From 849ae3ece67052cebcada906d9e6d20d79e9b7c7 Mon Sep 17 00:00:00 2001 From: Haesun Park Date: Mon, 9 Apr 2018 19:13:31 +0900 Subject: [PATCH] =?UTF-8?q?10=EC=9E=A5=20=EC=8B=A4=ED=96=89?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...uction_to_artificial_neural_networks.ipynb | 2250 ++++++++--------- 1 file changed, 1121 insertions(+), 1129 deletions(-) diff --git a/10_introduction_to_artificial_neural_networks.ipynb b/10_introduction_to_artificial_neural_networks.ipynb index 2fcdbce..573000b 100644 --- a/10_introduction_to_artificial_neural_networks.ipynb +++ b/10_introduction_to_artificial_neural_networks.ipynb @@ -1,53 +1,77 @@ { "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Chapter 10 – Introduction to Artificial Neural Networks**" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "_This notebook contains all the sample code and solutions to the exercises in chapter 10._" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Setup" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "First, let's make sure this notebook works well in both python 2 and 3, import a few common modules, ensure MatplotLib plots figures inline and prepare a function to save the figures:" - ] - }, { "cell_type": "code", "execution_count": 1, "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPython 3.5.5\n", + "IPython 6.3.0\n", + "\n", + "numpy 1.14.2\n", + "scipy 1.0.1\n", + "matplotlib 2.2.2\n", + "tensorflow 1.7.0\n" + ] + } + ], + "source": [ + "%load_ext watermark\n", + "%watermark -v -p numpy,scipy,matplotlib,tensorflow" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**10장 – 인공 신경망 소개**" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "_이 노트북은 10장에 있는 모든 샘플 코드와 연습문제 해답을 가지고 있습니다._" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 설정" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "파이썬 2와 3을 모두 지원합니다. 공통 모듈을 임포트하고 맷플롯립 그림이 노트북 안에 포함되도록 설정하고 생성한 그림을 저장하기 위한 함수를 준비합니다:" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, "outputs": [], "source": [ - "# To support both python 2 and python 3\n", + "# 파이썬 2와 파이썬 3 지원\n", "from __future__ import division, print_function, unicode_literals\n", "\n", - "# Common imports\n", + "# 공통\n", "import numpy as np\n", "import os\n", "\n", - "# to make this notebook's output stable across runs\n", + "# 일관된 출력을 위해 유사난수 초기화\n", "def reset_graph(seed=42):\n", " tf.reset_default_graph()\n", " tf.set_random_seed(seed)\n", " np.random.seed(seed)\n", "\n", - "# To plot pretty figures\n", + "# 맷플롯립 설정\n", "%matplotlib inline\n", "import matplotlib\n", "import matplotlib.pyplot as plt\n", @@ -55,13 +79,16 @@ "plt.rcParams['xtick.labelsize'] = 12\n", "plt.rcParams['ytick.labelsize'] = 12\n", "\n", - "# Where to save the figures\n", + "# 한글출력\n", + "plt.rcParams['font.family'] = 'NanumBarunGothic'\n", + "plt.rcParams['axes.unicode_minus'] = False\n", + "\n", + "# 그림을 저장할 폴더\n", "PROJECT_ROOT_DIR = \".\"\n", "CHAPTER_ID = \"ann\"\n", "\n", "def save_fig(fig_id, tight_layout=True):\n", " path = os.path.join(PROJECT_ROOT_DIR, \"images\", CHAPTER_ID, fig_id + \".png\")\n", - " print(\"Saving figure\", fig_id)\n", " if tight_layout:\n", " plt.tight_layout()\n", " plt.savefig(path, format='png', dpi=300)" @@ -71,15 +98,13 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Perceptrons" + "# 퍼셉트론" ] }, { "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": true - }, + "execution_count": 3, + "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", @@ -87,10 +112,10 @@ "from sklearn.linear_model import Perceptron\n", "\n", "iris = load_iris()\n", - "X = iris.data[:, (2, 3)] # petal length, petal width\n", + "X = iris.data[:, (2, 3)] # 꽃잎 길이, 꽃잎 너비\n", "y = (iris.target == 0).astype(np.int)\n", "\n", - "per_clf = Perceptron(random_state=42)\n", + "per_clf = Perceptron(max_iter=5, random_state=42)\n", "per_clf.fit(X, y)\n", "\n", "y_pred = per_clf.predict([[2, 0.5]])" @@ -98,7 +123,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -107,7 +132,7 @@ "array([1])" ] }, - "execution_count": 3, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -118,21 +143,14 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "metadata": {}, "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Saving figure perceptron_iris_plot\n" - ] - }, { "data": { - "image/png": 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IcqJW05ImuLq60rfv26xZM5RChXI54hs3fsmwYc9y7Nh2G7MTERGRlEgFsqQK1aqVYuvW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\n", 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//nsHMxMRkbwsL7Vazsp78dT7N6msP5FaPKs5ONJq2hizCJhsrZ1hjKkCrATaWWsjjTFdgKcBFwkrXiwGBltrzxhjfElY5u0uEqZXbAKettamOclYy7zlP/Hx8YwY8SVvvDHzglUtbrnlf9x//0j8/AqkcbaIiIjkRRltNe1IgexpKpDzr2XLNtKpUzh79x5xx6pWbUz37jMpW7aGg5mJiIiIp2W0QHZ8ioVITrrhhiAiI8fSps35r4Xdu9cSGtqQNWsu6lMjIiIiogJZ8r7SpQP5+uuXGT26K/7+fgBER5/ggw8eZerU7pw9e9rhDEVERMSb+DmdgIgnGGPo1+8err/+ajp2HM2OHfsBWL78ff76awXdus2kUqVrHM5SRETS4s3tnD0lK62WMysrn+dLbwGdsft4ikaQJV9p3LgmERHhPPLIje7Yv/9uYsSIa1m69D3yw5x8EZHcKi+1c86qrLRazqysfJ6zswW0N/x9qkCWfCcwsDCffPIc77zTh0KFElaziI2NZvr0HnzwwaOcORPlcIYiIiLiJBXIki8ZY+jSpRUrVozm6qvP956JjJxJaGgjdu5c42B2IiIi4iQVyJKvBQVVZcWKUXTrdrs7dvjwX4wadT3z54dryoWIiEg+pAJZ8r3ChQOYNKk306YNIDCwMADx8bF8+WV/Jk26m5MnDzucoYiIiHiSCmSRRA8/fAOrV4cTHFzLHduwYQ5vvFGfbdsWO5iZiIhA3mrnnFVZabWcWVn5PGdnC2hv+PtUJz2RZM6ejeXVV6cxduy37piPjw9t2gymTZtX8PHxdTA7ERERySp10hPJogIF/Bk5sgvffPMKpUsXA8DlcvH9968xbtxtHDu2z+EMRUREJCepQBZJRZs2wURGjuOmm4LcsW3bFhESUp8//vjBwcxEREQkJ6lAFklDpUql+emnobzyyiP4+CR8uZw8eZi33mrDl18OIC7urMMZioiISHZTgSySDl9fXwYPfpSffnqdihVLuePz549h9OgbOXToLwezExERkeymh/REMuHQoSi6dRvPDz+sdccKFgykU6f3adz4IQczExHJHgMHptzqNzAQwsI8n8+l6tkz9X2TJ6cc79Ur5RbJxsDbbzt7Tlb+fjJ7Tl77N5CUHtITyQFlyxbn669fJiysC/7+fgBERx/nvfceZvr0npw9e8bhDEVELk1KhVFa8bwotbHDtMYUPXVOVv5+MnuO/g2oQBbJNB8fH/73v3tZvHg41auXd8eXLn2HESOasG/fJgezExERkUulAlkki4KDa7F6dTgPPni9O7Zv3x8MHx7M8uUfqk21iIhILqUCWeQSFC9ehOnTBzBpUi8KFiwAQGzsGaZOfZIPP+zImTP56PdRIiIieYQKZJFLZIyhW7fWLF8exlVXVXbH16z5lGHDGrNr19o0zhYRERFv49EC2Rjjb4wZYIyJNca0T+WY8saYd4wxm40xq40xS40xdRP3lTbGnDLGrEqyjfbkexBJTd261Vi5cjRdutzmjh069CejRzdjwYI3NeVCRHKFwMDMxfMiYzIX9+Q5Wfn7yew5+jfg4WXejDG9gUJAO2CitXZGCsfcCRSy1s5KfN0faG2tvd0YcyXwlrW2VWbuq2XexNNmzFhCnz5vc+LE+VUt6tW7m8cfn0LRoqUdzExERCT/8spl3qy1k6y1Y4D4NI754VxxnOhfwC/x4zJAPWPMSmPMGmPMBGNM+YuvAsaYHsaYSGNM5OHDmgcqntW+/U1ERITTqFENd2z9+tmEhjZg+/alDmYmIiIi6fHqOciJxe9Q4PXEUCRQ0Vp7HdASOAvMNebiX0ZYa9+11gZba4PLlMlHvxMQr1GzZgUWLx5Bv353u2P//beH8PCWzJ0bgsuV6s+JIiIi4iCvLZCNMaWBucBr1trFANbaGGttfOLHp4CBwFVATccSFUlDQIA/o0c/yaxZgyhVqhgA1rr47rtXefPN24mK+tfhDEVERCQ5v/QP8TxjTAXgByDMWvtpWoeSUORrDoV4tbZtm7BmTTiPPx7O8uWbAdi69RdCQurzxBNTCQpq7XCGIiLezxNtlj2ZmzffJ7O8Na+scnwEOXFliuXGmFqJry8HFgBvJC+OjTH3JBbPJE6rGAosstYe8HTeIplVpUpZfv45hJdeeohzs4JOnDjEhAl3MGvWC8THxzqcoYiId/NEm+Wsymv3ySxvzSurUhxBNsZ8mNkLWWu7ZjGHwsDlQPHE12OA8sDzxpjnE2Mx1toWgAVmGWP8ARewHuiQxfuKeJyfny+vv96Bli3r0rnzWPbv/w+AefPC2L59CU8++RllylRzNkkREZF8LrUpFqdy8qbW2pZJPv4HqJzk9YNpnDcbmJ2TuYl4ws031yMycixdu77JvHnrAPj771WEhjagU6cPaNToAYczFBERyb9SLJCttX09nYhIflOuXAm+++5Vxo79lldfnUZcXDxnzkTx7rsPctNNvXjooXD8/Qs6naaIiEi+4/gcZJH8zMfHh/7972PhwmFUq1bOHV+y5G1GjGjK/v1bHMxOREQkf0qxQDbGNDHGzEpj+8rTiYrkZU2bXsnq1eHcd9917tjevesZNqwxK1d+7GBmIiLewxNtlrMqr90ns7w1r6xKsdV0Ykvn9klCg0lYMSKOhKXVXgf8rbUuTyR5qdRqWnILay3vvvsjAwZ8SEzM+VUtmjbtxKOPTqRgwWIOZiciIpK7ZbTVdIoF8kUHGeMCClprzyZ57acCWSRn/P7733ToMJpt2/a6Y+XK1aJ798+pUqWBg5mJiIjkXhktkDM6Bzl5FZ1+VS0iWVa/fnUiIsbw+OO3uGMHD25n5MimLFz4Fhn5wVZERESyJjMP6ek7sogHFSlSkPff78eUKf+jaNGE1Szi4s4yc2ZfJk++n1OnjjqcoYiISN6UWqOQu4FZnC+KDRB1rvuXiHhOhw4tufbaWnTsOIbffvsLgN9//4bQ0F958snPqFGjucMZinivvNb+Ni/x5rbRIqmNIK8AbgNaJW43A3cm2W7JLfOPRfKC2rUrsXTpSPr0ucsdO3p0N2PG3MSPPw7H5dKXo0hK8lr727zEm9tGi6TWKOQIsNjDuYhIGgIC/Bk7tjstW9alR4+3+O+/k7hc8XzzzSC2bl1Ily5TCQws73SaIiIiuV5q6yCXyuzm6cRF8qt7723GmjVjue66q9yxzZt/JiSkPps2/exgZiIiInlDalMsDgOHMridO1ZEPKRq1bIsWBDKCy88yLlnA44fP8CECa355ptBxMfHpnMFERERSU1qBXJ14IrErQYJD+k1SBJLup07VkQ8yM/Plzfe6Mjcua9RvnwJIKHRyI8/DmfMmJYcObLL4QxFRERypxQLZGvtriTbThJWs9hlrd0F1CHhIb2kx+g7sYhDbr21PpGR47jttvru2F9/rSA0tAG//faNg5mJOC+vtb/NS7y5bbRIRjvpxQMlrbXHjTF3Ai9Ya1vmdHLZRZ30JD9wuVyMHv01Q4ZMJz7+/KoWLVs+zQMPjMLfv6CD2YmIiDgvuzvpneX8msh/AbWympiI5AwfHx8GDnyAX34ZRtWqZd3xRYveIizsOg4c2OZgdiIiIrlHhgpka20ha+2JxJdHgXJGXUNEvNJ1113F6tXh3HNPU3fsn39+IyysPhER0xzMTEREJHfITKvpc44BvkDRbM5FRLJJqVLF+OKLFxk3rjsFCiQsd37qVDRTpnTio4+eIDr6pMMZioiIeK8UG4WkxVobmzh47J/96YhIdjHG0Lv3XTRvXocOHUazffs+AFat+pi//15F9+4zqVy5fjpXEZGc0qsXpPQYkDHw9tu56z7e3AJaLa0lK1IdQTbGDE5t4/x85EwxxvgbYwYYY2KNMe1TOcYYY94wxmw1xmwyxkwzxhRJb5+IpKxBgyuIiBhDhw4t3bEDB7YyYkRTFi2aREYe1BWR7Jfal152f0l64j7e3AJaLa0lK9KaYnFzGtsSICudCLqTUFyvSuOYzkAboIG19urE+4zMwD4RSUXRooWYMuV/fPDBMxQpkrCaRVxcDDNm9OHddx/i9OljDmcoIiLiPVKdYmGtvTm7b2atnQRgjLk7jcMeAd6x1p5JfP0msAB4Op19FzDG9AB6ABc80S+Sn3XqdDNNmtTmscdGsWHDTgDWrfuKXbsi6dZtBldc0czZBEVERLxAVh7Sy2lXADuSvN4BlDLGFE9n3wWste9aa4OttcFlymgFcZFzrryyEsuXh9Gz553u2NGjuwgPv4GffgrD5XKlcbaIiEje540FsgHik7yOS/zTJ519IpJBBQsWYPz4p5gxYyDFixcGIC4unq+/foG33mrD8eMHHc5QRETEOd5YWO4BqiZ5XRU4ScLycmntE5FMuv/+5qxZM5amTa90xzZt+omQkPps2bLAwcxE8r7Uuglkd5cBT9zHm1tAq6W1ZEWGWk1n+02NWQRMttbOMMaUBr4DnrDWbjfGdAG6ALdZa88aY94CillrO6e1L637qdW0SNpiY+MYMuRTRo+e5Y4ZY7jjjkG0bfsavr6ZXhFSRETE62S01XSK3/WMMb3TOGcKEGqtfS7xWB9gkbX2pixlCoWBy4Fz84g/AWoCq40xccAmzj+El9Y+Eckif38/hg17nJYt69KlyzgOHYrCWssPP4Syfftiunb9lFKlqjidpoiIiEekOIJsjFmZyvEWaAscAupwfj7wNmutb45kmA00giyScf/+e5QuXcbxyy/r3bEiRUrx+ONTqF//HgczExERuTQZHUFOcQ6ytfa6VLbmQBQJD8ttBrYnbiKSR1SoUIo5c4bw+usd8PFJ+C/i1KmjvP32vXz++f+IjY1xOEMREZGcldWH9CxQDCgBXJZ96YiIN/D19eWllx5iwYIQKlcu7Y7/8subjBrVnIMH/3QwOxERkZyVVqvpbsaYhYnbU8n3W2tPW2uPA0dyNEMRccz111/NmjVjadu2iTu2e/evDBvWiDVrPnMwMxERkZyTYoFsjOkLvE7C6hLfAi8bYwZ4MjER8Q6lSwfy1VcvER7ejQIFEp7rjY4+wQcfPMYnnzxJTMwphzMUERHJXqmNIPcGHrPWjrXWjgMeToydY4wx7xtjpgIzcjpJEXGWMYann27L0qUjqVmzgju+YsWHDB9+LXv3bnAwOxERkeyVWoFcGVif5PV6Lp5rfArYD2gyokg+0bBhDSIiwmnf/vyqjvv3b2bEiCYsWfIOTqyrLiIikt1SK5A3Am2SvL4D2Jb4sQXirbXPWGufB14lYVULEckHihUrxMcfP8t77/WlcOEAAGJjo/n00568994jnDkT5XCGIiIilya1Ankw8K4x5gtjzAwSGnS8AmCtdVlr/c8daK2NA6rneKYi4jWMMXTufCsrV44mKOh89/dff/2C0NCG/P33agezExERuTSprYM8D7iRhDWOdwItrbXfp3YRa+2uHMlORLxanTpVWLFiFN27t3bHDh/+m1Gjrufnn8fgcrkczE5ERCRrUl3mzVr7q7V2kLX2RWttpDFGLbRE5CKFCgUwcWIvPv30eQIDCwPgcsXx1VcDmDTpbk6cOORwhiIiIpmTmUYhX6e2wxjTKBtyEZFc7MEHr2fNmrFce20td+yPP+YSEtKArVsXOZeYiIhIJqXVKOQxY8xdxpgmxphyJHsQzxhTIPHPSiSslSwi+Vz16uVZuHAYzz3Xzh2LitrHuHG3MHv2EFyueAezExERyZi0RpCnAaOA74FIElavSOpvY8yVwNXA7zmTnojkNgUK+DNixBN8992rlCkTCIC1ljlzhjJ27C38999ehzMUERFJW3pTLIKtteVIKIIBMMb8aIwpA5QnoUnIrcDynEtRRHKjO+5oTGTkOFq0uMYd2759CSEh9dmwYY6DmYmIiKQtrQLZAqWNMTcBSecY3wQUAM6S0EDkWRJaUouIXKBixVL8+OPrDB78KD4+Cf/dnDp1hIkT2/Lll/2JizvrcIYiIiIXS6tANkAzYEHiljRuAF9gJBBjrd2YYxmKSK7m6+vLK688wrx5Q6lUqbQ7Pn9+OKNGXc+hQzsczE5ERORiaRXI/YBZ1lr/pI1BOD8X2VhrNwGbjTGNcyxDEckTbrrpGtasGUubNsEVW5OdAAAgAElEQVTu2K5dkYSGNiQycqaDmYmIiFworQJ5PBBjjDlkjJmXJJ68rfQcoB0iIukoUyaQr79+mVGjuuLv7wdAdPQJ3n+/PdOm9eDs2dMOZygiIpKBh/SAu4A3k8Q6AUeSvF4INM/mvEQkjzLG8Mwz97BkyQiuuKK8O75s2XuMGNGEffs0Y0tERJyVXoG8zVq72lrrfuTcWvultTYaOLdW00bgmhTPTsYY08IY86sxZr0xJtIY0yyFY340xqxKsq0zxvyduK+xMeZosv39M/ZWRcSbNG5ck4iIcB566AZ3bN++jQwffi3Llr2PtclXlhQREfEMvzT2vQrEpLbTWls98c+jxpjx6d3IGFMCmAW0tdauNMa0BL41xlS31rp/r2qtvSPZec8C51pzlQG+stZ2T+9+IuL9ihcvwrRp/bn11vo8++x7nDlzltjYM0yb1p0tWxbQocM7FCoU6HSaIiKSz6Q6gmytDbXWJm17tTutYzNwr9bAVmvtysRzFgH/krCOcoqMMYWA/wHDE0NlgNuNMRGJW6gxplgq5/ZIHKWOPHz4eAbSExEnGGPo2rUVK1aMpk6dKu54ZOQMhg1rxK5dkQ5mJyIi+VF6Uyzczo0YX4IrgOTrOe1IjKemFzDXWvtP4utZQDVrbVPgTqA68ElKJ1pr37XWBltrg8918xIR7xUUVJWVK0fz5JOt3LFDh3YQFtacBQvGacqFiIh4TIYL5GxggPhksbjUckhh9Bhr7Rmb+F3SWnsU6A/cnXisiORyhQsH8PbbfZg2bQDFiiV8WcfHx/LFF88yadI9nDx52OEMRUQkP/BkgbwHqJosVjUxnpLewA/W2lSndpDQrCSaNOZKi0ju8/DDN7B6dTiNG9d0xzZs+J6QkAZs377EwcxERCQ/8GSB/C1QzxhTF8AY0wS4CvjFGLPcGHPuQTyMMYVJGD0elvQCxpj2iQ/7YYzxI2F0eaq11uWh9yAiHlKjRgUWLx7OM8/c444dO7aXsWNvZs6cobhcyX8hJSIikj08ViBba6OAh4APjTGrgXFAG6AwcDlQPMnhvYEfrbW7kl2mELDAGLMGWAHsB57L6dxFxBkFCvgzalRXvv76ZUqXTnge1+VyMXv2EN58sxXHju1zOEMREcmLTH548KVx45p21aoxTqchIpdgz57DdO48lqVLzzcSKVasLE888QlBQXekcaaIiEiCnj3NWmttcHrHeXKKhYhIllWuXIaffhrKK688gjEJHe9PnDjEhAl38tVXA4mLO+twhiIikleoQBaRXMPPz5fBgx9l3ryhVKhQ0h3/+edRjB59I4cP/+1gdiIikleoQBaRXKdFi7pERo7jjjsauWM7d64mNLQha9d+6WBmIiKSF6hAFpFcqWzZ4nzzzSuMGPEEfn6+AJw5E8V77z3Ep5/24uzZMw5nKCIiuZUKZBHJtXx8fHjuuXYsWjScatXKueNLlkxm5Mim/PvvZgezExGR3EoFsojkek2a1Gb16nAeeKC5O7Z37waGDw9mxYopalMtIiKZogJZRPKEEiWK8umnzzNxYi8KFiwAwNmzp/nkk65MmdKJ6OgTDmcoIiK5hQpkEbnAwYOLiYzszvLl9xEZ2Z2DBxc7nVKGGWPo3r01y5eHcdVVld3x1aunExraiN27f3UwOxERyS1UIIuI28GDi9mxYxIxMYcAS0zMIXbsmJSrimSAunWrsXLlaJ544lZ37NChPwkLu45ffhmvKRciIpImFcgi4rZ79zRcrpgLYi5XDLt3T3Moo6wrUqQg777bl48/fpaiRQsCEBd3ls8/f4bJk+/j1KmjDmcoIiLeSgWyiLjFxBzOVDw3ePTRFkREhNOw4RXu2O+/f0tISAP+/HOZg5mJiIi3UoEsIm4BAWUyFc8tatWqyJIlI3n66bbu2H///UN4eEvmzg3F5Yp3MDsREfE2KpBFxK1q1Y74+ARcEPPxCaBq1Y4OZZR9AgL8CQ/vxldfDaJkyaIAuFzxfPfdK4wf35qoqP0OZygiIt5CBbKIuJUr14IaNXoTEFAWMAQElKVGjd6UK9fC6dSyzd13NyEycizXX1/HHduyZQEhIfXZtGmeg5mJiIi3MPnhae7GjWvaVavGOJ2GiHiRuLh43nhjBiNGfHnBqhatW7/APfe8ga+vv4PZiYhITujZ06y11gand5xGkEUkX/Lz8+X11zvwww+vcdllJd3xn34ayZgxLThyZJeD2YmIiJNUIItIvnbLLfVZs2YsrVo1cMf++msloaENWLduloOZiYiIU1Qgi0i+V758CWbPHkxo6OP4+ib8t3j69DHeeecBPvvsaWJjox3OUEREPEkFsogI4OPjw/PP38/ChcO4/PKy7vjixRMZObIZ+/dvdTA7ERHxJI8WyMaYFsaYX40x640xkcaYZikc09gYc9QYsyrJ1j9xnzHGvGGM2WqM2WSMmWaMKeLJ9yAiFzt4cDGRkd1Zvvw+IiO757rW1Ek1a3YVq1ePpV278/897dnzO8OHN2bVqk8czExERDzFYwWyMaYEMAvoY62tBwwAvjXGFE52aBngK2ttsyTbuSUoOgNtgAbW2quBWGCkh96CiKTg4MHF7NgxiZiYQ4AlJuYQO3ZMytVFcsmSRZk58wXGj+9BQEDCahYxMaf46KPOfPRRZ6KjTzqcoYiI5CRPjiC3BrZaa1cCWGsXAf8CtyY7rgxwuzEmInELNcYUS9z3CPCOtfZM4us3gUdzPnURSc3u3dNwuWIuiLlcMezePc2hjLKHMYaePduwbFkYtWtXcsdXrfqE4cMb888/vzmYnYiI5CRPFshXADuSxXYkxpOaBVSz1jYF7gSqA+d+r5n8GjuAUsaY4slvZozpkTiNI/Lw4ePZkb+IpCAm5nCm4rlN/frVWbVqNJ063eyOHTiwjZEjm7Fo0UTyw1ryIiL5jScLZAPEJ4vFJc/BWnvGJn7HsdYeBfoDdxtjCqVwjbjEPy96H9bad621wdba4DJlArPpLYhIcgEBZTIVz42KFi3EBx88w4cfPkORIgUBiIuLYcaMp3nnnQc4deo/hzMUEZHs5MkCeQ9QNVmsamI8Lb5ANBCTwjWqAieBY9mUo4hkUtWqHfHxCbgg5uMTQNWqHR3KKOd07Hgzq1aNoV69au7Yb799TWhoA/76a6VziYmISLbyWKvpxGkQO4CbrbUbjDFNgJ+AmsB3wBPW2u3GmPbAj9baY8YYP2AKcNJa28sY0wXoAtxmrT1rjHkLKGat7ZzWvdVqWiRnHTy4mN27pxETc5iAgDJUrdqRcuVaOJ1WjomOPsuLL37EpElz3TEfH1/uuSeE228fiI+PVtAUySg/v1hq1NhD4cJab1wuTXy8LwcOlODgwTJYm/L/wxltNe2X7dmlwlobZYx5CPjQGGNJmB7RBigMXA6cm0dcCFhgjHEBFlgMDE7c9wkJBfVqY0wcsAl42lPvQURSVq5cizxdECdXsGABxo3rQcuW9ejRYwLHjp3C5Yrnm29eYtu2hTzxxCcEBpZ3Ok2RXKFGjT1UqVKMYsWqYYxxOh3Jpay1xMfHEhh4gKJF97BjR/JJC5njsRFkJ2kEWURyyq5dB3n88XBWrtzijgUGlqdLl2nUqXObg5mJ5A4NG26mWrWrVBxLtrDWxc6dW1m3rk6K+zM6gqzfA4qIXILLLy/H/PkhDBz4gPsb/PHjBxg//na++eZl4uPj0rmCiKg4luxiTPaUtiqQRUQukb+/HyEhnZgzZwjlyiXMFrPW8uOPwwgPb8nRo7sdzlBERDLDY3OQReRCnniwbcOGwRw/vt79OjCwHnXrDs32vDz1kJ63Pwx4220NiIwcR5cu41iw4HcAduxYTkhIAx5/fAoNGtzrcIYiIt6jceNqvPbaaO6++8EMn9OhQ1vq1w9m4MDXci4xNIIs4ghPtGdOXhwDHD++ng0bBqdyRtby8lSr6dzS0vqyy0oyZ84Qhg7tgK9vwn+xp0//x+TJ7Zg5sx+xsTHpXEFEvF27di15440XU91/4MC/XHllaX76afYl3+vIkcM891x3GjW6nKCgywgOvoKXXurLmTNn0j8Z2LDhNyZOHH3JeWTFqVOn6Nv3CapXL8bVV5cnPDwkzeOXL19EuXLeMd1GBbKIAzzRnjl5cZxePKt5earVdG5qae3j48OLLz7EggWhVKlyvmHKwoUTCAu7jgMHtjuYnUjeEhQE5cpdvAUFOZdTQEBBrrmmISVLlr7kaz355INER0ezZMlGNm7cz/z5a6lc+XJiY89m6PyNG39j0iRnCuSBA3uxefMGvv9+OW+/PZ133x3HJ5+860gumaUCWcQB3tqeOSt5eeq9eOvnLC3Nm9dhzZqx3HNPU3fsn3/WMWxYIyIivK+wF8mNDh3KXNwTSpQoyVdfzadJk+aXfK1161bTqtVdFC1a1H3tPn0GEBhYPJ0znXXo0EFmzfqUUaMmExRUjxYtbqNfv5d4991xTqeWISqQRRzgre2Zs5KXp96Lt37O0lOqVDG++OJFxo3rToECCY99xMScZMqUTnz8cRdiYk45nKGIXIqwsNe45ZaGDBkygKCgy7jzzmbExcVRrpxh+fJFAERELKd16ybUrVuRevUq0a9fF6KiMtYEuHnzloSFDWHVqmWpHvP997No3boJDRtWpXHjavTv34OoqGP07t2Jl19+hiNHDhEUdBm33toIgLi4OMaNG0bDhlW58srSNGlSk2nT3ndfb9u2zdx3383UrVuRoKDL6Nr1Qfbu/cd9r3vvbUG9epUICrqMBx9s5d6X1IYNv1KgQAEaNrzWHbvhhpvZtm0zJ0+ecMdOnz5NVNQxoqPPN4qJi4sjLi6OU6dOsX//PjZv/oO5c78hMnJVhj5n2UEFsogDPNGeOTCwXqbiWc3LU62mc3NLa2MMvXvfxdKlYdSsWdEdX7nyI4YPD2bPntSnvYiI9/vjj98oWrQY69fvZdashRftf/nlftx4461s2LCPlSu3Ub9+Y44fj8rQtSdM+Jjata/m3ntvomXL+syY8THx8fHu/d999wUzZ37E1KmzWbduN0uXbuL06dMMGtSPSZOmEhr6JqVLl2Xjxv0sWPArAKGhg5gzZxZz5qxg69YjTJs2m3HjQpk69T0Ahg9/hfLlK7B+/V5+/XUXbdrcx8GD+wHYsuUPBg0axrp1u/n99z2ULFkqxbnFJ04cp3jxkhfESpVKGNA4deqkO9a3b2dq1SrJqFGvuWMVK/pTsaI/1asXpXnzq+je/WFmzvyY06c9N6CgVSxEHHBu5YWcXJGhbt2hmV7FIit5eeK9ePI+OalhwyuIiBhD376T+fTThIcL9+/fwogRTXj44XHceONTWg9WJBeqUKESAwYMxhhDoUKFiIu7cP3zRo2asmzZL/z2WyQNGgTz5JPnmwDffvu1KY7AbtyYUJCWKVOWjz/+mm3bNvPBB2/x/PNPMX36+8yY8SNFihRhxIhXOXz4IDffXN99bnx8HGXLptzN01rLlCkTmTz5MypWrAxA7dp16NVrAO+/P55OnbrTqFFTpk9/n2XLFnLjjbfw4IMd3OcPGDCYI0cOs2TJAvbs2cWJE8c5evTiqW6XXVaJqKj/LogdPXoYPz8/ypQp54598MEX7lUsXC4Xu3cnPHxojMHf3x8fnwvHcqdNe4+CBQum+N6ykwpkEYd4oj1zeku6pSQreXmq1XReaGldrFghpkz5H7fcUo9+/d7l9OkY4uJi+PTTXmzZsoCOHd+jcOESTqcpIplQsWLlNH+4HTlyIjNnfsLzz/fEWsvTTw+kXbtHAJg3b02G7lG7dh1GjpxIhw5P0rbt9bz//nieeeYldu36i0mTpnHvvQ9n6DqHDx/i9OnTVKp0YSvmKlUuZ9euvwDo23cgtWpdxZgxQ3nhhd706PE/Hn+8B/Hx8fTt25k//viNVq3aUrny5VSrVoMtW/646D61al3F2bNniYxcRXBwMwCWLFlArVpX4evrm2JuPj4+FCxYkJiYGKKjzxAdffFKHaNGTSYgQAWyiEieY4zh8cdv5dpra9Ohw2j++GMXAL/++iW7dkXSrdsMqldvms5VRASgbNmUH8grW9bzuaTGGEP79p1p374za9dG8NhjbShQIIA2bdqleZ61FmvtBaOo9eo1onbtq92jzhUqVGLdujUZLpBLly5D4cKF2bNnF3XrNnDH//lnJ1WrVne/vuOOe7jjjnvYvn0LHTq0JSYmmuLFS7BixWJWr97hHsUdP35EigVyqVKluf/+xxg4sBcTJnzM4cMHGT9+OK+8MiLdHN9/fwKvv/58qvsHDBiidZBFRPKqOnWqsHx5GE89dYc7duTITsaMuZ5580bhcrkczE4kd9i4EQ4evHjbuNHpzBJERR1j5Mgh7oK2SpVq+PsXyNAc5OPHo2jf/k62bt3kjv3yy49s27aJNm3uA+CZZwYxZcpE5s79BpfLRXx8PIsXz2fOnK8BKFSoMGfOnObkyZPs37+PM2fO8OSTfRkzZij79u0BEh7Ke/vtMXTr1g+AkJCX2LFjG5BQgBcuXITjx6OIiYkhLi6WY8eOArBs2UImTw5PNf+wsLe55poGtG17Pb16daBXr/48/niPDH3emjdvwcGD9qKtVau7MnT+pdIIsoiIgwoVCmDChJ7cfHM9nnrqLaKiThMXF8+sWQPZuvUXOnf+mMDAculfSES8UsGChYiOPsP999/CiRPHCQgIoH37J3joofQfMC5SpCjBwdfRpcv9nDhxHGMMVapUY/Lkz2jZshUAnTp1x8/Pj7CwITz3XDf8/QtQp05dBg0KBeCmm27jyiuDqFOnDOXLV+Dbb5cwaFAoxYoF0qbNdZw5c5rAwBI888wgOnXq7r7vE0/cx5Ejh/Dz86dVq7b06fM8xhhWrlxMixZ18fPzp1mzG3niid6sWLEolfyLMH78FMaPn5Idn0qPMtZap3PIcY0b17SrVo1xOg2RC/z552QOHJgHuAAfype/nZo1e6Z5TmYfulML6Nxl584DdOw4htWrt7ljxYtXoEuXaVx11S0OZiaScxo23Ez16nWcTkMckFqr6YkTR/PDD98wffr3F53To0d7GjduluYUi7//3sy6dSn/m+rZ06y11ganl5umWIg4IKE4/pGE4hjAxYEDP/Lnn5NTPSezraPVAjr3qVatPAsXDqN///vcsaiof3nzzdv47rvBxMfHpXG2iEjesXr1cmrVKnnRtnDhTx65vwpkEQckjBxnPA6Zbx2tFtC5k7+/H8OHd2b27MGULZvQKctay9y5bzB27C38998ehzMUEckea9fuvGj0GKBPnwEpzj8+t+X0A3qgAlnEIak9fJV9D2WpBXTu1rp1I9asGUvLlnXdsT//XEpISAPWr7/4144iIpJ9VCCLOCK1L73s+5JUC+jcr2LFUvzww2u89tpj7mWeTp06wqRJd/P5588SGxuTzhVERCQrVCCLOKB8+dszFYfMt45WC+i8wdfXl0GDHmb+/DeoXLm0O/7LL+MYNep6Dh7808HsRETyJo8WyMaYFsaYX40x640xkcaYZikcU94Y844xZrMxZrUxZqkxpm7ivtLGmFPGmFVJttGefA8i2aFmzZ6UL38H578EfShf/o40V7GoW3foRcVwWqtYlCvXgho1ehMQUBYwBASUpUaN3jnSAtoT98nvbrghiDVrxnLXXde6Y7t3r2XYsEasWfOZg5mJiOQ9HlvmzRhTAtgBtLXWrjTGtARmAtWttaeTHHcnUMhaOyvxdX+gtbX2dmPMlcBb1tpWmbm3lnkTkbzCWstbb33Piy9+TGzs+VUtrr/+SR55ZDwFChR2MDuRzNMyb5Ldctsyb62BrdbalQDW2kXAv8CtSQ+y1v5wrjhO9C/nG5qUAeoZY1YaY9YYYyYYY8rnfOoiIt7BGEPfvnezdOkIatS4zB1fvvwDhg+/lr17L275KiIimePJAvkKEkaQk9qRGE9RYvE7FHg9MRQJVLTWXge0BM4Cc40xJoVzeyRO44g8fPh4NqQvIuI9GjWqSUREOI88cqM79u+/mxgx4lqWLn2P/NAESkQkp3iyQDZAfLJYXGo5GGNKA3OB16y1iwGstTHW2vjEj08BA4GrgJrJz7fWvmutDbbWBpcpE5h970JExEsEBhbmk0+e4513+lCoUAEAYmOjmT69Bx988ChnzkQ5nKGISO7kl/4h2WYPcFuyWFXgy+QHGmMqAD8AYdbaT9O4piGhwNYQsaTIUy2Qs9I2eu3avkRH/+N+XbBgFRo3npDmOcuXP8CFP2f6cv31X6V6/IoVHUj4WTKBMUVo3nx6mveIiOhKXNxR92s/v1I0bfphmueopbVzjDF06dKKpk2vokOHUWzcuBuAyMiZ7Ny5hm7dZlCt2rXpXEVEMqtdu5Y0btyMV18dcdG+Awf+5aabrmH8+I9o3fruS7rPkSOHCQ19iUWL5hETE0OhQoVp1eouBg8Oo1ChQumev2HDbyxZMp8+fQZcUh75jSdHkL8lYf7wuRUpmpAw+vuLMWa5MaZWYvxyYAHwRvLi2BhzT2LxTOK0iqHAImvtAQ++D8klPNUCOStto5MXxwDR0f+wdm3fVM+5uDgGiE+MXyx5cQxg7SlWrOiQ6j2SF8cAcXFHiYjomuo5amntHa6+ugorVoyiW7fzSwUePvwXYWHN+fnnMbhc2deERsTbHDs2nW3bqrFxow/btlXj2LG0BwJyWkBAQa65piElS5ZO/+B0PPnkg0RHR7NkyUY2btzP/PlrqVz5cmJjz2bo/I0bf2PSJC34lVkeK5CttVHAQ8CHxpjVwDigDVAYuBwonnjoGKA88HySpdzOfQe0wCxjTCQQAZQDUv9uL/map1ogZ6VtdPLiOL14guTFcdrx5MVxenHgouI4vTiopbU3KVQogEmTejN9+gACAxNWs3C54vjqqwFMmnQ3J0+qu6HkPceOTWffvh7Exu4CLLGxu9i3r4ejRXKJEiX56qv5NGnS/JKvtW7dalq1uouiRYu6r92nzwACA4unc6ZcCo+ug2ytXWitvdZa28Ra29xau9Ja+4+1trK1NjLxmAettaWttc2SbC0S98221l6XOLe4ibW2m7U29e/ckq95rgVyzreN9mZqae19HnroBlavDic4uJY79scfc3njjfps26YRd8lbDh58mSSrxQJg7WkOHnzZI/cPC3uNW25pyJAhAwgKuow772xGXFwc5coZli9fBEBExHJat25C3boVqVevEv36dSEq6liGrt+8eUvCwoawatWyVI/5/vtZtG7dhIYNq9K4cTX69+9BVNQxevfuxMsvP8ORI4cICrqMW29tBEBcXBzjxg2jYcOqXHllaZo0qcm0ae+7r7dt22buu+9m6tatSFDQZXTt+iB79/7jvte997agXr1KBAVdxoMPtnLvy0vUSU/yLM+1QM75ttHeTC2tvdMVV1zGokXDePbZe92xqKh9jB17C99//zouV2q/kRDJXWJjd2cqnhP++OM3ihYtxvr1e5k1a+FF+19+uR833ngrGzbsY+XKbdSv35jjxzP2EO2ECR9Tu/bV3HvvTbRsWZ8ZMz4mPv781+93333BzJkfMXXqbNat283SpZs4ffo0gwb1Y9KkqYSGvknp0mXZuHE/Cxb8CkBo6CDmzJnFnDkr2Lr1CNOmzWbcuFCmTn0PgOHDX6F8+QqsX7+XX3/dRZs293Hw4H4Atmz5g0GDhrFu3W5+/30PJUuWIjw85FI/hV4nf3wHl3zJUy2Qs9I2umDBKpmKJ/DNVNyYIpmKQ8IDeZmJg1pae7MCBfwZObIL33zzCqVLFwPAWhfff/8a48bdxrFj+xzOUOTS+ftXzVQ8J1SoUIkBAwbj6+ub4oNzjRo1ZdmyX/jtt0iKFCnCk08+TZUqlwNw++3XEhR02UXbOWXKlOXjj79m6dKNNG16A88//xTt2rXk1KmE6XIjRrxKRMQybr65PkFBl3HttVewcOGPrF+/NsVcrbVMmTKR/v0HU7FiZQBq165Dr14DeP/98e58f/stkmXLFhIQEMCDD3agYcOEh30HDBhMzZpXsmTJAj77bAonThxn58681/JeBbLkWZ5qgZyVttGNG0+4qBhObxWLhNUqkhfDqa9i0bz59IuK4fRWsWja9MOLiuH0VrFQS2vv16ZNMJGR47jppiB3bNu2RYSE1GfDhrkOZiZy6cqVC8WYCztIGlOYcuVCPZZDxYqVSaElg9vIkRN54onePP98T267rTHffDPTvW/evDVs3Lj/oi252rXrMHLkRObMWcHvv0e6i9ldu/5i1KjJF5y7Zcthli7dmGIuhw8f4vTp01SqdOEPEFWqXM6uXX8B0LfvQF57bTRjxgylefOr+OijybhcLmJjY+nZ8zHuvfcmliyZz9mzZ6lWrcYFI9p5hSeXeRPxuHLlWnikgKpZs2e6y7oll96SbilJa0m3lKS3pFtK0lvSLSWe+jx76j55UaVKpfnpp6EMG/YFoaGf43K5OHnyMBMn3sVtt/WnXbth+PkVcDpNkUwrUSLhWf2DB18mNnY3/v5VKVcu1B33BsYY2rfvTPv2nVm7NoLHHmtDgQIBtGnTLs3zrLVYa/HxOT+eWa9eI2rXvto977dChUqsW7eGe+99OEO5lC5dhsKFC7Nnzy7q1m3gjv/zz06qVq3ufn3HHfdwxx33sH37Fjp0aEtMTDTFi5dgxYrFrF69g4IFCwIwfvwItmzJex08NYIsIpJP+Pr68uqr7Zk3bygVK57/TcH8+WMYNeoGDh36y8HsRLKuRIkO1K69k6AgF7Vr7/Sq4jgq6hgjRw5xF7RVqlTD379AhuYgHz8eRfv2d7J16yZ37JdffmTbtk20aXMfAM88M4gpUyYyd+43uFwu4uPjWbx4PnPmfA3w//buPbjK+s7j+PsTAkGQy7aIOmLkOjRFrULWYjugtkpb1o6rNS22qKhoFeu17nGh8xQAAAzcSURBVLhL7S5W8MrFaitIHacLaG/WKepUV2sVtVwEdcpaLUszlAwgclMgNYQT8t0/ziEejyE5ET1PcvJ5zZyB5/v8nuf5JvNM8uXH78Ihh/Sgru49amtr2bx5E3V1dVxyyVXMmvUjNm3aAKQn5c2dO4vJk68GYPr0/6C6+v+AdAHeo0dPdu3aSX19PQ0NKd59N70+wksvPce8ebM/pu9U++IeZDOzTmbs2GNZuXIOkyffw5NPpscprl+/khkzTmTixJ9RWZlfT5SZta5790PYs6eOc875Ert376KsrIwJEyZRVdX6/ImePQ+lsvJkLrroHHbv3oUkjj56IPPm/YJTTz0DgPPPv5TS0lLuvPO/uP76yXTt2o2KiuOYOjU9xGTs2NMZPnwEFRX9OPzwI1m8+AWmTp1Br169GT/+ZOrq3qN3775cc81Uzj//0qbnTpp0Ntu3b6W0tCtnnHEmV175b0hi2bIlnHLKcZSWdmX06DFMmjSFpUuf/4S+e8lRRCSdwydu1KihsXz5rKTTMDNrVxobG7nnnsf5wQ8Wkko1NMXHjLmMqqq76dat9V26zA7WiSe+yaBBFUmnYUVk3bo3ee215t+pyy/XKxFR2do9PMTCzKyTKikp4dprz2LJktsYNOjwpviLL87n9ttPYtOmN1q42syseHmIhVmOLVuWUFOziPr6bZSV9aO8fGK7mRjW1txyt7RubaUM65wqK4fx8suzueKK+3jkkT8BsGnT69x114mce+5cvvCFi1qcoW9mVmzcg2yWZcuWJVRX30d9/VYgqK/fSnX1fWzZkvzuY23NLbc4hvRW1q+8clUBsrWOpk+fnjz00A3MnTuF7t3Tq1nU1e1l4cJLePDBidTV7Uo4QzOzwnGBbJalpmYRjY31H4g1NtZTU7MooYze19bccovj1uJmkrjkknEsXXoXFRXvr9O9cuXD3HrrSNavb37jATOzYuMC2SxLff22NsULqT3nZsXl2GOPYdmymVx00elNsa1bq7nzzpN59tm76QyTu82sc3OBbJalrKxfm+KF1J5zs+LTo0cZ99//PRYsuJ5evdKrWezbl+I3v7mOuXPPorZ2e8IZWjHxP7rs4/JxvUsukM2ylJdPpKSk7AOxkpIyystbX6/yk9bW3HK3sm4tbtacCRPGsmLFbEaOHNIUW736cWbMOIG1a19MMDMrFvv2dWHfvlTSaViRSKXqSKW6HvR9XCCbZenf/xSGDJlCWdlhgCgrO4whQ6a0i1Us2prbqFH3fqgY9ioW9lEMHXokS5bcztVXf70p9s47G5gz51R+//vpNDbuSzA76+jefrsvO3a8TURj0qlYBxYR7N37Hm+9tZGamv4HfT9vFGJmZnl74omXmTz5Xnbs2N0UGz78S1x88SL69Dkywcyso5IaGTx4A717/yPpVKyDS6W6UlPTn127eh+wTb4bhbhANjOzNtmwYRsXXDCbl156fyORXr0OY9KkBYwY8dUEMzMza5l30jMzs0/EgAH9ePrpW5g69ZtNG4js3r2Ve+/9Go8+eqPHk5pZh+cC2czM2qy0tAvTpn2bp566mSOO+Kem+NNP38nMmWPYtu3vySVnZnaQClogSzpF0quSVktaJWl0M20k6RZJayS9IWmRpJ6tnTMzs8I77bTjWbVqDuPGndgUW7duBTNmnMCrr/42wczMzD66ghXIkvoCjwJXRsTxwA3AYkk9cppeCIwHToiIzwIp4I48zpmZWQL69+/LY4/9kNtuu5DS0i4A1NXtZP78c3n44SmkUnsSztDMrG0K2YP8FWBNRCwDiIjngbeAL+e0+xZwf0TUZY5/DJyXxzkzM0tISUkJ3//+2Tz33K0MHPj+EksvvDCX22//PJs3/zXB7MzM2qa0gM8aDFTnxKoz8ZbaVQOfktSnpXMRsTP7JpIuAy7LHNZ36/avrx9k/tax9QO8J3Pn5ncgIRs3rmbatIqk0wC/A+Z3wGB4Po0KWSALyF1NvoEP92LntmvI/FnSyrkPiIj5wHwASavyWdLDipffAfM7YH4HzO+ASVqVT7tCDrHYAJTnxMoz8ZbalQO1wLutnDMzMzMzO2iFLJAXA8dLOg5A0knAZ4A/SvqTpGGZdguByZK6ZY6vAh6N9I4mLZ0zMzMzMztoBRtiERE7JVUBD0oK0sMjxgM9gGOAPpmmC4ChwMuSGoA3gO/lca4l8z+2L8Q6Kr8D5nfA/A6Y3wHL6x3oFFtNm5mZmZnlyzvpmZmZmZllcYFsZmZmZpbFBbKZmZmZWZaiLpAlnSLpVUmrJa2SNDrpnKywJHWVdIOklKQJSedjhSfpu5L+nPkZsFrSlKRzssKSdEvmHXg58zvhiqRzsmRIGiFph6RpSedihSXpfyWtlLQ88/ljS+0LuVFIQUnqCzwKnBkRyySdCiyWNCgi3ks2OyugS4EAliediBWepC7AMOCLEVEr6Sjgb5IWR8TGhNOzwnkHqIyIlKTDgHWSnoqIdUknZoWTqQt+Cvwi6VwsEb2Az0VEYz6Ni7kH+SvAmohYBhARzwNvAV9OMikrrIi4LyJm8eFdHK0TiIh9EXFDRNRmQtuBvUCXBNOyAouI2RGRyhwOJL3B1I7kMrJCk1QC/DcwFdiacDqWjE8BSyS9JunXkk5oqXExF8iDgeqcWHUmbmad093AryKiJulErLAkDZO0FvgfYGJE7Ew6Jyuo6cAzEbE06UQsMYdHxBhgFOnN6/4gKXeH5ybFXCCLD/caNlDcX7OZHYCk6cBR5Le5kBWZiFgbEcNI/y/iAkkjks7JCkPSN4DyiPhJ0rlYciKiLvNnY0Q8BLxCerRBs4p2DDKwATg9J1YOPJJALmaWIEkzgSHANyJib9L5WHIi4jVJy4DTgL8knY8VxNeACkn756IMgPSEvYioSi4tS1gXYNeBThZzb+pi4HhJxwFIOgn4DPBMolmZWcFIKpE0DzgaqHJx3PlIOk7SNyUpc3wU8HlgVbKZWaFExOSIGBURoyNiNPAA8ICL485D0j9LGpV1PB6oID3kqllF24McETslVQEPSgrSwyvGR8S7CadmZoUzHvgu6WLopUyNBHBTRPwhsayskNYDlwM3SkoB3YAfRoRXtjHrPGqB2ZKOAOpJT9Id11JNqIgoVHJmZmZmZu1eMQ+xMDMzMzNrMxfIZmZmZmZZXCCbmZmZmWVxgWxmZmZmlsUFspmZmZlZFhfIZmYdkKRxklZIqpW0RtLVOeeXS2rTroFZawVXZpbHbMu1P5fkncrMrCi4QDYzS5ikmyRF1ueqnOOQNDGr/Ujgd8BPgWNIr/X875Iuy/N5D0lqyPqkJDUCV7RwzWBJz0jaI6la0oSD+6rNzNovF8hmZsm7A+gFjAQCmAd8CxiYif8V2JnVvgp4MiIWRMT2iHgemAl8J8/nnQ90z3zKgBGkF89f3FxjSWXAk8Am4FjgR8DPJY3J+ys0M+tAinYnPTOzjiIiUkBKUjfgnYhISXoYOD0i1kvqB2zNuqQE2Jd7mzY8rxFoBMjsLPVrYFpEbDzAJWcB/YFLM9t1/03SWOA64MV8n2tm1lG4B9nMrP3oB7yWHZB0KLAHWJcVfgQ4U9K5knpKGg1cD/wq3wdJKpH0HWAF8FhE3NFC85OApZnieL9nM3Ezs6LjHmQzswRJ+jvpccTZsf29wc9lhTdn5tCdHRG/y4wBvgVYBGwGfgLMzeN5PYH/JD1M423gwswQjZb0BrbnxLaRHv5hZlZ0XCCbmSUoIgZ+xOseAx77CNf9Q9Ja4OvAGgBJub8L1gPnZR1v5MO9xf0ycTOzouMC2cysnZBUBSzMCb8QEeMO0L4LoAPc7l+AuuZORMQDmes3H+DaUuDTwC8zx68D10nqHhF7MrEzgL8c4Hozsw7NBbKZWfvxBDA0J1YvSRHR3CS8eqBLC/f7MXDtgU5GxBHNxSWdwAfHQj9OejjGzyTdDHwR+DZwegvPNjPrsFwgm5m1A5mxyAOyQgE0AN2ALZIGZfXephtElEq6Bzg0Ii7O3KcnUAt8NiLebOWZea18ERF7JX0VmE+6N3kjcEFEeAULMytKLpDNzNqB3LHIkkYANwKfA87LLY6z7CW9lvF++3+uN+T56Ergz3nkt470sAozs6LnAtnMrB2Q9GnSG3acRnr8cAXpjTzmAAMk7QI2Zg+1yEyuawC6Z020218sh6QuEZG7XnKuLhzgd4GkxsyayWZmnYrXQTYzS5ikm4A3gZtJD6m4kvTSaiNJ76B3Den1ikdlXTMQSJHuZT4n8/cU6bHCAGuBJXk8fgXpyXzNfZqdHGhmVuzU/LwPMzMzM7POyT3IZmZmZmZZXCCbmZmZmWVxgWxmZmZmlsUFspmZmZlZFhfIZmZmZmZZXCCbmZmZmWVxgWxmZmZmluX/AUXePQk1nC/sAAAAAElFTkSuQmCC\n", "text/plain": [ - "" + "
