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使用 TensorFlow Estimators 和 TensorFlow Hub 对酒店评论进行情绪分类
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| { | |
| "cells": [ | |
| { | |
| "cell_type": "code", | |
| "execution_count": 1, | |
| "metadata": { | |
| "colab": {}, | |
| "colab_type": "code", | |
| "id": "4cx-1AuMzYCX" | |
| }, | |
| "outputs": [ | |
| { | |
| "name": "stderr", | |
| "output_type": "stream", | |
| "text": [ | |
| "C:\\Users\\secsi\\AppData\\Local\\Continuum\\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", | |
| " from ._conv import register_converters as _register_converters\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "from pathlib import Path\n", | |
| "\n", | |
| "import matplotlib.pyplot as plt\n", | |
| "import numpy as np\n", | |
| "import pandas as pd\n", | |
| "import seaborn as sns\n", | |
| "import tensorflow as tf\n", | |
| "import tensorflow_hub as hub\n", | |
| "from sklearn.model_selection import train_test_split\n", | |
| "%matplotlib inline" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 2, | |
| "metadata": { | |
| "colab": {}, | |
| "colab_type": "code", | |
| "id": "H7USxnbI7xLu" | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "sns.set(rc={'figure.figsize': (10, 8)})" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 3, | |
| "metadata": { | |
| "colab": {}, | |
| "colab_type": "code", | |
| "id": "WTDdU2f_2u3e" | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "pos = Path('pos.txt').read_text(encoding='utf8').split('\\n')\n", | |
| "neg = Path('neg.txt').read_text(encoding='utf8').split('\\n')\n", | |
| "pos = list(filter(None, pos))\n", | |
| "neg = list(filter(None, neg))" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 4, | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 52 | |
| }, | |
| "colab_type": "code", | |
| "id": "maCqkZQl3FTe", | |
| "outputId": "3e8df2f0-57b0-4773-967c-921d1f411e9a" | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/plain": [ | |
| "['1 距离川沙公路较近,但是公交指示不对,如果是\"蔡陆线\"的话,会非常麻烦.建议用别的路线.房间较为简单.',\n", | |
| " '1 商务大床房，房间很大，床有2M宽，整体感觉经济实惠不错!']" | |
| ] | |
| }, | |
| "execution_count": 4, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "pos[:2]" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 5, | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 72 | |
| }, | |
| "colab_type": "code", | |
| "id": "yIOUD4tA3e8O", | |
| "outputId": "756abd5d-c6d4-4685-a0bb-7b1e31d22e8c" | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/plain": [ | |
| "['-1 标准间太差房间还不如3星的而且设施非常陈旧.建议酒店把老的标准间从新改善.',\n", | |
| " '-1 服务态度极其差，前台接待好象没有受过培训，连基本的礼貌都不懂，竟然同时接待几个客人；大堂副理更差，跟客人辩解个没完，要总经理的电话投诉竟然都不敢给。要是没有作什么亏心事情，跟本不用这么怕。']" | |
| ] | |
| }, | |
| "execution_count": 5, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "neg[:2]" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 6, | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 35 | |
| }, | |
| "colab_type": "code", | |
| "id": "TDeVsJbg3geG", | |
| "outputId": "ffcae6cc-9bc2-4391-c359-cff2cb9c1133" | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/plain": [ | |
| "'积极样本数：7000，消极样本数：3000'" | |
| ] | |
| }, | |
| "execution_count": 6, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "f'积极样本数：{len(pos)}，消极样本数：{len(neg)}'" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 7, | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 219 | |
| }, | |
| "colab_type": "code", | |
| "id": "qUjjOpYR33K2", | |
| "outputId": "5fd773c6-bab6-400b-dad8-30b099a5879e" | |
| }, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "(10000, 2)\n" | |
| ] | |
| }, | |
| { | |
| "data": { | |
| "text/html": [ | |
| "<div>\n", | |
| "<style scoped>\n", | |
| " .dataframe tbody tr th:only-of-type {\n", | |
| " vertical-align: middle;\n", | |
| " }\n", | |
| "\n", | |
| " .dataframe tbody tr th {\n", | |
| " vertical-align: top;\n", | |
| " }\n", | |
| "\n", | |
| " .dataframe thead th {\n", | |
