "1/1 [==============================] - 0s 50ms/step - loss: 0.5570 - acc: 0.9273 - val_loss: 0.3461 - val_acc: 1.0000\n",
+      "Epoch 52/150\n",
+      "1/1 [==============================] - 0s 49ms/step - loss: 0.3835 - acc: 0.9455 - val_loss: 2.6240 - val_acc: 0.4444\n",
+      "Epoch 53/150\n",
+      "1/1 [==============================] - 0s 45ms/step - loss: 2.2489 - acc: 0.5091 - val_loss: 0.6370 - val_acc: 0.5556\n",
+      "Epoch 54/150\n",
+      "1/1 [==============================] - 0s 49ms/step - loss: 0.6616 - acc: 0.5091 - val_loss: 1.9845 - val_acc: 0.4444\n",
+      "Epoch 55/150\n",
+      "1/1 [==============================] - 0s 51ms/step - loss: 1.5985 - acc: 0.5091 - val_loss: 0.8373 - val_acc: 0.5556\n",
+      "Epoch 56/150\n",
+      "1/1 [==============================] - 0s 48ms/step - loss: 0.9484 - acc: 0.4909 - val_loss: 0.7999 - val_acc: 0.4444\n",
+      "Epoch 57/150\n",
+      "1/1 [==============================] - 0s 49ms/step - loss: 0.7114 - acc: 0.5091 - val_loss: 1.4345 - val_acc: 0.5556\n",
+      "Epoch 58/150\n",
+      "1/1 [==============================] - 0s 55ms/step - loss: 2.1682 - acc: 0.4909 - val_loss: 31.0540 - val_acc: 0.4444\n",
+      "Epoch 59/150\n",
+      "1/1 [==============================] - 0s 57ms/step - loss: 27.3452 - acc: 0.5091 - val_loss: 1.6479 - val_acc: 0.5556\n",
+      "Epoch 60/150\n",
+      "1/1 [==============================] - 0s 57ms/step - loss: 1.8433 - acc: 0.4909 - val_loss: 0.8670 - val_acc: 0.4444\n",
+      "Epoch 61/150\n",
+      "1/1 [==============================] - 0s 59ms/step - loss: 0.7444 - acc: 0.5091 - val_loss: 1.2553 - val_acc: 0.5556\n",
+      "Epoch 62/150\n",
+      "1/1 [==============================] - 0s 51ms/step - loss: 1.5007 - acc: 0.4909 - val_loss: 2.2075 - val_acc: 0.4444\n",
+      "Epoch 63/150\n",
+      "1/1 [==============================] - 0s 48ms/step - loss: 1.8793 - acc: 0.5091 - val_loss: 3.3015 - val_acc: 0.5556\n",
+      "Epoch 64/150\n",
+      "1/1 [==============================] - 0s 46ms/step - loss: 4.0625 - acc: 0.4909 - val_loss: 0.9948 - val_acc: 0.4444\n",
+      "Epoch 65/150\n",
+      "1/1 [==============================] - 0s 48ms/step - loss: 0.8742 - acc: 0.5091 - val_loss: 5.8501 - val_acc: 0.5556\n",
+      "Epoch 66/150\n",
+      "1/1 [==============================] - 0s 46ms/step - loss: 6.7076 - acc: 0.4909 - val_loss: 0.7257 - val_acc: 0.4444\n",
+      "Epoch 67/150\n",
+      "1/1 [==============================] - 0s 47ms/step - loss: 0.6767 - acc: 0.5091 - val_loss: 0.5136 - val_acc: 0.5556\n",
+      "Epoch 68/150\n",
+      "1/1 [==============================] - 0s 47ms/step - loss: 0.6693 - acc: 0.4909 - val_loss: 1.9919 - val_acc: 0.4444\n",
+      "Epoch 69/150\n",
+      "1/1 [==============================] - 0s 54ms/step - loss: 1.7509 - acc: 0.5091 - val_loss: 1.0900 - val_acc: 0.5556\n",
+      "Epoch 70/150\n",