" ] }, "metadata": {}, @@ -154,16 +172,16 @@ "zz = y_predict.reshape(x0.shape)\n", "\n", "plt.figure(figsize=(10, 4))\n", - "plt.plot(X[y==0, 0], X[y==0, 1], \"bs\", label=\"Not Iris-Setosa\")\n", + "plt.plot(X[y==0, 0], X[y==0, 1], \"bs\", label=\"Iris-Setosa 아님\")\n", "plt.plot(X[y==1, 0], X[y==1, 1], \"yo\", label=\"Iris-Setosa\")\n", "\n", "plt.plot([axes[0], axes[1]], [a * axes[0] + b, a * axes[1] + b], \"k-\", linewidth=3)\n", "from matplotlib.colors import ListedColormap\n", "custom_cmap = ListedColormap(['#9898ff', '#fafab0'])\n", "\n", - "plt.contourf(x0, x1, zz, cmap=custom_cmap, linewidth=5)\n", - "plt.xlabel(\"Petal length\", fontsize=14)\n", - "plt.ylabel(\"Petal width\", fontsize=14)\n", + "plt.contourf(x0, x1, zz, cmap=custom_cmap)\n", + "plt.xlabel(\"꽃잎 길이\", fontsize=14)\n", + "plt.ylabel(\"꽃잎 너비\", fontsize=14)\n", "plt.legend(loc=\"lower right\", fontsize=14)\n", "plt.axis(axes)\n", "\n", @@ -175,15 +193,13 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Activation functions" + "# 활성화 함수" ] }, { "cell_type": "code", - "execution_count": 5, - "metadata": { - "collapsed": true - }, + "execution_count": 6, + "metadata": {}, "outputs": [], "source": [ "def logit(z):\n", @@ -198,21 +214,14 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 7, "metadata": {}, "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Saving figure activation_functions_plot\n" - ] - }, { "data": { - "image/png": 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BB/DAA97ORammgSSiSCCaJKI4SwTJhOf7nE4I6YSQQTAZBOf72rGcRSBWLOc9bAQUuE77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\n", "text/plain": [ - "" + "
" ] }, "metadata": {}, @@ -225,13 +234,13 @@ "plt.figure(figsize=(11,4))\n", "\n", "plt.subplot(121)\n", - "plt.plot(z, np.sign(z), \"r-\", linewidth=2, label=\"Step\")\n", - "plt.plot(z, logit(z), \"g--\", linewidth=2, label=\"Logit\")\n", + "plt.plot(z, np.sign(z), \"r-\", linewidth=2, label=\"스텝\")\n", + "plt.plot(z, logit(z), \"g--\", linewidth=2, label=\"로지스틱\")\n", "plt.plot(z, np.tanh(z), \"b-\", linewidth=2, label=\"Tanh\")\n", "plt.plot(z, relu(z), \"m-.\", linewidth=2, label=\"ReLU\")\n", "plt.grid(True)\n", "plt.legend(loc=\"center right\", fontsize=14)\n", - "plt.title(\"Activation functions\", fontsize=14)\n", + "plt.title(\"활성화 함수\", fontsize=14)\n", "plt.axis([-5, 5, -1.2, 1.2])\n", "\n", "plt.subplot(122)\n", @@ -242,8 +251,7 @@ "plt.plot(z, derivative(np.tanh, z), \"b-\", linewidth=2, label=\"Tanh\")\n", "plt.plot(z, derivative(relu, z), \"m-.\", linewidth=2, label=\"ReLU\")\n", "plt.grid(True)\n", - "#plt.legend(loc=\"center right\", fontsize=14)\n", - "plt.title(\"Derivatives\", fontsize=14)\n", + "plt.title(\"도함수\", fontsize=14)\n", "plt.axis([-5, 5, -0.2, 1.2])\n", "\n", "save_fig(\"activation_functions_plot\")\n", @@ -252,10 +260,8 @@ }, { "cell_type": "code", - "execution_count": 7, - "metadata": { - "collapsed": true - }, + "execution_count": 8, + "metadata": {}, "outputs": [], "source": [ "def heaviside(z):\n", @@ -270,14 +276,14 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 9, "metadata": {}, "outputs": [ { "data": { - "image/png": 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+xaOxElhgjPlRuDyZwN/xLmPMK4mx6jFTvEJ7nVWBE49ZKfmrnccsSe0OdSld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\nrkJggg==\n", 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\n", "text/plain": [ - "" + "
" ] }, "metadata": {}, @@ -298,14 +304,14 @@ "plt.contourf(x1, x2, z1)\n", "plt.plot([0, 1], [0, 1], \"gs\", markersize=20)\n", "plt.plot([0, 1], [1, 0], \"y^\", markersize=20)\n", - "plt.title(\"Activation function: heaviside\", fontsize=14)\n", + "plt.title(\"활성화 함수: 헤비사이드\", fontsize=14)\n", "plt.grid(True)\n", "\n", "plt.subplot(122)\n", "plt.contourf(x1, x2, z2)\n", "plt.plot([0, 1], [0, 1], \"gs\", markersize=20)\n", "plt.plot([0, 1], [1, 0], \"y^\", markersize=20)\n", - "plt.title(\"Activation function: sigmoid\", fontsize=14)\n", + "plt.title(\"활성화 함수: 시그모이드\", fontsize=14)\n", "plt.grid(True)" ] }, @@ -313,19 +319,28 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# FNN for MNIST" + "# MNIST를 위한 FNN" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## using tf.learn" + "## tf.learn를 사용" ] }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "import tensorflow as tf" + ] + }, + { + "cell_type": "code", + "execution_count": 57, "metadata": {}, "outputs": [ { @@ -341,16 +356,14 @@ ], "source": [ "from tensorflow.examples.tutorials.mnist import input_data\n", - "\n", + "tf.logging.set_verbosity(tf.logging.ERROR) # deprecated 경고 메세지를 출력하지 않기 위해 \n", "mnist = input_data.read_data_sets(\"/tmp/data/\")" ] }, { "cell_type": "code", - "execution_count": 10, - "metadata": { - "collapsed": true - }, + "execution_count": 58, + "metadata": {}, "outputs": [], "source": [ "X_train = mnist.train.images\n", @@ -361,824 +374,849 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 59, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "INFO:tensorflow:Using config: {'_master': '', '_num_ps_replicas': 0, '_task_type': None, '_save_checkpoints_steps': None, '_tf_config': gpu_options {\n", - " per_process_gpu_memory_fraction: 1.0\n", - "}\n", - ", '_evaluation_master': '', '_save_summary_steps': 100, '_save_checkpoints_secs': 600, '_num_worker_replicas': 0, '_model_dir': None, '_environment': 'local', '_cluster_spec': , '_is_chief': True, '_keep_checkpoint_max': 5, '_keep_checkpoint_every_n_hours': 10000, '_tf_random_seed': 42, '_task_id': 0}\n", - "WARNING:tensorflow:Using temporary folder as model directory: /tmp/tmpecewk7kb\n", - "WARNING:tensorflow:From /home/ageron/dev/py/envs/ml/lib/python3.5/site-packages/tensorflow/contrib/learn/python/learn/estimators/head.py:615: scalar_summary (from tensorflow.python.ops.logging_ops) is deprecated and will be removed after 2016-11-30.\n", - "Instructions for updating:\n", - "Please switch to tf.summary.scalar. Note that tf.summary.scalar uses the node name instead of the tag. This means that TensorFlow will automatically de-duplicate summary names based on the scope they are created in. Also, passing a tensor or list of tags to a scalar summary op is no longer supported.\n", "INFO:tensorflow:Create CheckpointSaverHook.\n", - "INFO:tensorflow:Saving checkpoints for 1 into /tmp/tmpecewk7kb/model.ckpt.\n", - "INFO:tensorflow:loss = 2.36404, step = 1\n", - "INFO:tensorflow:global_step/sec: 260.817\n", - "INFO:tensorflow:loss = 0.311432, step = 101 (0.383 sec)\n", - "INFO:tensorflow:global_step/sec: 260.391\n", - "INFO:tensorflow:loss = 0.265409, step = 201 (0.384 sec)\n", - "INFO:tensorflow:global_step/sec: 262.476\n", - "INFO:tensorflow:loss = 0.408733, step = 301 (0.381 sec)\n", - "INFO:tensorflow:global_step/sec: 261.314\n", - "INFO:tensorflow:loss = 0.244357, step = 401 (0.383 sec)\n", - "INFO:tensorflow:global_step/sec: 258.889\n", - "INFO:tensorflow:loss = 0.238858, step = 501 (0.386 sec)\n", - "INFO:tensorflow:global_step/sec: 258.784\n", - "INFO:tensorflow:loss = 0.0918271, step = 601 (0.386 sec)\n", - "INFO:tensorflow:global_step/sec: 164.338\n", - "INFO:tensorflow:loss = 0.123374, step = 701 (0.609 sec)\n", - "INFO:tensorflow:global_step/sec: 184.472\n", - "INFO:tensorflow:loss = 0.196473, step = 801 (0.542 sec)\n", - "INFO:tensorflow:global_step/sec: 187.728\n", - "INFO:tensorflow:loss = 0.0932024, step = 901 (0.533 sec)\n", - "INFO:tensorflow:global_step/sec: 189.618\n", - "INFO:tensorflow:loss = 0.196834, step = 1001 (0.527 sec)\n", - "INFO:tensorflow:global_step/sec: 264.9\n", - "INFO:tensorflow:loss = 0.194408, step = 1101 (0.377 sec)\n", - "INFO:tensorflow:global_step/sec: 262.289\n", - "INFO:tensorflow:loss = 0.152817, step = 1201 (0.381 sec)\n", - "INFO:tensorflow:global_step/sec: 262.723\n", - "INFO:tensorflow:loss = 0.149922, step = 