| " text-align: right;\n", | |
| " }\n", | |
| "</style>\n", | |
| "<table border=\"1\" class=\"dataframe\">\n", | |
| " <thead>\n", | |
| " <tr style=\"text-align: right;\">\n", | |
| " <th></th>\n", | |
| " <th>comment</th>\n", | |
| " <th>label</th>\n", | |
| " </tr>\n", | |
| " </thead>\n", | |
| " <tbody>\n", | |
| " <tr>\n", | |
| " <th>0</th>\n", | |
| " <td>距离川沙公路较近,但是公交指示不对,如果是\"蔡陆线\"的话,会非常麻烦.建议用别的路线.房间较...</td>\n", | |
| " <td>1</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>1</th>\n", | |
| " <td>商务大床房，房间很大，床有2M宽，整体感觉经济实惠不错!</td>\n", | |
| " <td>1</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>2</th>\n", | |
| " <td>早餐太差，无论去多少人，那边也不加食品的。酒店应该重视一下这个问题了。房间本身很好。</td>\n", | |
| " <td>1</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>3</th>\n", | |
| " <td>宾馆在小街道上，不大好找，但还好北京热心同胞很多~宾馆设施跟介绍的差不多，房间很小，确实挺小...</td>\n", | |
| " <td>1</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>4</th>\n", | |
| " <td>CBD中心,周围没什么店铺,说5星有点勉强.不知道为什么卫生间没有电吹风</td>\n", | |
| " <td>1</td>\n", | |
| " </tr>\n", | |
| " </tbody>\n", | |
| "</table>\n", | |
| "</div>" | |
| ], | |
| "text/plain": [ | |
| " comment label\n", | |
| "0 距离川沙公路较近,但是公交指示不对,如果是\"蔡陆线\"的话,会非常麻烦.建议用别的路线.房间较... 1\n", | |
| "1 商务大床房，房间很大，床有2M宽，整体感觉经济实惠不错! 1\n", | |
| "2 早餐太差，无论去多少人，那边也不加食品的。酒店应该重视一下这个问题了。房间本身很好。 1\n", | |
| "3 宾馆在小街道上，不大好找，但还好北京热心同胞很多~宾馆设施跟介绍的差不多，房间很小，确实挺小... 1\n", | |
| "4 CBD中心,周围没什么店铺,说5星有点勉强.不知道为什么卫生间没有电吹风 1" | |
| ] | |
| }, | |
| "execution_count": 7, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "pos_text = list(map(lambda x: x[1:].strip(), pos))\n", | |
| "pos_label = [1] * len(pos_text)\n", | |
| "neg_text = list(map(lambda x: x[1:].strip(), neg))\n", | |
| "neg_label = [0] * len(neg_text)\n", | |
| "\n", | |
| "dataset = pd.DataFrame({'comment': pos_text + neg_text, 'label': pos_label + neg_label}, columns=['comment', 'label'])\n", | |
| "print(dataset.shape)\n", | |
| "dataset.head()" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 8, | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 496 | |
| }, | |
| "colab_type": "code", | |
| "id": "K1EUC3DV3_HQ", | |
| "outputId": "7affdd86-4367-40c8-ca61-41e972b9f7ae" | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/plain": [ | |
| "<matplotlib.axes._subplots.AxesSubplot at 0x1e2b9dcfc88>" | |
| ] | |
| }, | |
| "execution_count": 8, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| }, | |
| { | |
| "data": { | |
| "image/png": 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\n", | |
| "text/plain": [ | |
| "<Figure size 720x576 with 1 Axes>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "dataset.label.value_counts().plot(kind='bar')" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 9, | |
| "metadata": { | |
| "colab": {}, | |
| "colab_type": "code", | |
| "id": "4bK_bTB6773U" | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "train_df, test_df = train_test_split(dataset, test_size=0.3, random_state=73)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 10, | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 640 | |
| }, | |
| "colab_type": "code", | |
| "id": "pT0EBLjV4AEO", | |
| "outputId": "a13e7843-52d1-4aa8-fe21-ae272db5d688" | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "image/png": 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2bfa7AwD8Dj355JM5+eST+1//6Z/+aUaNGpXLL78811xzTf72b/82w4cPz4wZM7J48eJMnDgxe/fuTWdnZx2nBgAa3SG3lX3729/O8OHDc/755+erX/1qkhx0D3tPT0+GDh3a/76Xj9vvDq+dPb+UymcfXt1JJ52Ue++9t//1d7/73QPWfPrTnz7g2OjRo3P33Xe/rrMBAEePQ8ah++67L5VKJevWrctjjz2WGTNmZNu2bf0/f3kPe0tLS3p7e/c73traar87/Bbs+aVE9ruXSxQEAGgch9xW9o1vfCN33313li9fnre+9a254447Mn78+AP2sI8bNy5r1qxJtVrNli1bUq1WM3z4cPvdAQAAABrcb/2r7GfMmJHZs2enq6srY8aMSWdnZ5qamtLe3p6JEyemWq1mzpw5r7oWAAAAgMZRqdVqtXoP8Uqlby+YuuiH9R6BOvn6zRfUewQ44mwrK5dtZY2n5GvR/Ve53H9RKvdgZfr/+lX2AAAAABy9xCEAAACAgolDAAAAAAUThwAAAAAKJg4BAAAAFEwcAgAAACiYOAQAAABQMHEIAAAAoGDiEAAAAEDBxCEAAACAgolDAAAAAAUThwAAAAAKJg4BAAAAFEwcAgAAACiYOAQAAABQMHEIAAAAoGDiEAAAAEDBxCEAAACAgolDAAAAAAUThwAAAAAKJg4BAAAAFEwcAgAAACiYOAQAAABQMHEIAAAAoGDiEAAAAEDBxCEAAACAgolDAAAAAAUThwAAAAAKJg4BAAAAFEwcAgAAACiYOAQAAABQMHEIAAAAoGDiEAAAAEDBxCEAAACAgolDAAAAAAUThwAAAAAKJg4BAAAAFEwcAgAAACiYOAQAAABQMHEIAAAAoGDiEAAAAEDBxCEAAACAgolDAAAAAAUThwAAAAAKJg4BAAAAFEwcAgAAACiYOAQAAABQMHEIAAAAoGDiEAAAAEDBxCEAgAb18MMP58orr0ySdHd35/zzz8+VV16ZK6+8Mt/73veSJEuWLMmECRMyadKkPPLII0mSzZs3Z/LkyZkyZUrmzp2barVat3MAABpfc70HAADgQMuWLcv999+f448/Pkny05/+NFdffXWmTp3av6a7uzsbNmzIypUr89xzz2XatGm57777snDhwkyfPj1vf/vbM2fOnKxatSoXXXRRvU4FAGhwnhwCAGhAp5xyShYvXtz/+tFHH82DDz6YK664IjNnzkxPT082btyYjo6OVCqVjBw5Mn19fdm2bVu6u7tz7rnnJknGjx+ftWvX1us0AIABwJNDAAANqLOzM88880z/67POOiuXXXZZzjjjjNx555358pe/nNbW1gwdOrR/zZAhQ7Jz587UarVUKpX9jh3OsGGD09zc9Ls/EWhgI0a01nsEqBuff15JHAIAGAAuuuiitLW19f/3/Pnzc+GFF6a3t7d/TW9vb1pbWzNo0KD9jr38vkPZvn3X735oaHBbtx4+nMLRaMSIVp//Ah0qCNpWBgAwAFxzzTX9Xzi9bt26nH766Rk3blzWrFmTarWaLVu2pFqtZvjw4Rk7dmzWr1+fJFm9enXa29vrOToA0OA8OQQAMADceuutmT9/fo455piccMIJmT9/flpaWtLe3p6JEyemWq1mzpw5SZIZM2Zk9uzZ6erqypgxY9LZ2Vnn6QGARlap1Wq1eg/xSqU/2jZ10Q/rPQJ18vWbL6j3CHDEeaS5XL7noPGUfC26/yqX+y9K5R6sTLaVAQAAAHBQ4hAAAABAwcQhAAAAgIKJQwAAAAAFE4cAAAAACiYOAQAAABRMHAIAAAAomDgEAAAAUDBxCAAAAKBg4hAAAABAwcQhAAAAgIKJQwAAAAAFE4cAAAAACiYOAQAAABRMHAIAAAAomDgEAAAAUDBxCAAAAKBg4hAAAABAwcQhAAAAgIKJQwAAAAAFE4cAAAAACiYOAQAAABRMHAIAAAAomDgEAAAAUDBxCAAAAKBg4hAAAABAwcQhAAAAgIKJQwAAAAAFE4cAAAAACiYOAQAAABRMHAIAAAAomDgEAAAAUDBxCAAAAKBg4hAAAABAwcQhAAAAgIKJQwAAAAAFE4cAAAAACiYOAQAAABRMHAIAAAAomDgEAAAAUDBxCAAAAKBgzYdb0NfXl1mzZuXJJ59MU1NTFi5cmFqtlptvvjmVSiWnnXZa5s6dm0GDBmXJkiV58MEH09zcnJkzZ+ass87K5s2bD7oWAAAAgPo7bKV54IEHkiTf/OY3c8MNN2ThwoVZuHBhpk+fnhUrVqRWq2XVqlXp7u7Ohg0bsnLlynR1dWXevHlJctC1AAAAADSGw8ahd7/73Zk/f36SZMuWLTnhhBPS3d2dc889N0kyfvz4rF27Nhs3bkxHR0cqlUpGjhyZvr6+bNu27aBrAQAAAGgMh91WliTNzc2ZMWNGfvCDH+RLX/pSHnjggVQqlSTJkCFDsnPnzvT09GTo0KH973n5eK1WO2DtoQwbNjjNzU3/1/OBAWvEiNZ6jwB14bMPAAD19ZriUJLccccdufHGG3P55Zdn9+7d/cd7e3vT1taWlpaW9Pb27ne8tbV1v+8XenntoWzfvuu3mR+OGlu3HjqcwtFoxIhWn/1CiYIAAI3jsNvK/vmf/zlf+cpXkiTHH398KpVKzjjjjKxfvz5Jsnr16rS3t2fcuHFZs2ZNqtVqtmzZkmq1muHDh2fs2LEHrAUAAACgMRz2yaH3vOc9ueWWW3LFFVdk3759mTlzZt785jdn9uzZ6erqypgxY9LZ2Zmmpqa0t7dn4sSJqVarmTNnTpJkxowZB6wFAAAAoDFUarVard5DvFLp2wumLvphvUegTr5+8wX1HgGOONvKymVbWeMp+Vp0/1Uu91+Uyj1YmQ51/3XYbWUAAAAAHL3EIQAAAICCiUMAAAAABROHAAAAAAomDgEAAAAU7LC/yh4AgPp4+OGH87nPfS7Lly/PY489lvnz56epqSnHHnts7rjjjpxwwglZsGBBHnrooQwZMiRJsnTp0uzduzc33nhjXnrppZx44olZuHBhjj/++DqfDQDQqDw5BADQgJYtW5ZZs2Zl9+7dSZLbbrsts2fPzvLly3PRRRdl2bJlSZLu7u587Wtfy/Lly7N8+fK0trZm6dKled/73pcVK1Zk7Nixueeee+p5KgBAg/PkEABAAzrllFOyePHifOpTn0qSdHV15cQTT0yS9PX15Q1veEOq1Wo2b96cOXPm5Pnnn8+ECRMyYcKEbNy4MX/1V3+VJBk/fny6urrykY985JB/3rBhg9Pc3PS6nhM0mhEjWus9AtSNzz+vJA4BADSgzs7OPPPMM/2vXw5DDz30UO6+++584xvfyK5du/LhD384V199dfr6+nLVVVfljDPOSE9PT1pb//umf8iQIdm5c+dh/7zt23e9PicCDWzr1sNfG3A0GjGi1ee/QIcKguIQAMAA8b3vfS933nlnvvrVr2b48OH9Qejl7xM677zz8vjjj6elpSW9vb057rjj0tvbm7a2tjpPDgA0Mt85BAAwAHznO9/J3XffneXLl+fkk09Okjz11FOZMmVK+vr6snfv3jz00EM5/fTTM27cuPzoRz9KkqxevTpve9vb6jk6ANDgPDkEANDg+vr6ctttt+VNb3pTpk2bliT54z/+49xwww255JJLcvnll+eYY47J+9///px22mm57rrrMmPGjNx7770ZNmxYPv/5z9f5DACARiYOAQA0qJNOOin33ntvkmTDhg0HXXPttdfm2muv3e/YCSeckH/4h3943ecDAI4OtpUBAAAAFEwcAgAAACiYOAQAAABQMHEIAAAAoGDiEAAAAEDBxCEAAACAgolDAAAAAAUThwAAAAAKJg4BAAAAFEwcAgAAACiYOAQAAABQMHEIAAAAoGDiEAAAAEDBxCEAAACAgolDAAAAAAUThwAAAAAKJg4BAAAAFEwcAgAAACiYOAQAAABQMHEIAAAAoGDiEAAAAEDBxCEAAACAgolDAAAAAAUThwAAAAAKJg4BAAAAFEwcAgAAACiYOAQAAABQMHEIAAAAoGDiEAAAAEDBxCEAAACAgolDAAAAAAUThwAAAAAKJg4BAAAAFEwcAgAAACiYOAQAAABQMHEIAAAAoGDiEAAAAEDBxCEAAACAgolDAAAAAAUThwAAAAAKJg4BAAAAFEwcAgAAACiYOAQAAABQMHEIAAAAoGDiEAAAAEDBxCEAAACAgolDAAAAAAUThwAAAAAKJg4BAAAAFEwcAgAAACiYOAQAAABQMHEIAAAAoGDiEAAAAEDBxCEAgAb18MMP58orr0ySbN68OZMnT86UKVMyd+7cVKvVJMmSJUsyYcKETJo0KY888sgh1wIAHIw4BADQgJYtW5ZZs2Zl9+7dSZKFCxdm+vTpWbFiRWq1WlatWpXu7u5s2LAhK1euTFdXV+bNm/eqawEAXo04BADQgE455ZQsXry4/3V3d3fOPffcJMn48eOzdu3abNy4MR0dHalUKhk5cmT6+vqybdu2g64FAHg1zfUeAACAA3V2duaZZ57pf12r1VKpVJIkQ4YMyc6dO9PT05OhQ4f2r3n5+MHWHs6wYYPT3Nz0Oz4LaGwjRrTWewSoG59/XkkcAgAYAAYN+p8Hvnt7e9PW1paWlpb09vbud7y1tfWgaw9n+/Zdv9uBYQDYuvXw4RSORiNGtPr8F+hQQdC2MgCAAWDs2LFZv359kmT16tVpb2/PuHHjsmbNmlSr1WzZsiXVajXDhw8/6FoAgFfjySEAgAFgxowZmT17drq6ujJmzJh0dnamqakp7e3tmThxYqrVaubMmfOqawEAXk2lVqvV6j3EK5X+aNvURT+s9wjUyddvvqDeI8AR55Hmcvmeg8ZT8rXo/qtc7r8olXuwMtlWBgAAAMBBiUMAAAAABROHAAAAAAomDgEAAAAUTBwCAAAAKJg4BAAAAFAwcQgAAACgYOIQAAAAQMHEIQAAAICCiUMAAAAABROHAAAAAAomDgEAAAAUTBwCAAAAKJg4BAAAAFAwcQgAAACgYM31HgCgZNf/8FP1HoE6+fIFf1/vEQAAIIknhwAAAACKJg4BAAAAFEwcAgAAACiYOAQAAABQMHEIAAAAoGDiEAAAAEDBxCEAAACAgjXXewAAAAA4kq7/4afqPQJ18uUL/r7eIzSkQ8ahvXv3ZubMmXn22WezZ8+eXHfddTn11FNz8803p1Kp5LTTTsvcuXMzaNCgLFmyJA8++GCam5szc+bMnHXWWdm8efNB1wIAAADQGA5Zau6///4MHTo0K1asyLJlyzJ//vwsXLgw06dPz4oVK1Kr1bJq1ap0d3dnw4YNWblyZbq6ujJv3rwkOehaAAAAABrHIePQxRdfnE9+8pP9r5uamtLd3Z1zzz03STJ+/PisXbs2GzduTEdHRyqVSkaOHJm+vr5s27btoGsBAAAAaByH3FY2ZMiQJElPT09uuOGGTJ8+PXfccUcqlUr/z3fu3Jmenp4MHTp0v/ft3LkztVrtgLWHM2zY4DQ3N/2fTwgGqhEjWus9AnAEueYBAGgUh/1C6ueeey7XX399pkyZkksuuSSf/exn+3/W29ubtra2tLS0pLe3d7/jra2t+32/0MtrD2f79l2/7TnAUWHr1sPHU+DoUfo1L44BADSOQ24re/755zN16tTcdNNNmTBhQpJk7NixWb9+fZJk9erVaW9vz7hx47JmzZpUq9Vs2bIl1Wo1w4cPP+haAAAAABrHIZ8cuuuuu7Jjx44sXbo0S5cuTZJ8+tOfzoIFC9LV1ZUxY8aks7MzTU1NaW9vz8SJE1OtVjNnzpwkyYwZMzJ79uz91gIAAADQOA4Zh2bNmpVZs2YdcPzuu+8+4Ni0adMybdq0/Y6NHj36oGsBAAAAaAyH3FYGAAAAwNFNHAIAAAAomDgEAAAAUDBxCAAAAKBg4hAAAABAwcQhAAAAgIKJQwAAAAAFE4cAAAAACiYOAQAAABRMHAIAAAAomDgEAAAAUDBxCAAAAKBg4hAAAABAwcQhAAAAgIKJQwAAAAAFE4cAAAAACiYOAQAAABRMHAIAAAAomDgEAAAAUDBxCAAAAKBg4hAAAABAwZrrPQAAAK/Nt7/97fzTP/1TkmRrB5hKAAAOiUlEQVT37t157LHH8vnPfz5///d/nze96U1JkmnTpqW9vT233nprnnjiiRx77LFZsGBBRo0aVc/RAYAGJg4BAAwQl156aS699NIkybx58/KhD30o3d3duemmm9LZ2dm/7l//9V+zZ8+e3HPPPdm0aVMWLVqUO++8s15jAwANzrYyAIAB5t///d/zn//5n5k4cWK6u7tz3333ZcqUKVm0aFH27duXjRs35vzzz0+SnHPOOXn00UfrPDEA0Mg8OQQAMMB85StfyfXXX58keec735l3v/vdOemkkzJ37tx885vfTE9PT1paWvrXNzU1Zd++fWlufvVbv2HDBqe5uel1nx0ayYgRrfUeATjCXPcHJw4BAAwgO3bsyH/913/lvPPOS5J86EMfSltbW5LkwgsvzPe///20tramt7e3/z3VavWQYShJtm/f9foNDQ1q69ad9R4BOMJKvu4PFcZsKwMAGEB+8pOf5B3veEeSpFar5S/+4i/yy1/+Mkmybt26nH766Rk3blxWr16dJNm0aVPe8pa31G1eAKDxeXIIAGAAefLJJ3PSSSclSSqVShYsWJBPfOITOe644/LmN785l19+eZqamvLjH/84kyZNSq1Wy+23317nqQGARiYOAQAMIB/96Ef3e93R0ZGOjo4D1n3mM585UiMBAAOcbWUAAAAABROHAAAAAAomDgEAAAAUTBwCAAAAKJg4BAAAAFAwcQgAAACgYOIQAAAAQMHEIQAAAICCiUMAAAAABROHAAAAAAomDgEAAAAUTBwCAAAAKJg4BAAAAFAwcQgAAACgYOIQAAAAQMHEIQAAAICCiUMAAAAABROHAAAAAAomDgEAAAAUTBwCAAAAKJg4BAAAAFAwcQgAAACgYOIQAAAAQMHEIQAAAICCiUMAAAAABROHAAAAAAomDgEAAAAUTBwCAAAAKJg4BAAAAFAwcQgAAACgYOIQAAAAQMHEIQAAAICCiUMAAAAABROHAAAAAAomDgEAAAAUTBwCAAAAKJg4BAAAAFAwcQgAAACgYOIQAAAAQMHEIQAAAICCiUMAAAAABROHAAAAAAomDgEAAAAUTBwCAAAAKJg4BAAAAFAwcQgAAACgYOIQAAAAQMHEIQAAAICCiUMAAAAABROHAAAAAAomDgEAAAAUTBwCAAAAKJg4BAAAAFCw5noPAADAa/eBD3wgra2tSZKTTjopEydOzG233ZampqZ0dHTkE5/4RKrVam699dY88cQTOfbYY7NgwYKMGjWqzpMDAI1KHAIAGCB2796dJFm+fHn/sfe///1ZvHhxTj755HzsYx9Ld3d3nn322ezZsyf33HNPNm3alEWLFuXOO++s19gAQIMThwAABojHH388L774YqZOnZp9+/Zl2rRp2bNnT0455ZQkSUdHR9atW5etW7fm/PPPT5Kcc845efTRR+s5NgDQ4MQhAIAB4rjjjss111yTyy67LE899VSuvfbatLW19f98yJAh+cUvfpGenp60tLT0H29qasq+ffvS3Pzqt37Dhg1Oc3PT6zo/NJoRI1rrPQJwhLnuD04cAgAYIEaPHp1Ro0alUqlk9OjRaW1tzW9+85v+n/f29qatrS0vvfRSent7+49Xq9VDhqEk2b591+s2NzSqrVt31nsE4Agr+bo/VBjz28oAAAaIb33rW1m0aFGS5Fe/+lVefPHFDB48OE8//XRqtVrWrFmT9vb2jBs3LqtXr06SbNq0KW95y1vqOTYA0OA8OQQAMEBMmDAht9xySyZPnpxKpZLbb789gwYNyo033pi+vr50dHTk7LPPzplnnpkf//jHmTRpUmq1Wm6//fZ6jw4ANDBxCABggDj22GPz+c9//oDj9957736vBw0alM985jNHaiwAYICzrQwAAACgYOIQAAAAQMHEIQAAAICCiUMAAAAABROHAAAAAAomDgEAAAAUTBwCAAAAKJg4BAAAAFCw1xSHHn744Vx55ZVJks2bN2fy5MmZMmVK5s6dm2q1miRZsmRJJkyYkEmTJuWRRx455FoAAAAAGsNh49CyZcsya9as7N69O0mycOHCTJ8+PStWrEitVsuqVavS3d2dDRs2ZOXKlenq6sq8efNedS0AAAAAjeOwceiUU07J4sWL+193d3fn3HPPTZKMHz8+a9euzcaNG9PR0ZFKpZKRI0emr68v27ZtO+haAAAAABpH8+EWdHZ25plnnul/XavVUqlUkiRDhgzJzp0709PTk6FDh/avefn4wdYezrBhg9Pc3PRbnwgMdCNGtNZ7BOAIcs0DANAoDhuH/rdBg/7nYaPe3t60tbWlpaUlvb29+x1vbW096NrD2b591287EhwVtm49fDwFjh6lX/PiGABA4/itf1vZ2LFjs379+iTJ6tWr097ennHjxmXNmjWpVqvZsmVLqtVqhg8fftC1AAAAADSO3/rJoRkzZmT27Nnp6urKmDFj0tnZmaamprS3t2fixImpVquZM2fOq64FAAAAoHG8pjh00kkn5d57702SjB49OnffffcBa6ZNm5Zp06btd+zV1gIAAADQGH7rbWUAAAAAHD3EIQAAAICCiUMAAAAABROHAAAAAAomDgEAAAAUTBwCAAAAKJg4BAAAAFAwcQgAAACgYOIQAAAAQMHEIQAAAICCiUMAAAAABROHAAAAAAomDgEAAAAUTBwCAAAAKJg4BAAAAFAwcQgAAACgYOIQAAAAQMHEIQAAAICCiUMAAAAABROHAAAAAAomDgEAAAAUTBwCAAAAKJg4BAAAAFAwcQgAAACgYOIQAAAAQMHEIQAAAICCiUMAAAAABROHAAAAAAomDgEAAAAUTBwCAAAAKJg4BAAAAFAwcQgAAACgYOIQAAAAQMHEIQAAAICCiUMAAAAABROHAAAAAAomDgEAAAAUrLneAwAA8Nrs3bs3M2fOzLPPPps9e/bkuuuuy+///u/n4x//eP7wD/8wSTJ58uT82Z/9WZYsWZIHH3wwzc3NmTlzZs4666z6Dg8ANCxxCABggLj//vszdOjQfPazn8327dvzwQ9+MNdff32uvvrqTJ06tX9dd3d3NmzYkJUrV+a5557LtGnTct9999VxcgCgkYlDAAADxMUXX5zOzs7+101NTXn00Ufz5JNPZtWqVRk1alRmzpyZjRs3pqOjI5VKJSNHjkxfX1+2bduW4cOH13F6AKBRiUMAAAPEkCFDkiQ9PT254YYbMn369OzZsyeXXXZZzjjjjNx555358pe/nNbW1gwdOnS/9+3cufOQcWjYsMFpbm563c8BGsmIEa31HgE4wlz3BycOAQAMIM8991yuv/76TJkyJZdcckl27NiRtra2JMlFF12U+fPn58ILL0xvb2//e3p7e9Paeuib4e3bd72uc0Mj2rp1Z71HAI6wkq/7Q4Uxv60MAGCAeP755zN16tTcdNNNmTBhQpLkmmuuySOPPJIkWbduXU4//fSMGzcua9asSbVazZYtW1KtVm0pAwBelSeHAAAGiLvuuis7duzI0qVLs3Tp0iTJzTffnNtvvz3HHHNMTjjhhMyfPz8tLS1pb2/PxIkTU61WM2fOnDpPDgA0MnEIAGCAmDVrVmbNmnXA8W9+85sHHJs2bVqmTZt2JMYCAAY428oAAAAACiYOAQAAABRMHAIAAAAomDgEAAAAUDBxCAAAAKBg4hAAAABAwcQhAAAAgIKJQwAAAAAFE4cAAAAACiYOAQAAABRMHAIAAAAomDgEAAAAUDBxCAAAAKBg4hAAAABAwcQhAAAAgIKJQwAAAAAFE4cAAAAACiYOAQAAABRMHAIAAAAomDgEAAAAUDBxCAAAAKBg4hAAAABAwcQhAAAAgIKJQwAAAAAFE4cAAAAACiYOAQAAABRMHAIAAAAomDgEAAAAUDBxCAAAAKBg4hAAAABAwcQhAAAAgIKJQwAAAAAFE4cAAAAACiYOAQAAABRMHAIAAAAomDgEAAAAUDBxCAAAAKBg4hAAAABAwcQhAAAAgIKJQwAAAAAFE4cAAAAACiYOAQAAABRMHAIAAID/194d6jQSRWEAPk0b1CDRDSFpJU19ZfXKVfASxTRBIAjUrCRreABYu7YGfJOKGrC8QmsKmVm1JCS7mxF7GcL9PjdjzlGTP3/uzUDGlEMAAAAAGVMOAQAAAGRMOQQAAACQMeUQAAAAQMaUQwAAAAAZUw4BAAAAZEw5BAAAAJAx5RAAAABAxpRDAAAAABlTDgEAAABkrJN6QFmWcXZ2Fg8PD7GzsxPn5+fR7XZTjwUAyJoMBgDUlfzk0Hw+j+12G7e3tzGZTGI2m6UeCQCQPRkMAKgreTm0WCxiNBpFRMRgMIjVapV6JABA9mQwAKCu5NfK1ut1FEXx+txut+Pl5SU6nT+P3tvbTb3Sh/bz25emVwDe0Y+v35teAfikZLD65C/IjwwGbyU/OVQURWw2m9fnsiz/GkoAAPg/ZDAAoK7k5dBwOIz7+/uIiFgul9Hr9VKPBADIngwGANTVqqqqSjng958yHh8fo6qquLi4iIODg5QjAQCyJ4MBAHUlL4cAAAAA+LiSXysDAAAA4ONSDgEAAABkTDkEAAAAkDHlEADvrizLplcAAMiK/MW/dJpeAIA8PD09xeXlZaxWq+h0OlGWZfR6vZhOp7G/v9/0egAAn478RV3+VgbAuzg+Po7JZBKHh4ev75bLZcxms7i5uWlwMwCAz0n+oi4nh2jM0dFRPD8/v3lXVVW0Wi0fKviEttvtm2ASETEYDBraBiBP8hfkRf6iLuUQjTk5OYnT09O4urqKdrvd9DpAYv1+P6bTaYxGo9jd3Y3NZhN3d3fR7/ebXg0gG/IX5EX+oi7XymjU9fV1dLvdGI/HTa8CJFZVVczn81gsFrFer6MoihgOhzEej6PVajW9HkA25C/Ih/xFXcohAAAAgIz5lT0AAABAxpRDAAAAABlTDgEAAABkTDkEAAAAkDHlEAAAAEDGfgFflNXtQxPLUAAAAABJRU5ErkJggg==\n", | |
| "text/plain": [ | |
| "<Figure size 1440x720 with 2 Axes>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "plt.figure(figsize=(20, 10))\n", | |
| "plt.subplot(121)\n", | |
| "train_df.label.value_counts().plot(kind='bar')\n", | |
| "plt.title('Train')\n", | |
| "plt.subplot(122)\n", | |
| "test_df.label.value_counts().plot(kind='bar')\n", | |
| "plt.title('Test');" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 11, | |
| "metadata": { | |
| "colab": {}, | |
| "colab_type": "code", | |
| "id": "Hx9D6FjD8YWl" | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "# 定义训练输入函数\n", | |
| "train_input_fn = tf.estimator.inputs.pandas_input_fn(\n", | |
| " train_df, train_df[\"label\"], num_epochs=50, shuffle=True)\n", | |
| "\n", | |
| "# 用于在整个训练集上进行测试的输入函数\n", | |
| "predict_train_input_fn = tf.estimator.inputs.pandas_input_fn(\n", | |
| " train_df, train_df[\"label\"], shuffle=False)\n", | |
| "\n", | |
| "# 用于在整个测试集上进行测试的输入函数\n", | |
| "predict_test_input_fn = tf.estimator.inputs.pandas_input_fn(\n", | |
| " test_df, test_df[\"label\"], shuffle=False)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 12, | |
| "metadata": { | |
| "colab": {}, | |
