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Created June 14, 2017 15:26
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Linear regression vs. Logistic regresion
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"Linear regression vs Logstic regression"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"%matplotlib inline\n",
"import tensorflow as tf\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import math"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"x_data = np.arange(10).reshape(-1, 1)\n",
"y_data = (x_data >= 5).astype(np.int).reshape(-1, 1)\n",
"\n",
"# outlier\n",
"x_data[9] = 100"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/plain": [
"array([[ 0],\n",
" [ 1],\n",
" [ 2],\n",
" [ 3],\n",
" [ 4],\n",
" [ 5],\n",
" [ 6],\n",
" [ 7],\n",
" [ 8],\n",
" [100]])"
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"x_data"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/plain": [
"array([[0],\n",
" [0],\n",
" [0],\n",
" [0],\n",
" [0],\n",
" [1],\n",
" [1],\n",
" [1],\n",
" [1],\n",
" [1]])"
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"y_data"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"xlim = (-1, 20)\n",
"ylim = (-0.5, 1.5)\n",
"\n",
"def show_plot():\n",
" plt.plot(x_data, y_data, 'o')\n",
" plt.xlabel('x')\n",
" plt.ylabel('y')\n",
" plt.xlim(xlim[0], xlim[1])\n",
" plt.ylim(ylim[0], ylim[1])\n",
" plt.legend()\n",
" plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
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QJM1GX2c2FwM3VtVy4MZufrKHgTdV1QuAFcAHkxwxsvyiqjqle21qX7Ik6cnqK2xWAld1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\nUnOGjSSpuf8P9Rew+mHxDsIAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fcf2d7f77d0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"show_plot()"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"tf.reset_default_graph()\n",
"\n",
"# placeholders for a tensor that will be always fed.\n",
"X = tf.placeholder(tf.float32, shape=[None, 1])\n",
"Y = tf.placeholder(tf.float32, shape=[None, 1])\n",
"\n",
"# W = tf.random_normal([1, 1])\n",
"# b = tf.random_normal([1])\n",
"W = tf.ones([1,1])\n",
"b = tf.zeros([1])\n",
"\n",
"W_line = tf.Variable(W, name='weight_line')\n",
"b_line = tf.Variable(b, name='bias_line')\n",
"W_sigmoid = tf.Variable(W, name='weight_sigmoid')\n",
"b_sigmoid = tf.Variable(b, name='bias_sigmoid')\n",
"\n",
"# Hypothesis using sigmoid: tf.div(1., 1. + tf.exp(tf.matmul(X, W) + b))\n",
"hypo_line = tf.matmul(X, W_line) + b_line\n",
"hypo_sigmoid = tf.sigmoid(tf.matmul(X, W_sigmoid) + b_sigmoid)\n",
"\n",
"# cost/loss function\n",
"cost_line = tf.reduce_mean(tf.square(hypo_line - Y))\n",
"cost_sigmoid = -tf.reduce_mean(Y * tf.log(hypo_sigmoid + 1e-8) + (1 - Y) * tf.log(1 - hypo_sigmoid + 1e-8))\n",
"optimizer_line = tf.train.GradientDescentOptimizer(learning_rate=0.0001)\n",
"optimizer_sigmoid = tf.train.GradientDescentOptimizer(learning_rate=0.01)\n",
"train_line = optimizer_line.minimize(cost_line)\n",