1301 (0.381 sec)\n", - "INFO:tensorflow:global_step/sec: 164.842\n", - "INFO:tensorflow:loss = 0.0659786, step = 1401 (0.607 sec)\n", - "INFO:tensorflow:global_step/sec: 228.232\n", - "INFO:tensorflow:loss = 0.0721019, step = 1501 (0.438 sec)\n", - "INFO:tensorflow:global_step/sec: 202.979\n", - "INFO:tensorflow:loss = 0.120699, step = 1601 (0.493 sec)\n", - "INFO:tensorflow:global_step/sec: 228.385\n", - "INFO:tensorflow:loss = 0.0398833, step = 1701 (0.437 sec)\n", - "INFO:tensorflow:global_step/sec: 262.751\n", - "INFO:tensorflow:loss = 0.151045, step = 1801 (0.381 sec)\n", - "INFO:tensorflow:global_step/sec: 259.859\n", - "INFO:tensorflow:loss = 0.0709309, step = 1901 (0.385 sec)\n", - "INFO:tensorflow:global_step/sec: 260.716\n", - "INFO:tensorflow:loss = 0.0622928, step = 2001 (0.384 sec)\n", - "INFO:tensorflow:global_step/sec: 259.09\n", - "INFO:tensorflow:loss = 0.0229075, step = 2101 (0.386 sec)\n", - "INFO:tensorflow:global_step/sec: 259.745\n", - "INFO:tensorflow:loss = 0.0303926, step = 2201 (0.385 sec)\n", - "INFO:tensorflow:global_step/sec: 260.825\n", - "INFO:tensorflow:loss = 0.0458983, step = 2301 (0.383 sec)\n", - "INFO:tensorflow:global_step/sec: 259.816\n", - "INFO:tensorflow:loss = 0.0586828, step = 2401 (0.385 sec)\n", - "INFO:tensorflow:global_step/sec: 243.524\n", - "INFO:tensorflow:loss = 0.0920379, step = 2501 (0.411 sec)\n", - "INFO:tensorflow:global_step/sec: 224.828\n", - "INFO:tensorflow:loss = 0.0315541, step = 2601 (0.445 sec)\n", - "INFO:tensorflow:global_step/sec: 233.629\n", - "INFO:tensorflow:loss = 0.0114352, step = 2701 (0.428 sec)\n", - "INFO:tensorflow:global_step/sec: 185.194\n", - "INFO:tensorflow:loss = 0.0563824, step = 2801 (0.540 sec)\n", - "INFO:tensorflow:global_step/sec: 178.371\n", - "INFO:tensorflow:loss = 0.0811181, step = 2901 (0.561 sec)\n", - "INFO:tensorflow:global_step/sec: 173.41\n", - "INFO:tensorflow:loss = 0.0140813, step = 3001 (0.576 sec)\n", - "INFO:tensorflow:global_step/sec: 194.442\n", - "INFO:tensorflow:loss = 0.0322334, step = 3101 (0.514 sec)\n", - "INFO:tensorflow:global_step/sec: 186.116\n", - "INFO:tensorflow:loss = 0.0132405, step = 3201 (0.538 sec)\n", - "INFO:tensorflow:global_step/sec: 192.012\n", - "INFO:tensorflow:loss = 0.0339669, step = 3301 (0.520 sec)\n", - "INFO:tensorflow:global_step/sec: 182.959\n", - "INFO:tensorflow:loss = 0.157921, step = 3401 (0.547 sec)\n", - "INFO:tensorflow:global_step/sec: 182.487\n", - "INFO:tensorflow:loss = 0.0848148, step = 3501 (0.548 sec)\n", - "INFO:tensorflow:global_step/sec: 142.784\n", - "INFO:tensorflow:loss = 0.155988, step = 3601 (0.700 sec)\n", - "INFO:tensorflow:global_step/sec: 184.358\n", - "INFO:tensorflow:loss = 0.0346186, step = 3701 (0.542 sec)\n", - "INFO:tensorflow:global_step/sec: 169.954\n", - "INFO:tensorflow:loss = 0.00991458, step = 3801 (0.589 sec)\n", - "INFO:tensorflow:global_step/sec: 160.217\n", - "INFO:tensorflow:loss = 0.150861, step = 3901 (0.623 sec)\n", - "INFO:tensorflow:global_step/sec: 211.631\n", - "INFO:tensorflow:loss = 0.110915, step = 4001 (0.473 sec)\n", - "INFO:tensorflow:global_step/sec: 177.961\n", - "INFO:tensorflow:loss = 0.0493516, step = 4101 (0.562 sec)\n", - "INFO:tensorflow:global_step/sec: 178.605\n", - "INFO:tensorflow:loss = 0.0546287, step = 4201 (0.560 sec)\n", - "INFO:tensorflow:global_step/sec: 183.171\n", - "INFO:tensorflow:loss = 0.159896, step = 4301 (0.546 sec)\n", - "INFO:tensorflow:global_step/sec: 207.876\n", - "INFO:tensorflow:loss = 0.112916, step = 4401 (0.481 sec)\n", - "INFO:tensorflow:global_step/sec: 158.187\n", - "INFO:tensorflow:loss = 0.0161132, step = 4501 (0.632 sec)\n", - "INFO:tensorflow:global_step/sec: 241.573\n", - "INFO:tensorflow:loss = 0.0171333, step = 4601 (0.414 sec)\n", - "INFO:tensorflow:global_step/sec: 233.973\n", - "INFO:tensorflow:loss = 0.00835642, step = 4701 (0.427 sec)\n", - "INFO:tensorflow:global_step/sec: 236.421\n", - "INFO:tensorflow:loss = 0.01708, step = 4801 (0.423 sec)\n", - "INFO:tensorflow:global_step/sec: 237.868\n", - "INFO:tensorflow:loss = 0.0864718, step = 4901 (0.420 sec)\n", - "INFO:tensorflow:global_step/sec: 239.187\n", - "INFO:tensorflow:loss = 0.0439425, step = 5001 (0.418 sec)\n", - "INFO:tensorflow:global_step/sec: 238.716\n", - "INFO:tensorflow:loss = 0.00764107, step = 5101 (0.419 sec)\n", - "INFO:tensorflow:global_step/sec: 237.782\n", - "INFO:tensorflow:loss = 0.0232963, step = 5201 (0.421 sec)\n", - "INFO:tensorflow:global_step/sec: 238.707\n", - "INFO:tensorflow:loss = 0.046633, step = 5301 (0.419 sec)\n", - "INFO:tensorflow:global_step/sec: 239.04\n", - "INFO:tensorflow:loss = 0.066787, step = 5401 (0.418 sec)\n", - "INFO:tensorflow:global_step/sec: 252.49\n", - "INFO:tensorflow:loss = 0.0494149, step = 5501 (0.396 sec)\n", - "INFO:tensorflow:global_step/sec: 258.641\n", - "INFO:tensorflow:loss = 0.0707151, step = 5601 (0.387 sec)\n", - "INFO:tensorflow:global_step/sec: 241.49\n", - "INFO:tensorflow:loss = 0.0192079, step = 5701 (0.414 sec)\n", - "INFO:tensorflow:global_step/sec: 229.017\n", - "INFO:tensorflow:loss = 0.00933775, step = 5801 (0.437 sec)\n", - "INFO:tensorflow:global_step/sec: 235.112\n", - "INFO:tensorflow:loss = 0.106693, step = 5901 (0.425 sec)\n", - "INFO:tensorflow:global_step/sec: 233.677\n", - "INFO:tensorflow:loss = 0.0908673, step = 6001 (0.428 sec)\n", - "INFO:tensorflow:global_step/sec: 236.366\n", - "INFO:tensorflow:loss = 0.01711, step = 6101 (0.423 sec)\n", - "INFO:tensorflow:global_step/sec: 186.503\n", - "INFO:tensorflow:loss = 0.0224653, step = 6201 (0.536 sec)\n", - "INFO:tensorflow:global_step/sec: 167.806\n", - "INFO:tensorflow:loss = 0.0684235, step = 6301 (0.596 sec)\n", - "INFO:tensorflow:global_step/sec: 210.251\n", - "INFO:tensorflow:loss = 0.0247326, step = 6401 (0.476 sec)\n", - "INFO:tensorflow:global_step/sec: 197.723\n", - "INFO:tensorflow:loss = 0.00986267, step = 6501 (0.506 sec)\n", - "INFO:tensorflow:global_step/sec: 266.76\n", - "INFO:tensorflow:loss = 0.0220192, step = 6601 (0.375 sec)\n", - "INFO:tensorflow:global_step/sec: 264.179\n", - "INFO:tensorflow:loss = 0.0224969, step = 6701 (0.379 sec)\n", - "INFO:tensorflow:global_step/sec: 257.535\n", - "INFO:tensorflow:loss = 0.0130047, step = 6801 (0.388 sec)\n", - 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(0.415 sec)\n", - "INFO:tensorflow:global_step/sec: 229.499\n", - "INFO:tensorflow:loss = 0.00547122, step = 7801 (0.436 sec)\n", - "INFO:tensorflow:global_step/sec: 222.969\n", - "INFO:tensorflow:loss = 0.012388, step = 7901 (0.448 sec)\n", - "INFO:tensorflow:global_step/sec: 234.443\n", - "INFO:tensorflow:loss = 0.0043136, step = 8001 (0.427 sec)\n", - "INFO:tensorflow:global_step/sec: 181.467\n", - "INFO:tensorflow:loss = 0.0393611, step = 8101 (0.552 sec)\n", - "INFO:tensorflow:global_step/sec: 178.792\n", - "INFO:tensorflow:loss = 0.0268929, step = 8201 (0.559 sec)\n", - "INFO:tensorflow:global_step/sec: 185.438\n", - "INFO:tensorflow:loss = 0.0534401, step = 8301 (0.539 sec)\n", - "INFO:tensorflow:global_step/sec: 151.617\n", - "INFO:tensorflow:loss = 0.017818, step = 8401 (0.660 sec)\n", - "INFO:tensorflow:global_step/sec: 155.867\n", - "INFO:tensorflow:loss = 0.0116427, step = 8501 (0.642 sec)\n", - "INFO:tensorflow:global_step/sec: 181.382\n", - "INFO:tensorflow:loss = 0.00533878, step = 8601 (0.551 sec)\n", - "INFO:tensorflow:global_step/sec: 187.288\n", - "INFO:tensorflow:loss = 0.00569211, step = 8701 (0.534 sec)\n", - "INFO:tensorflow:global_step/sec: 205.664\n", - "INFO:tensorflow:loss = 0.00665956, step = 8801 (0.486 sec)\n", - "INFO:tensorflow:global_step/sec: 204.953\n", - "INFO:tensorflow:loss = 0.00315022, step = 8901 (0.488 sec)\n", - "INFO:tensorflow:global_step/sec: 194.252\n", - "INFO:tensorflow:loss = 0.0133965, step = 9001 (0.515 sec)\n", - "INFO:tensorflow:global_step/sec: 188.951\n", - "INFO:tensorflow:loss = 0.00906787, step = 9101 (0.529 sec)\n", - "INFO:tensorflow:global_step/sec: 173.39\n", - "INFO:tensorflow:loss = 0.00360787, step = 9201 (0.577 sec)\n", - "INFO:tensorflow:global_step/sec: 198.042\n", - "INFO:tensorflow:loss = 0.01566, step = 9301 (0.505 sec)\n", - "INFO:tensorflow:global_step/sec: 220.507\n", - "INFO:tensorflow:loss = 0.0403218, step = 9401 (0.454 sec)\n", - "INFO:tensorflow:global_step/sec: 220.935\n", - "INFO:tensorflow:loss = 0.00779154, step = 9501 (0.452 sec)\n", - "INFO:tensorflow:global_step/sec: 239.463\n", - "INFO:tensorflow:loss = 0.017734, step = 9601 (0.418 