| "colab_type": "code", | |
| "id": "wwbRQwmk921z" | |
| }, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "INFO:tensorflow:Using F:\\tfhubcache to cache modules.\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "# 定义特征列，这里使用 TensorFlow Hub 来构建\n", | |
| "embedded_text_feature_column = hub.text_embedding_column(\n", | |
| " key=\"comment\", \n", | |
| " module_spec=\"https://tfhub.dev/google/nnlm-zh-dim128/1\")" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 13, | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 72 | |
| }, | |
| "colab_type": "code", | |
| "id": "DPBWsepd972D", | |
| "outputId": "0738a027-5f50-48af-bb7d-87fcb33e0871" | |
| }, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "INFO:tensorflow:Using default config.\n", | |
| "INFO:tensorflow:Using config: {'_model_dir': 'models', '_tf_random_seed': None, '_save_summary_steps': 100, '_save_checkpoints_steps': None, '_save_checkpoints_secs': 600, '_session_config': None, '_keep_checkpoint_max': 5, '_keep_checkpoint_every_n_hours': 10000, '_log_step_count_steps': 100, '_train_distribute': None, '_device_fn': None, '_service': None, '_cluster_spec': <tensorflow.python.training.server_lib.ClusterSpec object at 0x000001E2BA66E438>, '_task_type': 'worker', '_task_id': 0, '_global_id_in_cluster': 0, '_master': '', '_evaluation_master': '', '_is_chief': True, '_num_ps_replicas': 0, '_num_worker_replicas': 1}\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "# 初始化内置的 DNNClassifier\n", | |
| "estimator = tf.estimator.DNNClassifier(\n", | |
| " hidden_units=[100, 100],\n", | |
| " feature_columns=[embedded_text_feature_column],\n", | |
| " n_classes=2,\n", | |
| " optimizer=tf.train.AdagradOptimizer(learning_rate=0.003),\n", | |
| " model_dir='models')" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 14, | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 1179 | |
| }, | |
| "colab_type": "code", | |
| "id": "3RsI8Kkb-SQL", | |
| "outputId": "bc31cf37-e92c-4fcc-e6bb-4f8d3c04711e" | |
| }, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "INFO:tensorflow:Calling model_fn.\n", | |
| "INFO:tensorflow:Saver not created because there are no variables in the graph to restore\n", | |
| "INFO:tensorflow:Saver not created because there are no variables in the graph to restore\n", | |
| "INFO:tensorflow:Done calling model_fn.\n", | |
| "INFO:tensorflow:Create CheckpointSaverHook.\n", | |
| "INFO:tensorflow:Graph was finalized.\n", | |
| "INFO:tensorflow:Restoring parameters from models\\model.ckpt-2735\n", | |
| "INFO:tensorflow:Running local_init_op.\n", | |
| "INFO:tensorflow:Done running local_init_op.\n", | |
| "INFO:tensorflow:Saving checkpoints for 2735 into models\\model.ckpt.\n", | |
| "INFO:tensorflow:loss = 0.06739878, step = 2736\n", | |
| "INFO:tensorflow:global_step/sec: 337.655\n", | |
| "INFO:tensorflow:loss = 0.034682423, step = 2836 (0.311 sec)\n", | |
| "INFO:tensorflow:global_step/sec: 485.717\n", | |
| "INFO:tensorflow:loss = 0.04515608, step = 2936 (0.185 sec)\n", | |
| "INFO:tensorflow:global_step/sec: 543.789\n", | |
| "INFO:tensorflow:loss = 0.028866358, step = 3036 (0.184 sec)\n", | |
| "INFO:tensorflow:global_step/sec: 546.763\n", | |
| "INFO:tensorflow:loss = 0.04221748, step = 3136 (0.183 sec)\n", | |
| "INFO:tensorflow:global_step/sec: 546.761\n", | |
| "INFO:tensorflow:loss = 0.10310146, step = 3236 (0.183 sec)\n", | |
| "INFO:tensorflow:global_step/sec: 543.79\n", | |
| "INFO:tensorflow:loss = 0.046141706, step = 3336 (0.184 sec)\n", | |
| "INFO:tensorflow:global_step/sec: 510.497\n", | |
| "INFO:tensorflow:loss = 0.056765363, step = 3436 (0.277 sec)\n", | |
| "INFO:tensorflow:global_step/sec: 363.79\n", | |
| "INFO:tensorflow:loss = 0.038011882, step = 3536 (0.194 sec)\n", | |
| "INFO:tensorflow:global_step/sec: 507.906\n", | |
| "INFO:tensorflow:loss = 0.03202858, step = 3636 (0.197 sec)\n", | |