+      "1/1 [==============================] - 0s 58ms/step - loss: 1.1162 - acc: 0.4909 - val_loss: 0.5361 - val_acc: 0.6667\n",
+      "Epoch 71/150\n",
+      "1/1 [==============================] - 0s 61ms/step - loss: 0.6112 - acc: 0.5273 - val_loss: 0.6292 - val_acc: 0.4444\n",
+      "Epoch 72/150\n",
+      "1/1 [==============================] - 0s 58ms/step - loss: 0.6091 - acc: 0.5091 - val_loss: 0.9855 - val_acc: 0.5556\n",
+      "Epoch 73/150\n",
+      "1/1 [==============================] - 0s 48ms/step - loss: 1.2277 - acc: 0.4909 - val_loss: 0.5374 - val_acc: 1.0000\n",
+      "Epoch 74/150\n",
+      "1/1 [==============================] - 0s 47ms/step - loss: 0.5515 - acc: 0.9273 - val_loss: 0.3882 - val_acc: 1.0000\n",
+      "Epoch 75/150\n",
+      "1/1 [==============================] - 0s 48ms/step - loss: 0.4105 - acc: 1.0000 - val_loss: 0.2941 - val_acc: 1.0000\n",
+      "Epoch 76/150\n",
+      "1/1 [==============================] - 0s 49ms/step - loss: 0.3066 - acc: 0.9818 - val_loss: 0.4058 - val_acc: 0.6667\n",
+      "Epoch 77/150\n",
+      "1/1 [==============================] - 0s 50ms/step - loss: 0.5610 - acc: 0.5818 - val_loss: 2.0341 - val_acc: 0.4444\n",
+      "Epoch 78/150\n",
+      "1/1 [==============================] - 0s 50ms/step - loss: 1.8288 - acc: 0.5091 - val_loss: 0.3859 - val_acc: 1.0000\n",
+      "Epoch 79/150\n",
+      "1/1 [==============================] - 0s 49ms/step - loss: 0.5038 - acc: 0.6364 - val_loss: 0.4217 - val_acc: 1.0000\n",
+      "Epoch 80/150\n",
+      "1/1 [==============================] - 0s 47ms/step - loss: 0.4362 - acc: 1.0000 - val_loss: 1.3000 - val_acc: 0.5556\n",
+      "Epoch 81/150\n",
+      "1/1 [==============================] - 0s 62ms/step - loss: 1.9887 - acc: 0.4909 - val_loss: 1.1026 - val_acc: 0.4444\n",
+      "Epoch 82/150\n",
+      "1/1 [==============================] - 0s 50ms/step - loss: 0.9565 - acc: 0.5091 - val_loss: 0.6159 - val_acc: 0.4444\n",
+      "Epoch 83/150\n",
+      "1/1 [==============================] - 0s 47ms/step - loss: 0.6157 - acc: 0.6909 - val_loss: 0.2191 - val_acc: 1.0000\n",
+      "Epoch 84/150\n",
+      "1/1 [==============================] - 0s 47ms/step - loss: 0.3211 - acc: 0.9818 - val_loss: 1.7307 - val_acc: 0.4444\n",
+      "Epoch 85/150\n",
+      "1/1 [==============================] - 0s 68ms/step - loss: 1.5347 - acc: 0.5091 - val_loss: 0.4815 - val_acc: 1.0000\n",
+      "Epoch 86/150\n",
+      "1/1 [==============================] - 0s 47ms/step - loss: 0.5681 - acc: 0.8000 - val_loss: 0.1289 - val_acc: 1.0000\n",
+      "Epoch 87/150\n",
+      "1/1 [==============================] - 0s 52ms/step - loss: 0.2729 - acc: 0.9091 - val_loss: 0.5549 - val_acc: 0.5556\n",
+      "Epoch 88/150\n",
+      "1/1 [==============================] - 0s 46ms/step - loss: 0.6969 - acc: 0.7273 - val_loss: 4.6701 - val_acc: 0.5556\n",
+      "Epoch 89/150\n",
+      "1/1 [==============================] - 0s 50ms/step - loss: 6.0015 - acc: 0.4909 - val_loss: 0.7380 - val_acc: 0.4444\n",
+      "Epoch 90/150\n",
+      "1/1 [==============================] - 0s 46ms/step - loss: 0.6583 - acc: 0.5091 - val_loss: 0.6981 - val_acc: 0.5556\n",
+      "Epoch 91/150\n",
+      "1/1 [==============================] - 0s 46ms/step - loss: 1.0240 - acc: 0.4909 - val_loss: 3.2355 - val_acc: 0.4444\n",
+      "Epoch 92/150\n",
+      "1/1 [==============================] - 0s 51ms/step - loss: 3.0298 - acc: 0.5091 - val_loss: 0.4480 - val_acc: 1.0000\n",
+      "Epoch 93/150\n",
+      "1/1 [==============================] - 0s 47ms/step - loss: 0.5889 - acc: 0.6545 - val_loss: 0.2015 - val_acc: 1.0000\n",
+      "Epoch 94/150\n",
+      "1/1 [==============================] - 0s 52ms/step - loss: 0.3743 - acc: 0.8182 - val_loss: 0.0988 - val_acc: 1.0000\n",
+      "Epoch 95/150\n",
+      "1/1 [==============================] - 0s 47ms/step - loss: 0.1720 - acc: 0.9818 - val_loss: 0.0055 - val_acc: 1.0000\n",
+      "Epoch 96/150\n",
+      "1/1 [==============================] - 0s 47ms/step - loss: 0.0554 - acc: 1.0000 - val_loss: 5.3639 - val_acc: 0.4444\n",
+      "Epoch 97/150\n",
+      "1/1 [==============================] - 0s 53ms/step - loss: 4.7347 - acc: 0.5091 - val_loss: 0.1757 - val_acc: 1.0000\n",
+      "Epoch 98/150\n",
+      "1/1 [==============================] - 0s 46ms/step - loss: 0.4989 - acc: 0.6545 - val_loss: 0.3692 - val_acc: 0.8889\n",
+      "Epoch 99/150\n",
+      "1/1 [==============================] - 0s 46ms/step - loss: 0.4229 - acc: 0.9818 - val_loss: 0.1220 - val_acc: 1.0000\n",
+      "Epoch 100/150\n",
+      "1/1 [==============================] - 0s 46ms/step - loss: 0.1708 - acc: 1.0000 - val_loss: 0.5889 - val_acc: 0.5556\n",
+      "Epoch 101/150\n",
+      "1/1 [==============================] - 0s 47ms/step - loss: 0.6792 - acc: 0.6909 - val_loss: 7.3951 - val_acc: 0.5556\n",
+      "Epoch 102/150\n",
+      "1/1 [==============================] - 0s 53ms/step - loss: 9.1840 - acc: 0.4909 - val_loss: 0.4706 - val_acc: 0.4444\n",
+      "Epoch 103/150\n",
+      "1/1 [==============================] - 0s 55ms/step - loss: 0.5305 - acc: 0.5455 - val_loss: 0.0835 - val_acc: 1.0000\n",
+      "Epoch 104/150\n",
+      "1/1 [==============================] - 0s 47ms/step - loss: 0.3087 - acc: 0.8364 - val_loss: 0.6441 - val_acc: 0.4444\n",
+      "Epoch 105/150\n",
+      "1/1 [==============================] - 0s 47ms/step - loss: 0.6064 - acc: 0.5091 - val_loss: 0.0214 - val_acc: 1.0000\n",
+      "Epoch 106/150\n",
+      "1/1 [==============================] - 0s 49ms/step - loss: 0.6859 - acc: 0.6545 - val_loss: 0.4048 - val_acc: 0.6667\n",
+      "Epoch 107/150\n",
+      "1/1 [==============================] - 0s 50ms/step - loss: 0.3453 - acc: 0.8909 - val_loss: 0.0654 - val_acc: 1.0000\n",
+      "Epoch 108/150\n",
+      "1/1 [==============================] - 0s 46ms/step - loss: 0.0774 - acc: 1.0000 - val_loss: 0.0253 - val_acc: 1.0000\n",