sec)\n", - "INFO:tensorflow:global_step/sec: 238.71\n", - "INFO:tensorflow:loss = 0.00776719, step = 9701 (0.419 sec)\n", - "INFO:tensorflow:global_step/sec: 236.017\n", - "INFO:tensorflow:loss = 0.00336712, step = 9801 (0.424 sec)\n", - "INFO:tensorflow:global_step/sec: 230.414\n", - "INFO:tensorflow:loss = 0.0176963, step = 9901 (0.434 sec)\n", - "INFO:tensorflow:global_step/sec: 229.642\n", - "INFO:tensorflow:loss = 0.0169004, step = 10001 (0.435 sec)\n", - "INFO:tensorflow:global_step/sec: 218.107\n", - "INFO:tensorflow:loss = 0.00473883, step = 10101 (0.458 sec)\n", - "INFO:tensorflow:global_step/sec: 202.282\n", - "INFO:tensorflow:loss = 0.00767333, step = 10201 (0.494 sec)\n", - "INFO:tensorflow:global_step/sec: 221.607\n", - "INFO:tensorflow:loss = 0.00469218, step = 10301 (0.451 sec)\n", - 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step = 11201 (0.468 sec)\n", - "INFO:tensorflow:global_step/sec: 215.302\n", - "INFO:tensorflow:loss = 0.0130732, step = 11301 (0.464 sec)\n", - "INFO:tensorflow:global_step/sec: 234.367\n", - "INFO:tensorflow:loss = 0.0126658, step = 11401 (0.427 sec)\n", - "INFO:tensorflow:global_step/sec: 201.051\n", - "INFO:tensorflow:loss = 0.0185032, step = 11501 (0.497 sec)\n", - "INFO:tensorflow:global_step/sec: 211.212\n", - "INFO:tensorflow:loss = 0.000567014, step = 11601 (0.473 sec)\n", - "INFO:tensorflow:global_step/sec: 170.486\n", - "INFO:tensorflow:loss = 0.00216249, step = 11701 (0.587 sec)\n", - "INFO:tensorflow:global_step/sec: 194.085\n", - "INFO:tensorflow:loss = 0.000577293, step = 11801 (0.515 sec)\n", - "INFO:tensorflow:global_step/sec: 215.625\n", - "INFO:tensorflow:loss = 0.00549651, step = 11901 (0.464 sec)\n", - "INFO:tensorflow:global_step/sec: 191.043\n", - "INFO:tensorflow:loss = 0.000167048, step = 12001 (0.524 sec)\n", - "INFO:tensorflow:global_step/sec: 154.278\n", - 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0.000979147, step = 31001 (0.373 sec)\n", - "INFO:tensorflow:global_step/sec: 264.721\n", - "INFO:tensorflow:loss = 0.00142901, step = 31101 (0.378 sec)\n", - "INFO:tensorflow:global_step/sec: 266.221\n", - "INFO:tensorflow:loss = 0.000594258, step = 31201 (0.376 sec)\n", - "INFO:tensorflow:global_step/sec: 265.146\n", - "INFO:tensorflow:loss = 0.000413715, step = 31301 (0.377 sec)\n", - "INFO:tensorflow:global_step/sec: 267.588\n", - "INFO:tensorflow:loss = 0.000751181, step = 31401 (0.374 sec)\n", - "INFO:tensorflow:global_step/sec: 265.785\n", - "INFO:tensorflow:loss = 0.000252517, step = 31501 (0.376 sec)\n", - "INFO:tensorflow:global_step/sec: 260.745\n", - "INFO:tensorflow:loss = 8.00835e-05, step = 31601 (0.384 sec)\n", - "INFO:tensorflow:global_step/sec: 271.475\n", - "INFO:tensorflow:loss = 0.000599239, step = 31701 (0.368 sec)\n", - "INFO:tensorflow:global_step/sec: 257.765\n", - "INFO:tensorflow:loss = 0.00010314, step = 31801 (0.388 sec)\n", - 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0.00137255, step = 32701 (0.410 sec)\n", - "INFO:tensorflow:global_step/sec: 241.211\n", - "INFO:tensorflow:loss = 0.000887949, step = 32801 (0.415 sec)\n", - "INFO:tensorflow:global_step/sec: 246.688\n", - "INFO:tensorflow:loss = 0.000337199, step = 32901 (0.405 sec)\n", - "INFO:tensorflow:global_step/sec: 237.944\n", - "INFO:tensorflow:loss = 0.000573135, step = 33001 (0.420 sec)\n", - "INFO:tensorflow:global_step/sec: 247.035\n", - "INFO:tensorflow:loss = 0.00195268, step = 33101 (0.405 sec)\n", - "INFO:tensorflow:global_step/sec: 241.959\n", - "INFO:tensorflow:loss = 0.000947158, step = 33201 (0.413 sec)\n", - "INFO:tensorflow:global_step/sec: 239.726\n", - "INFO:tensorflow:loss = 0.00028633, step = 33301 (0.417 sec)\n", - "INFO:tensorflow:global_step/sec: 214.33\n", - "INFO:tensorflow:loss = 0.00124017, step = 33401 (0.467 sec)\n", - "INFO:tensorflow:global_step/sec: 193.288\n", - "INFO:tensorflow:loss = 0.000209001, step = 33501 (0.517 sec)\n", - "INFO:tensorflow:global_step/sec: 191.715\n", - "INFO:tensorflow:loss = 0.0010908, step = 33601 (0.522 sec)\n", - "INFO:tensorflow:global_step/sec: 193.058\n", - "INFO:tensorflow:loss = 0.000751158, step = 33701 (0.518 sec)\n", - "INFO:tensorflow:global_step/sec: 193.318\n", - "INFO:tensorflow:loss = 0.00117691, step = 33801 (0.517 sec)\n", - "INFO:tensorflow:global_step/sec: 192.555\n", - "INFO:tensorflow:loss = 0.00109041, step = 33901 (0.519 sec)\n", - "INFO:tensorflow:global_step/sec: 194.089\n", - "INFO:tensorflow:loss = 0.000558409, step = 34001 (0.515 sec)\n", - "INFO:tensorflow:global_step/sec: 190.68\n", - "INFO:tensorflow:loss = 0.000955204, step = 34101 (0.524 sec)\n", - "INFO:tensorflow:global_step/sec: 212.115\n", - "INFO:tensorflow:loss = 0.00114629, step = 34201 (0.471 sec)\n", - "INFO:tensorflow:global_step/sec: 220.762\n", - "INFO:tensorflow:loss = 0.000116086, step = 34301 (0.453 sec)\n", - "INFO:tensorflow:global_step/sec: 220.499\n", - "INFO:tensorflow:loss = 0.000376291, step = 34401 (0.454 sec)\n", - "INFO:tensorflow:global_step/sec: 220.746\n", - "INFO:tensorflow:loss = 0.000596384, step = 34501 (0.453 sec)\n", - "INFO:tensorflow:global_step/sec: 219.763\n", - "INFO:tensorflow:loss = 0.000287235, step = 34601 (0.455 sec)\n", - "INFO:tensorflow:global_step/sec: 166.882\n", - "INFO:tensorflow:loss = 0.0017038, step = 34701 (0.599 sec)\n", - "INFO:tensorflow:global_step/sec: 193.247\n", - "INFO:tensorflow:loss = 0.000414888, step = 34801 (0.518 sec)\n", - "INFO:tensorflow:global_step/sec: 168.67\n", - "INFO:tensorflow:loss = 0.000323204, step = 34901 (0.593 sec)\n", - "INFO:tensorflow:global_step/sec: 215.312\n", - "INFO:tensorflow:loss = 0.000240949, step = 35001 (0.464 sec)\n", - "INFO:tensorflow:global_step/sec: 209.156\n", - "INFO:tensorflow:loss = 0.00105711, step = 35101 (0.478 sec)\n", - "INFO:tensorflow:global_step/sec: 214.048\n", - "INFO:tensorflow:loss = 0.000331689, step = 35201 (0.467 sec)\n", - "INFO:tensorflow:global_step/sec: 172.105\n", - 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0.000382187, step = 37001 (0.525 sec)\n", - "INFO:tensorflow:global_step/sec: 200.515\n", - "INFO:tensorflow:loss = 0.000168937, step = 37101 (0.499 sec)\n", - "INFO:tensorflow:global_step/sec: 205.368\n", - "INFO:tensorflow:loss = 0.000345245, step = 37201 (0.487 sec)\n", - "INFO:tensorflow:global_step/sec: 195.98\n", - "INFO:tensorflow:loss = 0.000750656, step = 37301 (0.510 sec)\n", - "INFO:tensorflow:global_step/sec: 212.979\n", - "INFO:tensorflow:loss = 0.000352557, step = 37401 (0.470 sec)\n", - "INFO:tensorflow:global_step/sec: 210.174\n", - "INFO:tensorflow:loss = 0.000393667, step = 37501 (0.476 sec)\n", - "INFO:tensorflow:global_step/sec: 156.95\n", - "INFO:tensorflow:loss = 0.000323634, step = 37601 (0.637 sec)\n", - "INFO:tensorflow:global_step/sec: 172.972\n", - "INFO:tensorflow:loss = 0.00011668, step = 37701 (0.578 sec)\n", - "INFO:tensorflow:global_step/sec: 209.986\n", - "INFO:tensorflow:loss = 0.000419136, step = 37801 (0.476 sec)\n", - 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0.000432415, step = 38701 (0.415 sec)\n", - "INFO:tensorflow:global_step/sec: 239.449\n", - "INFO:tensorflow:loss = 0.000230968, step = 38801 (0.418 sec)\n", - "INFO:tensorflow:global_step/sec: 224.749\n", - "INFO:tensorflow:loss = 0.00131089, step = 38901 (0.445 sec)\n", - "INFO:tensorflow:global_step/sec: 185.815\n", - "INFO:tensorflow:loss = 0.000195044, step = 39001 (0.538 sec)\n", - "INFO:tensorflow:global_step/sec: 186.065\n", - "INFO:tensorflow:loss = 0.000720432, step = 39101 (0.537 sec)\n", - "INFO:tensorflow:global_step/sec: 189.428\n", - "INFO:tensorflow:loss = 0.000604851, step = 39201 (0.528 sec)\n", - "INFO:tensorflow:global_step/sec: 190.859\n", - "INFO:tensorflow:loss = 0.000287668, step = 39301 (0.524 sec)\n", - "INFO:tensorflow:global_step/sec: 188.808\n", - "INFO:tensorflow:loss = 0.000637942, step = 39401 (0.530 sec)\n", - "INFO:tensorflow:global_step/sec: 180.328\n", - "INFO:tensorflow:loss = 0.000344775, step = 39501 (0.554 sec)\n", - "INFO:tensorflow:global_step/sec: 191.627\n", - "INFO:tensorflow:loss = 0.000685066, step = 39601 (0.522 sec)\n", - "INFO:tensorflow:global_step/sec: 212.621\n", - "INFO:tensorflow:loss = 0.000204588, step = 39701 (0.470 sec)\n", - "INFO:tensorflow:global_step/sec: 216.98\n", - "INFO:tensorflow:loss = 0.00110618, step = 39801 (0.461 sec)\n", - "INFO:tensorflow:global_step/sec: 215.497\n", - "INFO:tensorflow:loss = 0.0012423, step = 39901 (0.464 sec)\n", - "INFO:tensorflow:Saving checkpoints for 40000 into /tmp/tmpecewk7kb/model.ckpt.