| "INFO:tensorflow:global_step/sec: 495.333\n", | |
| "INFO:tensorflow:loss = 0.039412722, step = 3736 (0.203 sec)\n", | |
| "INFO:tensorflow:global_step/sec: 543.792\n", | |
| "INFO:tensorflow:loss = 0.045118544, step = 3836 (0.183 sec)\n", | |
| "INFO:tensorflow:global_step/sec: 488.084\n", | |
| "INFO:tensorflow:loss = 0.03442596, step = 3936 (0.205 sec)\n", | |
| "INFO:tensorflow:global_step/sec: 505.341\n", | |
| "INFO:tensorflow:loss = 0.025132883, step = 4036 (0.198 sec)\n", | |
| "INFO:tensorflow:global_step/sec: 549.765\n", | |
| "INFO:tensorflow:loss = 0.023409069, step = 4136 (0.182 sec)\n", | |
| "INFO:tensorflow:global_step/sec: 495.335\n", | |
| "INFO:tensorflow:loss = 0.043221936, step = 4236 (0.203 sec)\n", | |
| "INFO:tensorflow:global_step/sec: 471.968\n", | |
| "INFO:tensorflow:loss = 0.03103593, step = 4336 (0.211 sec)\n", | |
| "INFO:tensorflow:global_step/sec: 469.753\n", | |
| "INFO:tensorflow:loss = 0.022563316, step = 4436 (0.213 sec)\n", | |
| "INFO:tensorflow:global_step/sec: 546.761\n", | |
| "INFO:tensorflow:loss = 0.021799732, step = 4536 (0.183 sec)\n", | |
| "INFO:tensorflow:global_step/sec: 523.862\n", | |
| "INFO:tensorflow:loss = 0.02162357, step = 4636 (0.191 sec)\n", | |
| "INFO:tensorflow:global_step/sec: 523.861\n", | |
| "INFO:tensorflow:loss = 0.018305503, step = 4736 (0.191 sec)\n", | |
| "INFO:tensorflow:global_step/sec: 415.176\n", | |
| "INFO:tensorflow:loss = 0.021797787, step = 4836 (0.241 sec)\n", | |
| "INFO:tensorflow:global_step/sec: 483.368\n", | |
| "INFO:tensorflow:loss = 0.039814856, step = 4936 (0.207 sec)\n", | |
| "INFO:tensorflow:global_step/sec: 452.748\n", | |
| "INFO:tensorflow:loss = 0.019658014, step = 5036 (0.221 sec)\n", | |
| "INFO:tensorflow:global_step/sec: 505.341\n", | |
| "INFO:tensorflow:loss = 0.019889731, step = 5136 (0.198 sec)\n", | |
| "INFO:tensorflow:global_step/sec: 513.114\n", | |
| "INFO:tensorflow:loss = 0.02168344, step = 5236 (0.196 sec)\n", | |
| "INFO:tensorflow:global_step/sec: 490.478\n", | |
| "INFO:tensorflow:loss = 0.022522777, step = 5336 (0.203 sec)\n", | |
| "INFO:tensorflow:global_step/sec: 523.86\n", | |
| "INFO:tensorflow:loss = 0.03371704, step = 5436 (0.191 sec)\n", | |
| "INFO:tensorflow:Saving checkpoints for 5470 into models\\model.ckpt.\n", | |
| "INFO:tensorflow:Loss for final step: 0.004834718.\n" | |
| ] | |
| }, | |
| { | |
| "data": { | |
| "text/plain": [ | |
| "<tensorflow.python.estimator.canned.dnn.DNNClassifier at 0x1e2b9db8550>" | |
| ] | |
| }, | |
| "execution_count": 14, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "# 开始训练\n", | |
| "estimator.train(input_fn=train_input_fn)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 15, | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 523 | |
| }, | |
| "colab_type": "code", | |
| "id": "gLgdyN4n-dUU", | |
| "outputId": "30fa242d-7ff4-4343-9590-558a258c42fc" | |
| }, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "INFO:tensorflow:Calling model_fn.\n", | |
| "INFO:tensorflow:Saver not created because there are no variables in the graph to restore\n", | |
| "INFO:tensorflow:Saver not created because there are no variables in the graph to restore\n", | |
| "WARNING:tensorflow:Trapezoidal rule is known to produce incorrect PR-AUCs; please switch to \"careful_interpolation\" instead.\n", | |
| "WARNING:tensorflow:Trapezoidal rule is known to produce incorrect PR-AUCs; please switch to \"careful_interpolation\" instead.\n", | |
| "INFO:tensorflow:Done calling model_fn.\n", | |
| "INFO:tensorflow:Starting evaluation at 2018-08-23-05:13:11\n", | |
| "INFO:tensorflow:Graph was finalized.\n", | |
| "INFO:tensorflow:Restoring parameters from models\\model.ckpt-5470\n", | |
| "INFO:tensorflow:Running local_init_op.\n", | |
| "INFO:tensorflow:Done running local_init_op.\n", | |
| "INFO:tensorflow:Finished evaluation at 2018-08-23-05:13:16\n", | |