+      "Epoch 109/150\n",
+      "1/1 [==============================] - 0s 46ms/step - loss: 0.0238 - acc: 1.0000 - val_loss: 0.0126 - val_acc: 1.0000\n",
+      "Epoch 110/150\n",
+      "1/1 [==============================] - 0s 46ms/step - loss: 0.0119 - acc: 1.0000 - val_loss: 0.0079 - val_acc: 1.0000\n",
+      "Epoch 111/150\n",
+      "1/1 [==============================] - 0s 60ms/step - loss: 0.0076 - acc: 1.0000 - val_loss: 0.0054 - val_acc: 1.0000\n",
+      "Epoch 112/150\n",
+      "1/1 [==============================] - 0s 60ms/step - loss: 0.0053 - acc: 1.0000 - val_loss: 0.0038 - val_acc: 1.0000\n",
+      "Epoch 113/150\n",
+      "1/1 [==============================] - 0s 46ms/step - loss: 0.0038 - acc: 1.0000 - val_loss: 0.0028 - val_acc: 1.0000\n",
+      "Epoch 114/150\n",
+      "1/1 [==============================] - 0s 60ms/step - loss: 0.0028 - acc: 1.0000 - val_loss: 0.0020 - val_acc: 1.0000\n",
+      "Epoch 115/150\n",
+      "1/1 [==============================] - 0s 58ms/step - loss: 0.0020 - acc: 1.0000 - val_loss: 0.0012 - val_acc: 1.0000\n",
+      "Epoch 116/150\n",
+      "1/1 [==============================] - 0s 52ms/step - loss: 0.0012 - acc: 1.0000 - val_loss: 4.7124e-04 - val_acc: 1.0000\n",
+      "Epoch 117/150\n",
+      "1/1 [==============================] - 0s 55ms/step - loss: 4.9879e-04 - acc: 1.0000 - val_loss: 1.7736e-04 - val_acc: 1.0000\n",
+      "Epoch 118/150\n",
+      "1/1 [==============================] - 0s 48ms/step - loss: 2.0493e-04 - acc: 1.0000 - val_loss: 8.4963e-05 - val_acc: 1.0000\n",
+      "Epoch 119/150\n",
+      "1/1 [==============================] - 0s 49ms/step - loss: 1.0330e-04 - acc: 1.0000 - val_loss: 5.2292e-05 - val_acc: 1.0000\n",
+      "Epoch 120/150\n",
+      "1/1 [==============================] - 0s 49ms/step - loss: 6.5227e-05 - acc: 1.0000 - val_loss: 3.5806e-05 - val_acc: 1.0000\n",
+      "Epoch 121/150\n",
+      "1/1 [==============================] - 0s 64ms/step - loss: 4.6158e-05 - acc: 1.0000 - val_loss: 2.6082e-05 - val_acc: 1.0000\n",
+      "Epoch 122/150\n",
+      "1/1 [==============================] - 0s 46ms/step - loss: 3.4608e-05 - acc: 1.0000 - val_loss: 2.0169e-05 - val_acc: 1.0000\n",
+      "Epoch 123/150\n",
+      "1/1 [==============================] - 0s 49ms/step - loss: 2.7225e-05 - acc: 1.0000 - val_loss: 1.6119e-05 - val_acc: 1.0000\n",
+      "Epoch 124/150\n",
+      "1/1 [==============================] - 0s 46ms/step - loss: 2.2106e-05 - acc: 1.0000 - val_loss: 1.3109e-05 - val_acc: 1.0000\n",
+      "Epoch 125/150\n",
+      "1/1 [==============================] - 0s 47ms/step - loss: 1.8185e-05 - acc: 1.0000 - val_loss: 1.0645e-05 - val_acc: 1.0000\n",
+      "Epoch 126/150\n",
+      "1/1 [==============================] - 0s 48ms/step - loss: 1.5021e-05 - acc: 1.0000 - val_loss: 8.8892e-06 - val_acc: 1.0000\n",