\n", - "INFO:tensorflow:Loss for final step: 0.000481317.\n" + "INFO:tensorflow:Graph was finalized.\n", + "INFO:tensorflow:Running local_init_op.\n", + "INFO:tensorflow:Done running local_init_op.\n", + "INFO:tensorflow:Saving checkpoints for 1 into /tmp/tmp8zcl_r8x/model.ckpt.\n", + "INFO:tensorflow:step = 0, loss = 2.2865593\n", + "INFO:tensorflow:global_step/sec: 863.886\n", + "INFO:tensorflow:step = 100, loss = 0.3356912 (0.116 sec)\n", + 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(0.096 sec)\n", + "INFO:tensorflow:global_step/sec: 1038.51\n", + "INFO:tensorflow:step = 1100, loss = 0.19595338 (0.097 sec)\n", + "INFO:tensorflow:global_step/sec: 1075.8\n", + "INFO:tensorflow:step = 1200, loss = 0.18142521 (0.093 sec)\n", + "INFO:tensorflow:global_step/sec: 1064.11\n", + "INFO:tensorflow:step = 1300, loss = 0.17464441 (0.094 sec)\n", + "INFO:tensorflow:global_step/sec: 1057.53\n", + "INFO:tensorflow:step = 1400, loss = 0.09934827 (0.095 sec)\n", + "INFO:tensorflow:global_step/sec: 1057.76\n", + "INFO:tensorflow:step = 1500, loss = 0.09750447 (0.094 sec)\n", + "INFO:tensorflow:global_step/sec: 1061.14\n", + "INFO:tensorflow:step = 1600, loss = 0.17522484 (0.094 sec)\n", + "INFO:tensorflow:global_step/sec: 1063.7\n", + "INFO:tensorflow:step = 1700, loss = 0.02980093 (0.094 sec)\n", + "INFO:tensorflow:global_step/sec: 1086.39\n", + "INFO:tensorflow:step = 1800, loss = 0.15622471 (0.092 sec)\n", + "INFO:tensorflow:global_step/sec: 1064.44\n", + "INFO:tensorflow:step = 1900, loss = 0.08783154 (0.094 sec)\n", + "INFO:tensorflow:global_step/sec: 1061.32\n", + "INFO:tensorflow:step = 2000, loss = 0.085202 (0.094 sec)\n", + "INFO:tensorflow:global_step/sec: 1028.3\n", + "INFO:tensorflow:step = 2100, loss = 0.027735986 (0.097 sec)\n", + "INFO:tensorflow:global_step/sec: 1036.98\n", + "INFO:tensorflow:step = 2200, loss = 0.038919766 (0.096 sec)\n", + "INFO:tensorflow:global_step/sec: 1077.8\n", + "INFO:tensorflow:step = 2300, loss = 0.05979547 (0.093 sec)\n", + "INFO:tensorflow:global_step/sec: 1088.85\n", + "INFO:tensorflow:step = 2400, loss = 0.040494952 (0.092 sec)\n", + "INFO:tensorflow:global_step/sec: 1074.89\n", + "INFO:tensorflow:step = 2500, loss = 0.060064077 (0.093 sec)\n", + "INFO:tensorflow:global_step/sec: 1094.41\n", + "INFO:tensorflow:step = 2600, loss = 0.040182363 (0.092 sec)\n", + "INFO:tensorflow:global_step/sec: 1062.75\n", + "INFO:tensorflow:step = 2700, loss = 0.020589514 (0.094 sec)\n", + "INFO:tensorflow:global_step/sec: 1068.68\n", + "INFO:tensorflow:step = 2800, loss = 0.07433957 (0.094 sec)\n", + "INFO:tensorflow:global_step/sec: 945.869\n", + "INFO:tensorflow:step = 2900, loss = 0.13938075 (0.109 sec)\n", + "INFO:tensorflow:global_step/sec: 259.638\n", + "INFO:tensorflow:step = 3000, loss = 0.017360074 (0.385 sec)\n", + "INFO:tensorflow:global_step/sec: 437.029\n", + "INFO:tensorflow:step = 3100, loss = 0.044024955 (0.227 sec)\n", + "INFO:tensorflow:global_step/sec: 502.521\n", + "INFO:tensorflow:step = 3200, loss = 0.012790296 (0.199 sec)\n", + "INFO:tensorflow:global_step/sec: 492.121\n", + "INFO:tensorflow:step = 3300, loss = 0.025715819 (0.203 sec)\n", + "INFO:tensorflow:global_step/sec: 495.325\n", + "INFO:tensorflow:step = 3400, loss = 0.20885624 (0.203 sec)\n", + "INFO:tensorflow:global_step/sec: 494.79\n", + "INFO:tensorflow:step = 3500, loss = 0.1235593 (0.201 sec)\n", + "INFO:tensorflow:global_step/sec: 493.162\n", + "INFO:tensorflow:step = 3600, loss = 0.14184065 (0.203 sec)\n", + 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"INFO:tensorflow:step = 35800, loss = 0.00059113634 (0.162 sec)\n", + "INFO:tensorflow:global_step/sec: 615.056\n", + "INFO:tensorflow:step = 35900, loss = 0.00071175856 (0.163 sec)\n", + "INFO:tensorflow:global_step/sec: 620.295\n", + "INFO:tensorflow:step = 36001, loss = 0.00031694007 (0.161 sec)\n", + "INFO:tensorflow:global_step/sec: 621.713\n", + "INFO:tensorflow:step = 36100, loss = 0.0013310048 (0.161 sec)\n", + "INFO:tensorflow:global_step/sec: 618.303\n", + "INFO:tensorflow:step = 36200, loss = 0.00068119355 (0.162 sec)\n", + "INFO:tensorflow:global_step/sec: 605.853\n", + "INFO:tensorflow:step = 36300, loss = 0.00030532392 (0.165 sec)\n", + "INFO:tensorflow:global_step/sec: 616.624\n", + "INFO:tensorflow:step = 36400, loss = 0.00065413833 (0.162 sec)\n", + "INFO:tensorflow:global_step/sec: 618.937\n", + "INFO:tensorflow:step = 36500, loss = 0.00033657916 (0.161 sec)\n", + "INFO:tensorflow:global_step/sec: 619.432\n", + "INFO:tensorflow:step = 36600, loss = 0.00039314985 (0.161 sec)\n", + "INFO:tensorflow:global_step/sec: 620.87\n", + "INFO:tensorflow:step = 36701, loss = 0.00032779446 (0.161 sec)\n", + "INFO:tensorflow:global_step/sec: 622.821\n", + "INFO:tensorflow:step = 36800, loss = 0.0007773846 (0.161 sec)\n", + "INFO:tensorflow:global_step/sec: 615.385\n", + "INFO:tensorflow:step = 36900, loss = 0.00069945364 (0.163 sec)\n", + "INFO:tensorflow:global_step/sec: 621.472\n", + "INFO:tensorflow:step = 37000, loss = 0.0012571606 (0.161 sec)\n", + "INFO:tensorflow:global_step/sec: 625.694\n", + "INFO:tensorflow:step = 37100, loss = 0.00027467374 (0.160 sec)\n", + "INFO:tensorflow:global_step/sec: 623.396\n", + "INFO:tensorflow:step = 37200, loss = 0.0003416043 (0.160 sec)\n", + "INFO:tensorflow:global_step/sec: 613.761\n", + "INFO:tensorflow:step = 37300, loss = 0.0007066202 (0.163 sec)\n", + "INFO:tensorflow:global_step/sec: 612.321\n", + "INFO:tensorflow:step = 37400, loss = 0.0003081595 (0.163 sec)\n", + "INFO:tensorflow:global_step/sec: 619.103\n", + "INFO:tensorflow:step = 37500, loss = 0.00021623619 (0.161 sec)\n", + "INFO:tensorflow:global_step/sec: 620.245\n", + "INFO:tensorflow:step = 37600, loss = 0.000343732 (0.161 sec)\n", + "INFO:tensorflow:global_step/sec: 621.691\n", + "INFO:tensorflow:step = 37701, loss = 0.0001752602 (0.161 sec)\n", + "INFO:tensorflow:global_step/sec: 621.966\n", + "INFO:tensorflow:step = 37800, loss = 0.00038288627 (0.161 sec)\n", + "INFO:tensorflow:global_step/sec: 617.984\n", + "INFO:tensorflow:step = 37900, loss = 0.001292691 (0.162 sec)\n", + "INFO:tensorflow:global_step/sec: 622.564\n", + "INFO:tensorflow:step = 38000, loss = 0.00023390524 (0.161 sec)\n", + "INFO:tensorflow:global_step/sec: 612.264\n", + "INFO:tensorflow:step = 38101, loss = 0.0006124892 (0.163 sec)\n", + "INFO:tensorflow:global_step/sec: 612.452\n", + "INFO:tensorflow:step = 38200, loss = 0.0005818791 (0.163 sec)\n", + "INFO:tensorflow:global_step/sec: 616.278\n", + "INFO:tensorflow:step = 38300, loss = 0.00014702232 (0.162 sec)\n", + "INFO:tensorflow:global_step/sec: 617.12\n", + "INFO:tensorflow:step = 38400, loss = 0.00016399998 (0.162 sec)\n", + "INFO:tensorflow:global_step/sec: 606.339\n", + "INFO:tensorflow:step = 38500, loss = 0.00057400187 (0.165 sec)\n", + "INFO:tensorflow:global_step/sec: 627.221\n", + "INFO:tensorflow:step = 38600, loss = 0.0007375686 (0.159 sec)\n", + "INFO:tensorflow:global_step/sec: 615.839\n", + "INFO:tensorflow:step = 38700, loss = 0.00052899786 (0.162 sec)\n", + "INFO:tensorflow:global_step/sec: 623.75\n", + "INFO:tensorflow:step = 38800, loss = 0.000111311994 (0.160 sec)\n", + "INFO:tensorflow:global_step/sec: 616.398\n", + "INFO:tensorflow:step = 38900, loss = 0.0014300102 (0.162 sec)\n", + "INFO:tensorflow:global_step/sec: 617.545\n", + "INFO:tensorflow:step = 39000, loss = 9.1103095e-05 (0.162 sec)\n", + "INFO:tensorflow:global_step/sec: 620.913\n", + "INFO:tensorflow:step = 39100, loss = 0.00094451586 (0.161 sec)\n", + "INFO:tensorflow:global_step/sec: 618.911\n", + "INFO:tensorflow:step = 39200, loss = 0.00041764986 (0.162 sec)\n", + "INFO:tensorflow:global_step/sec: 618.881\n", + "INFO:tensorflow:step = 39300, loss = 0.000314845 (0.161 sec)\n", + "INFO:tensorflow:global_step/sec: 618.187\n", + "INFO:tensorflow:step = 39400, loss = 0.0006256502 (0.162 sec)\n", + "INFO:tensorflow:global_step/sec: 622.969\n", + "INFO:tensorflow:step = 39500, loss = 0.00018341541 (0.161 sec)\n", + "INFO:tensorflow:global_step/sec: 613.211\n", + "INFO:tensorflow:step = 39600, loss = 0.00045592964 (0.163 sec)\n", + "INFO:tensorflow:global_step/sec: 614.614\n", + "INFO:tensorflow:step = 39700, loss = 0.00019703162 (0.163 sec)\n", + "INFO:tensorflow:global_step/sec: 615.455\n", + "INFO:tensorflow:step = 39800, loss = 0.0012410291 (0.163 sec)\n", + "INFO:tensorflow:global_step/sec: 616.796\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "INFO:tensorflow:step = 39900, loss = 0.00077618135 (0.162 sec)\n", + "INFO:tensorflow:Saving checkpoints for 40000 into /tmp/tmp8zcl_r8x/model.ckpt.