| "INFO:tensorflow:Saving dict for global step 5470: accuracy = 1.0, accuracy_baseline = 0.69757146, auc = 1.0, auc_precision_recall = 1.0, average_loss = 0.0001732409, global_step = 5470, label/mean = 0.69757146, loss = 0.022048842, precision = 1.0, prediction/mean = 0.6975434, recall = 1.0\n", | |
| "INFO:tensorflow:Saving 'checkpoint_path' summary for global step 5470: models\\model.ckpt-5470\n", | |
| "INFO:tensorflow:Calling model_fn.\n", | |
| "INFO:tensorflow:Saver not created because there are no variables in the graph to restore\n", | |
| "INFO:tensorflow:Saver not created because there are no variables in the graph to restore\n", | |
| "WARNING:tensorflow:Trapezoidal rule is known to produce incorrect PR-AUCs; please switch to \"careful_interpolation\" instead.\n", | |
| "WARNING:tensorflow:Trapezoidal rule is known to produce incorrect PR-AUCs; please switch to \"careful_interpolation\" instead.\n", | |
| "INFO:tensorflow:Done calling model_fn.\n", | |
| "INFO:tensorflow:Starting evaluation at 2018-08-23-05:13:18\n", | |
| "INFO:tensorflow:Graph was finalized.\n", | |
| "INFO:tensorflow:Restoring parameters from models\\model.ckpt-5470\n", | |
| "INFO:tensorflow:Running local_init_op.\n", | |
| "INFO:tensorflow:Done running local_init_op.\n", | |
| "INFO:tensorflow:Finished evaluation at 2018-08-23-05:13:20\n", | |
| "INFO:tensorflow:Saving dict for global step 5470: accuracy = 1.0, accuracy_baseline = 0.70566666, auc = 1.0, auc_precision_recall = 1.0, average_loss = 0.00036637738, global_step = 5470, label/mean = 0.70566666, loss = 0.045797173, precision = 1.0, prediction/mean = 0.7054589, recall = 1.0\n", | |
| "INFO:tensorflow:Saving 'checkpoint_path' summary for global step 5470: models\\model.ckpt-5470\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "# 在训练集和测试集上进行测试\n", | |
| "train_eval_result = estimator.evaluate(input_fn=predict_train_input_fn)\n", | |
| "test_eval_result = estimator.evaluate(input_fn=predict_test_input_fn)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 16, | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 208 | |
| }, | |
| "colab_type": "code", | |
| "id": "eUS3ATyY_KR8", | |
| "outputId": "4ba428be-3beb-4fec-ffa1-589e9c2d535b" | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/plain": [ | |
| "{'accuracy': 1.0,\n", | |
| " 'accuracy_baseline': 0.69757146,\n", | |
| " 'auc': 1.0,\n", | |
| " 'auc_precision_recall': 1.0,\n", | |
| " 'average_loss': 0.0001732409,\n", | |
| " 'label/mean': 0.69757146,\n", | |
| " 'loss': 0.022048842,\n", | |
| " 'precision': 1.0,\n", | |
| " 'prediction/mean': 0.6975434,\n", | |
| " 'recall': 1.0,\n", | |
| " 'global_step': 5470}" | |
| ] | |
| }, | |
| "execution_count": 16, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "train_eval_result" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 17, | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 208 | |
| }, | |
| "colab_type": "code", | |
| "id": "-ijy0go0_OKU", | |
| "outputId": "320f52a0-afee-444d-8cff-9bfda12cd2c7" | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/plain": [ | |
| "{'accuracy': 1.0,\n", | |
| " 'accuracy_baseline': 0.70566666,\n", | |
| " 'auc': 1.0,\n", | |
| " 'auc_precision_recall': 1.0,\n", | |
| " 'average_loss': 0.00036637738,\n", | |
| " 'label/mean': 0.70566666,\n", | |
| " 'loss': 0.045797173,\n", | |
| " 'precision': 1.0,\n", | |
| " 'prediction/mean': 0.7054589,\n", | |
| " 'recall': 1.0,\n", | |
| " 'global_step': 5470}" | |
| ] | |
| }, | |
| "execution_count": 17, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "test_eval_result" | |
| ] | |
| } | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "name": "tensorflowhub-share-ppt.ipynb", | |
| "provenance": [], | |
| "version": "0.3.2" | |
| }, | |
| "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": "3.6.5" | |
| } | |
| }, | |
| "nbformat": 4, | |
| "nbformat_minor": 2 | |
| } |
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