+      "Epoch 127/150\n",
+      "1/1 [==============================] - 0s 48ms/step - loss: 1.2678e-05 - acc: 1.0000 - val_loss: 7.5465e-06 - val_acc: 1.0000\n",
+      "Epoch 128/150\n",
+      "1/1 [==============================] - 0s 47ms/step - loss: 1.0886e-05 - acc: 1.0000 - val_loss: 6.5007e-06 - val_acc: 1.0000\n",
+      "Epoch 129/150\n",
+      "1/1 [==============================] - 0s 48ms/step - loss: 9.4708e-06 - acc: 1.0000 - val_loss: 5.6632e-06 - val_acc: 1.0000\n",
+      "Epoch 130/150\n",
+      "1/1 [==============================] - 0s 47ms/step - loss: 8.3282e-06 - acc: 1.0000 - val_loss: 4.9912e-06 - val_acc: 1.0000\n",
+      "Epoch 131/150\n",
+      "1/1 [==============================] - 0s 45ms/step - loss: 7.3885e-06 - acc: 1.0000 - val_loss: 4.4307e-06 - val_acc: 1.0000\n",
+      "Epoch 132/150\n",
+      "1/1 [==============================] - 0s 47ms/step - loss: 6.5804e-06 - acc: 1.0000 - val_loss: 3.9579e-06 - val_acc: 1.0000\n",
+      "Epoch 133/150\n",
+      "1/1 [==============================] - 0s 50ms/step - loss: 5.8918e-06 - acc: 1.0000 - val_loss: 3.5523e-06 - val_acc: 1.0000\n",
+      "Epoch 134/150\n",
+      "1/1 [==============================] - 0s 48ms/step - loss: 5.3042e-06 - acc: 1.0000 - val_loss: 3.2108e-06 - val_acc: 1.0000\n",
+      "Epoch 135/150\n",
+      "1/1 [==============================] - 0s 47ms/step - loss: 4.7924e-06 - acc: 1.0000 - val_loss: 2.9238e-06 - val_acc: 1.0000\n",
+      "Epoch 136/150\n",
+      "1/1 [==============================] - 0s 51ms/step - loss: 4.3552e-06 - acc: 1.0000 - val_loss: 2.6652e-06 - val_acc: 1.0000\n",
+      "Epoch 137/150\n",
+      "1/1 [==============================] - 0s 47ms/step - loss: 3.9738e-06 - acc: 1.0000 - val_loss: 2.4396e-06 - val_acc: 1.0000\n",
+      "Epoch 138/150\n",
+      "1/1 [==============================] - 0s 46ms/step - loss: 3.6392e-06 - acc: 1.0000 - val_loss: 2.2305e-06 - val_acc: 1.0000\n",
+      "Epoch 139/150\n",
+      "1/1 [==============================] - 0s 46ms/step - loss: 3.3406e-06 - acc: 1.0000 - val_loss: 2.0405e-06 - val_acc: 1.0000\n",
+      "Epoch 140/150\n",
+      "1/1 [==============================] - 0s 47ms/step - loss: 3.0710e-06 - acc: 1.0000 - val_loss: 1.8748e-06 - val_acc: 1.0000\n",
+      "Epoch 141/150\n",
+      "1/1 [==============================] - 0s 45ms/step - loss: 2.8294e-06 - acc: 1.0000 - val_loss: 1.7256e-06 - val_acc: 1.0000\n",
+      "Epoch 142/150\n",
+      "1/1 [==============================] - 0s 50ms/step - loss: 2.6120e-06 - acc: 1.0000 - val_loss: 1.5876e-06 - val_acc: 1.0000\n",
+      "Epoch 143/150\n",
+      "1/1 [==============================] - 0s 61ms/step - loss: 2.4154e-06 - acc: 1.0000 - val_loss: 1.4660e-06 - val_acc: 1.0000\n",
+      "Epoch 144/150\n",
+      "1/1 [==============================] - 0s 49ms/step - loss: 2.2354e-06 - acc: 1.0000 - val_loss: 1.3550e-06 - val_acc: 1.0000\n",
+      "Epoch 145/150\n",