\n", + "INFO:tensorflow:Loss for final step: 0.0004767616.\n" ] }, { @@ -1187,7 +1225,7 @@ "SKCompat()" ] }, - "execution_count": 11, + "execution_count": 59, "metadata": {}, "output_type": "execute_result" } @@ -1195,34 +1233,38 @@ "source": [ "import tensorflow as tf\n", "\n", - "config = tf.contrib.learn.RunConfig(tf_random_seed=42) # not shown in the config\n", + "config = tf.contrib.learn.RunConfig(tf_random_seed=42) # 책에는 없음\n", "\n", "feature_cols = tf.contrib.learn.infer_real_valued_columns_from_input(X_train)\n", "dnn_clf = tf.contrib.learn.DNNClassifier(hidden_units=[300,100], n_classes=10,\n", " feature_columns=feature_cols, config=config)\n", "dnn_clf = tf.contrib.learn.SKCompat(dnn_clf) # if TensorFlow >= 1.1\n", + "tf.logging.set_verbosity(tf.logging.INFO)\n", "dnn_clf.fit(X_train, y_train, batch_size=50, steps=40000)" ] }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "INFO:tensorflow:Restoring parameters from /tmp/tmpecewk7kb/model.ckpt-40000\n" + "INFO:tensorflow:Graph was finalized.\n", + "INFO:tensorflow:Restoring parameters from /tmp/tmpupshz99y/model.ckpt-40000\n", + "INFO:tensorflow:Running local_init_op.\n", + "INFO:tensorflow:Done running local_init_op.\n" ] }, { "data": { "text/plain": [ - "0.98250000000000004" + "0.9823" ] }, - "execution_count": 12, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" } @@ -1236,16 +1278,16 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "0.071487383848950564" + "0.06889318829651588" ] }, - "execution_count": 13, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" } @@ -1263,12 +1305,12 @@ "collapsed": true }, "source": [ - "## Using plain TensorFlow" + "## 저수준의 TensorFlow API 사용" ] }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 16, "metadata": {}, "outputs": [], "source": [ @@ -1282,7 +1324,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 17, "metadata": {}, "outputs": [], "source": [ @@ -1294,7 +1336,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 18, "metadata": {}, "outputs": [], "source": [ @@ -1314,7 +1356,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 19, "metadata": {}, "outputs": [], "source": [ @@ -1328,7 +1370,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 20, "metadata": {}, "outputs": [], "source": [ @@ -1340,7 +1382,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 21, "metadata": {}, "outputs": [], "source": [ @@ -1353,7 +1395,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 22, "metadata": {}, "outputs": [], "source": [ @@ -1364,7 +1406,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 23, "metadata": {}, "outputs": [], "source": [ @@ -1374,7 +1416,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 24, "metadata": {}, "outputs": [], "source": [ @@ -1384,53 +1426,53 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 25, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "0 Train accuracy: 0.9 Val accuracy: 0.9146\n", - "1 Train accuracy: 0.94 Val accuracy: 0.9348\n", - "2 Train accuracy: 0.92 Val accuracy: 0.9466\n", - "3 Train accuracy: 0.96 Val accuracy: 0.9508\n", - "4 Train accuracy: 0.92 Val accuracy: 0.9586\n", - "5 Train accuracy: 0.94 Val accuracy: 0.9584\n", - "6 Train accuracy: 0.98 Val accuracy: 0.9608\n", - "7 Train accuracy: 0.96 Val accuracy: 0.9636\n", - "8 Train accuracy: 0.92 Val accuracy: 0.9638\n", - "9 Train accuracy: 0.96 Val accuracy: 0.965\n", - "10 Train accuracy: 0.98 Val accuracy: 0.9686\n", - "11 Train accuracy: 0.94 Val accuracy: 0.9686\n", - "12 Train accuracy: 1.0 Val accuracy: 0.9702\n", - "13 Train accuracy: 0.94 Val accuracy: 0.9686\n", - "14 Train accuracy: 1.0 Val accuracy: 0.9716\n", - "15 Train accuracy: 1.0 Val accuracy: 0.973\n", - "16 Train accuracy: 1.0 Val accuracy: 0.9736\n", - "17 Train accuracy: 0.98 Val accuracy: 0.9736\n", - "18 Train accuracy: 1.0 Val accuracy: 0.9752\n", - "19 Train accuracy: 1.0 Val accuracy: 0.975\n", - "20 Train accuracy: 0.98 Val accuracy: 0.9748\n", - "21 Train accuracy: 1.0 Val accuracy: 0.975\n", - "22 Train accuracy: 1.0 Val accuracy: 0.9756\n", - "23 Train accuracy: 1.0 Val accuracy: 0.9772\n", - "24 Train accuracy: 1.0 Val accuracy: 0.978\n", - "25 Train accuracy: 1.0 Val accuracy: 0.9772\n", - "26 Train accuracy: 1.0 Val accuracy: 0.9778\n", - "27 Train accuracy: 1.0 Val accuracy: 0.9762\n", - "28 Train accuracy: 0.98 Val accuracy: 0.9774\n", - "29 Train accuracy: 1.0 Val accuracy: 0.9784\n", - "30 Train accuracy: 1.0 Val accuracy: 0.9776\n", - "31 Train accuracy: 1.0 Val accuracy: 0.9786\n", - "32 Train accuracy: 0.98 Val accuracy: 0.9776\n", - "33 Train accuracy: 0.98 Val accuracy: 0.9794\n", - "34 Train accuracy: 0.98 Val accuracy: 0.978\n", - "35 Train accuracy: 0.98 Val accuracy: 0.9768\n", - "36 Train accuracy: 0.98 Val accuracy: 0.9794\n", - "37 Train accuracy: 1.0 Val accuracy: 0.9784\n", - "38 Train accuracy: 1.0 Val accuracy: 0.979\n", - "39 Train accuracy: 1.0 Val accuracy: 0.9788\n" + "0 훈련 정확도: 0.88 검증 세트 정확도: 0.9182\n", + "1 훈련 정확도: 0.92 검증 세트 정확도: 0.9338\n", + "2 훈련 정확도: 0.92 검증 세트 정확도: 0.9412\n", + "3 훈련 정확도: 0.96 검증 세트 정확도: 0.9484\n", + "4 훈련 정확도: 0.92 검증 세트 정확도: 0.9536\n", + "5 훈련 정확도: 0.94 검증 세트 정확도: 0.9584\n", + "6 훈련 정확도: 0.96 검증 세트 정확도: 0.9596\n", + "7 훈련 정확도: 0.96 검증 세트 정확도: 0.9634\n", + "8 훈련 정확도: 0.94 검증 세트 정확도: 0.9658\n", + "9 훈련 정확도: 0.98 검증 세트 정확도: 0.9676\n", + "10 훈련 정확도: 1.0 검증 세트 정확도: 0.9694\n", + "11 훈련 정확도: 0.9 검증 세트 정확도: 0.9714\n", + "12 훈련 정확도: 0.98 검증 세트 정확도: 0.9718\n", + "13 훈련 정확도: 0.98 검증 세트 정확도: 0.9708\n", + "14 훈련 정확도: 1.0 검증 세트 정확도: 0.9722\n", + "15 훈련 정확도: 1.0 검증 세트 정확도: 0.9724\n", + "16 훈련 정확도: 1.0 검증 세트 정확도: 0.9734\n", + "17 훈련 정확도: 0.96 검증 세트 정확도: 0.9742\n", + "18 훈련 정확도: 1.0 검증 세트 정확도: 0.9732\n", + "19 훈련 정확도: 1.0 검증 세트 정확도: 0.9738\n", + "20 훈련 정확도: 1.0 검증 세트 정확도: 0.976\n", + "21 훈련 정확도: 1.0 검증 세트 정확도: 0.9756\n", + "22 훈련 정확도: 1.0 검증 세트 정확도: 0.9758\n", + "23 훈련 정확도: 1.0 검증 세트 정확도: 0.9744\n", + "24 훈련 정확도: 1.0 검증 세트 정확도: 0.9752\n", + "25 훈련 정확도: 1.0 검증 세트 정확도: 0.976\n", + "26 훈련 정확도: 0.98 검증 세트 정확도: 0.9764\n", + "27 훈련 정확도: 1.0 검증 세트 정확도: 0.9752\n", + "28 훈련 정확도: 0.98 검증 세트 정확도: 0.9764\n", + "29 훈련 정확도: 1.0 검증 세트 정확도: 0.9768\n", + "30 훈련 정확도: 1.0 검증 세트 정확도: 0.9774\n", + "31 훈련 정확도: 1.0 검증 세트 정확도: 0.9764\n", + "32 훈련 정확도: 0.98 검증 세트 정확도: 0.978\n", + "33 훈련 정확도: 0.98 검증 세트 정확도: 0.9782\n", + "34 훈련 정확도: 0.98 검증 세트 정확도: 0.9782\n", + "35 훈련 정확도: 1.0 검증 세트 정확도: 0.9784\n", + "36 훈련 정확도: 0.98 검증 세트 정확도: 0.9788\n", + "37 훈련 정확도: 0.98 검증 세트 정확도: 0.978\n", + "38 훈련 정확도: 1.0 검증 세트 정확도: 0.979\n", + "39 훈련 정확도: 1.0 검증 세트 정확도: 0.9792\n" ] } ], @@ -1444,14 +1486,14 @@ " acc_train = accuracy.eval(feed_dict={X: X_batch, y: y_batch})\n", " acc_val = accuracy.eval(feed_dict={X: mnist.validation.images,\n", " y: mnist.validation.labels})\n", - " print(epoch, \"Train accuracy:\", acc_train, \"Val accuracy:\", acc_val)\n", + " print(epoch, \"훈련 정확도:\", acc_train, \"검증 세트 정확도:\", acc_val)\n", "\n", " save_path = saver.save(sess, \"./my_model_final.ckpt\")" ] }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 26, "metadata": {}, "outputs": [ { @@ -1464,7 +1506,7 @@ ], "source": [ "with tf.Session() as sess:\n", - " saver.restore(sess, \"./my_model_final.ckpt\") # or better, use save_path\n", + " saver.restore(sess, \"./my_model_final.ckpt\") # 또는 save_path를 사용합니다\n", " X_new_scaled = mnist.test.images[:20]\n", " Z = logits.eval(feed_dict={X: X_new_scaled})\n", " y_pred = np.argmax(Z, axis=1)" @@ -1472,29 +1514,27 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 27, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Predicted classes: [7 2 1 0 4 1 4 9 6 9 0 6 9 0 1 5 9 7 3 4]\n", - "Actual classes: [7 2 1 0 4 1 4 9 5 9 0 6 9 0 1 5 9 7 3 4]\n" + "예측 클래스: [7 2 1 0 4 1 4 9 6 9 0 6 9 0 1 5 9 7 3 4]\n", + "진짜 클래스: [7 2 1 0 4 1 4 9 5 9 0 6 9 0 1 5 9 7 3 4]\n" ] } ], "source": [ - "print(\"Predicted classes:\", y_pred)\n", - "print(\"Actual classes: \", mnist.test.labels[:20])" + "print(\"예측 클래스:\", y_pred)\n", + "print(\"진짜 클래스:\", mnist.test.labels[:20])" ] }, { "cell_type": "code", - "execution_count": 26, - "metadata": { - "collapsed": true - }, + "execution_count": 28, + "metadata": {}, "outputs": [], "source": [ "from IPython.display import clear_output, Image, display, HTML\n", @@ -1537,7 +1577,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 29, "metadata": {}, "outputs": [ { @@ -1547,7 +1587,7 @@ "