+      "1/1 [==============================] - 0s 51ms/step - loss: 2.0710e-06 - acc: 1.0000 - val_loss: 1.2531e-06 - val_acc: 1.0000\n",
+      "Epoch 146/150\n",
+      "1/1 [==============================] - 0s 48ms/step - loss: 1.9199e-06 - acc: 1.0000 - val_loss: 1.1608e-06 - val_acc: 1.0000\n",
+      "Epoch 147/150\n",
+      "1/1 [==============================] - 0s 47ms/step - loss: 1.7817e-06 - acc: 1.0000 - val_loss: 1.0770e-06 - val_acc: 1.0000\n",
+      "Epoch 148/150\n",
+      "1/1 [==============================] - 0s 46ms/step - loss: 1.6546e-06 - acc: 1.0000 - val_loss: 1.0011e-06 - val_acc: 1.0000\n",
+      "Epoch 149/150\n",
+      "1/1 [==============================] - 0s 46ms/step - loss: 1.5400e-06 - acc: 1.0000 - val_loss: 9.3172e-07 - val_acc: 1.0000\n",
+      "Epoch 150/150\n",
+      "1/1 [==============================] - 0s 46ms/step - loss: 1.4358e-06 - acc: 1.0000 - val_loss: 8.6691e-07 - val_acc: 1.0000\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1080x360 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import os\n",
+    "import glob\n",
+    "from keras.models import Sequential\n",
+    "from keras.layers.core import Flatten, Dense, Dropout\n",
+    "from keras.layers.convolutional import Convolution2D, MaxPooling2D, ZeroPadding2D\n",
+    "from keras.optimizers import SGD\n",
+    "import numpy as np\n",
+    "from pandas import Series, DataFrame\n",
+    "from keras.preprocessing.image import ImageDataGenerator\n",
+    "from keras.models import Sequential\n",
+    "from keras.layers.core import Dense, Dropout, Activation, Flatten\n",
+    "from keras.layers.advanced_activations import PReLU\n",
+    "from keras.layers.convolutional import Convolution2D, MaxPooling2D\n",
+    "from keras.optimizers import SGD, Adadelta, Adagrad\n",
+    "from keras.utils import np_utils, generic_utils, plot_model\n",
+    "from six.moves import range\n",
+    "import os\n",
+    "from PIL import Image\n",
+    "import numpy as np\n",
+    "import cv2\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "def load_data():\n",
+    "    path = \"D:\\cat\\91\"\n",
+    "    files = os.listdir(path)\n",
+    "    print(len(files))\n",
+    "    images = []\n",
+    "    labels = []\n",
+    "    num = 64\n",
+    "    t = 0\n",
+    "    for f in files:\n",
+    "        img_path = path + '/' + f\n",
+    "        j = f.split(\".\")[0]\n",
+    "        if '_' in j:\n",
+    "            j = int(j.split(\"_\")[0])\n",
+    "        else:\n",
+    "            j = int(j)\n",
+    "\n",
+    "        from keras.preprocessing import image    \n",
+    "        img = image.load_img(img_path, target_size=(128, 128))\n",
+    "        img_array = image.img_to_array(img)\n",
+    "        images.append(img_array)\n",
+    "        if j > 8:\n",
+    "            labels.append(1)\n",
+    "        else:\n",
+    "            labels.append(0)\n",
+    "        t += 1\n",
+    " \n",
+    "    data = np.array(images)\n",
+    "    labels = np.array(labels)\n",
+    " \n",
+    "    # label = np_utils.to_categorical(labels, 2)\n",
+    "    return data, labels ,images\n",
+    "\n",
+    "data,label,images = load_data()\n",
+    "\n",
+    "print(data.shape)\n",
+    "train_data = data[:55]\n",
+    "train_labels = label[:55]\n",
+    "validation_data = data[55:]\n",
+    "validation_labels = label[55:]\n",
+    "# images = images / 255\n",
+    "for i in range(len(images)):\n",
+    "    if i>55:\n",
+    "        plt.imsave(r\"D:\\cat\\96\\1\\validation_{}.jpg\".format(i), images[i]/255)\n",
+    "    else:\n",
+    "        plt.imsave(r\"D:\\cat\\96\\2\\train_{}.jpg\".format(i), images[i]/255)    \n",
+    "        \n",
+    "model = Sequential()\n",
+    "model.add(Convolution2D(32, (3, 3), activation='relu', input_shape=(128, 128, 3)))\n",
+    "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
+    "model.add(Convolution2D(64, (3, 3), activation='relu'))\n",
+    "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
+    "model.add(Convolution2D(128, (3, 3), activation='relu'))\n",
+    "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
+    "model.add(Convolution2D(128, (3, 3), activation='relu'))\n",
+    "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
+    "model.add(Flatten())\n",
+    "model.add(Dense(512, activation='relu'))\n",
+    "model.add(Dense(1, activation='sigmoid'))\n",
+    "model.compile(loss='binary_crossentropy', optimizer='rmsprop', metrics=['acc'])\n",
+    "\n",
+    "history = model.fit(train_data, train_labels,\n",
+    "         epochs=150, batch_size=100,\n",
+    "         validation_data=(validation_data, validation_labels))\n",
+    "\n",
+    "# model.save('cats_and_dogs_small_1.h5')\n",
+    "# os.environ[\"PATH\"]+=os.pathsep + \"D:/graphviz-2.38/release/bin/\"\n",
+    "# plot_model(model, to_file='model.png', show_shapes=True)\n",
+    "\n",
+    "def plot_acc_loss_curve(history):\n",
+    "    # 显示训练集和验证集的acc和loss曲线\n",
+    "    from matplotlib import pyplot as plt\n",
+    "    acc = history.history['acc']\n",
+    "    val_acc = history.history['val_acc']\n",
+    "    loss = history.history['loss']\n",
+    "    val_loss = history.history['val_loss']\n",
+    "\n",
+    "    plt.figure(figsize=(15, 5))\n",
+    "    plt.subplot(1, 2, 1)\n",
+    "    plt.plot(acc, label='Training Accuracy')\n",
+    "    plt.plot(val_acc, label='Validation Accuracy')\n",
+    "    plt.title('Training and Validation Accuracy')\n",
+    "    plt.legend()\n",
+    "    #plt.grid()\n",
+    "\n",
+    "    plt.subplot(1, 2, 2)\n",
+    "    plt.plot(loss, label='Training Loss')\n",
+    "    plt.plot(val_loss, label='Validation Loss')\n",
+    "    plt.title('Training and Validation Loss')\n",
+    "    plt.legend()\n",
+    "    #plt.grid()\n",
+    "    plt.show()\n",
+    "\n",
+    "plot_acc_loss_curve(history)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}