"train_sigmoid = optimizer_sigmoid.minimize(cost_sigmoid)\n",
"\n",
"# Accuracy computation\n",
"# True if hypothesis > 0.5 else False\n",
"prediction_line = tf.cast(hypo_line > 0.5, dtype=tf.float32)\n",
"prediction_sigmoid = tf.cast(hypo_sigmoid > 0.5, dtype=tf.float32)\n",
"accuracy_line = tf.reduce_mean(tf.cast(tf.equal(prediction_line, Y), dtype=tf.float32))\n",
"accuracy_sigmoid = tf.reduce_mean(tf.cast(tf.equal(prediction_sigmoid, Y), dtype=tf.float32))"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"((10, 1), (10, 1))"
]
},
"execution_count": 43,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"x_data.shape, y_data.shape"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true,
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[0] (line) cost: 630.8366, acc: 60.00% | (sigmoid) cost: 1.1109, acc: 60.00%\n"
]
},
{
"data": {
"image/png": 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Gz38LfLnlAu7+uruviJ6/DawD8jM0LAhgwgQoKcnL5kREil1SYTPc3ddGz98B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\nRK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fcef868c710>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[1000] (line) cost: 0.3132, acc: 60.00% | (sigmoid) cost: 0.3334, acc: 90.00%\n"
]
},
{
"data": {
"image/png": 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"text/plain": [
"<matplotlib.figure.Figure at 0x7fcf2805a810>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[2000] (line) cost: 0.2843, acc: 60.00% | (sigmoid) cost: 0.2575, acc: 90.00%\n"
]
},
{
"data": {
"image/png": 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6RmtrBwDdfX0R6fza45xNnIfRhgDrE8Y3hNOSLuPuNUAFUNzKdQEws6lmVmZm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\nmrsDewAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fcf0079db90>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[3000] (line) cost: 0.2635, acc: 60.00% | (sigmoid) cost: 0.2172, acc: 90.00%\n"
]
},
{
"data": {
"image/png": 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Efh1OTwLmhNMnhtt3A4aF+8k+2HMeeeyR/uN//Njve/0+f2HdC75lzxY/mFmz\n3HNPn+VMK3RuMGdaoeeePstnzTpo0wPt8/Pdg3sYBI/8fO8y7UWk4wPKvY2f+bF1EDCzM4Eb3f2z\n4fx0AHe/JWGbp8NtXjCzHOA9oD9wfeK2ids195yH0kGgrfce6+rtRaTja48OAnGeRjsaSLz6vj5c\nlnQbd68BKoAjWtgWADMrNrNyMyt///33W11kslvRNLdc7UVEmur0X+p091J3L3L3ov79+7e6fUGK\nL/qnWq72IiJNxRk2G4AhCfODw2VJtwlPo/UGtrawbbsoKQluX5MoPz9YrvYiIi3U1os+h/og6Ha9\nmuACf30HgRGNtrmKhh0E5obTI2jYQWA1LeggcPrppx/SxbFZs9wLC93Ngp+tvTje1duLSMdGR+4g\nAGBmY4FfEPRMm+nuJWZ2U/iLzTOz7sCfgFOBbcAkd18dtp0BXAHUANPc/cmDPV8c97MREenoNFxN\nKylsRERar6P3RhMRkS5CYSMiIpFT2IiISOQUNiIiEjmFjYiIRE5hIyIikVPYiIhI5BQ2IiISOYWN\niIhETmEjIiKRU9iIiEjkFDYiIhI5hY2IiEROYSMiIpFT2IiISOQUNiIiEjmFjYiIRE5hIyIikYsl\nbMysn5k9a2Yrw599k2xzipm9YGZLzex1M/tCwrrfm9m/zGxx+Dglvb+BiIi0RlxHNtcD8939OGB+\nON9YJfAldx8BjAF+YWZ9EtZ/191PCR+Loy9ZREQOVVxhMx74Qzj9B2BC4w3cfYW7rwyn3wU2A/3T\nVqGIiLSbuMJmoLtvDKffAwY2t7GZjQLygLcTFpeEp9fuMLNuEdUpIiLtICeqHZvZX4EPJVk1I3HG\n3d3MvJn9DAL+BEx197pw8XSCkMoDSoHvAzelaF8MFAMUFBS08rcQEZH2EFnYuPt5qdaZ2SYzG+Tu\nG8Mw2Zxiu8OBx4EZ7v5iwr7rj4r2mdnvgO80U0cpQSBRVFSUMtRERCQ6cZ1GmwdMDaenAn9pvIGZ\n5QEPA3909wcbrRsU/jSC6z1vRFqtiIi0SVxhcyvwGTNbCZwXzmNmRWZ2T7jNJcAngS8n6eJcZmZL\ngCXAkcCP0lu+iIi0hrl3nTNLRUVFXl5eHncZIiIdipktcveituxDIwiIiEjkFDYiIhI5hY2IiERO\nYSMiIpFT2IiISOQUNiIiEjmFjYiIRE5hIyIikVPYiIhI5BQ2IiISOYWNiIhETmEjIiKRU9iIiEjk\nFDYiIhJzJj2AAAAHK0lEQVQ5hY2IiEROYSMiIpFT2IiISOQUNiIiEjmFjYiIRC6WsDGzfmb2rJmt\nDH/2TbFdrZktDh/zEpYPM7OXzGyVmc0xs7z0VS8iIq0V15HN9cB8dz8OmB/OJ7PX3U8JH+MSlv8E\nuMPdjwW2A1+JtlwREWmLuMJmPPCHcPoPwISWNjQzAz4NPHgo7UVEJP1yYnrege6+MZx+DxiYYrvu\nZlYO1AC3uvsjwBHADnevCbdZDxyd6onMrBgoDmd3m9nyNtR9JLClDe2jlsn1ZXJtoPraSvW1TabX\nd3xbdxBZ2JjZX4EPJVk1I3HG3d3MPMVuCt19g5kNB54zsyVARWvqcPdSoLQ1bVIxs3J3L2qPfUUh\nk+vL5NpA9bWV6mubjlBfW/cRWdi4+3mp1pnZJjMb5O4bzWwQsDnFPjaEP1eb2fPAqcCfgT5mlhMe\n3QwGNrT7LyAiIu0mrms284Cp4fRU4C+NNzCzvmbWLZw+EvgEsMzdHfgbcHFz7UVEJHPEFTa3Ap8x\ns5XAeeE8ZlZkZveE23wYKDez1wjC5VZ3Xxau+z5wnZmtIriGc2+a6m6X03ERyuT6Mrk2UH1tpfra\nptPXZ8GBgoiISHQ0goCIiEROYSMiIpFT2CRhZmPMbHk4HE6T0Q3MrFs4TM6qcNicoWmqa4iZ/c3M\nlpnZUjO7Nsk2Z5tZRcIwPz9MR20Jz/+OmS0Jn7tJd0kL3Bm+dq+b2WlprO34hNdlsZntNLNpjbZJ\n6+tnZjPNbLOZvZGwrKXDOU0Nt1lpZlOTbRNRfbeZ2Vvhv9/DZtYnRdtm3wsR1nejmW1I+Dccm6Jt\ns3/nEdY3J6G2d8xscYq2kb5+qT5PInv/ubseCQ8gG3gbGA7kAa8BJzba5krg1+H0JGBOmmobBJwW\nTh8GrEhS29nAYzG+fu8ARzazfizwJGDAx4CXYvx3fo/gu1yxvX7AJ4HTgDcSlv0UuD6cvh74SZJ2\n/YDV4c++4XTfNNU3GsgJp3+SrL6WvBcirO9G4Dst+Pdv9u88qvoarf8Z8MM4Xr9UnydRvf90ZNPU\nKGCVu6929/3A/QTD6yRKHG7nQeBcM7OoC3P3je7+Sji9C3iTZkZPyFDjgT964EWC70wNiqGOc4G3\n3X1NDM99gLv/A9jWaHFLhnP6LPCsu29z9+3As8CYdNTn7s/4ByN4vEjwXbdYpHj9WqIlf+dt1lx9\n4WfGJcDs9n7elmjm8ySS95/CpqmjgXUJ88mGwzmwTfhHV0HQBTttwlN3pwIvJVl9ppm9ZmZPmtmI\ndNYFOPCMmS2yYKigxlry+qbDJFL/kcf5+kHLhnPKlNfxCoIj1WQO9l6I0tXhab6ZKU4DZcLr9+/A\nJndfmWJ92l6/Rp8nkbz/FDYdkJn1IhhJYZq772y0+hWCU0MnA/8DPJLm8s5y99OA84GrzOyTaX7+\ng7LglhTjgAeSrI779WvAg3MWGfn9BDObQTBuYVmKTeJ6L9wNHAOcAmwkOFWViSbT/FFNWl6/5j5P\n2vP9p7BpagMwJGE+2XA4B7YxsxygN7A1HcWZWS7BG6PM3R9qvN7dd7r77nD6CSDXghEY0sI/GGJo\nM/AwwemKRC15faN2PvCKu29qvCLu1y+0qf7UoqUezinW19HMvgxcAEwJP5CaaMF7IRLuvsnda929\nDvhtiueN+/XLAS4C5qTaJh2vX4rPk0jefwqbpl4GjrPgBm15BKdb5jXaJnG4nYuB51L9wbWn8Bzv\nvcCb7v7zFNt8qP76kZmNIvg3TlcQ9jSzw+qnCS4kv9Fos3nAlyzwMaAi4ZA9XVL+jzLO1y/BQYdz\nAp4GRlswrFNfgtf66XQUZ2ZjgO8B49y9MsU2LXkvRFVf4jXAC1M8b0v+zqN0HvCWu69PtjIdr18z\nnyfRvP+i6unQkR8EPaZWEPRWmREuu4ngjwugO8EpmFXA/wOGp6muswgOaV8HFoePscDXga+H21wN\nLCXoXfMi8PE0vm7Dw+d9Layh/rVLrM+Au8LXdglQlOZ/254E4dE7YVlsrx9B6G0EqgnOe3+F4Prf\nfGAl8FegX7htEXBPQtsrwvfgKuDyNNa3iuB8ff17sL5n5lHAE829F9JU35/C99brBB+cgxrXF843\n+TtPR33h8t/Xv+cStk3r69fM50kk7z8NVyMiIpHTaTQREYmcwkZERCKnsBERkcgpbEREJHIKGxER\niZzCRkREIqewERGRyClsRGJkZh8NB4zsHn5rfKmZjYy7LpH2pi91isTMzH5EMCpFD2C9u98Sc0ki\n7U5hIxKzcGyul4EqguFxamMuSaTd6TSaSPyOAHoR3C2xe8y1iERCRzYiMTOzeQR3ihxGMGjk1TGX\nJNLucuIuQKQrM7MvAdXufp+ZZQMLzezT7v5c3LWJtCcd2YiISOR0zUZERCKnsBERkcgpbEREJHIK\nGxERiZzCRkREIqewERGRyClsREQkcv8fuMtih7PpWUAAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fcf2812f4d0>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[4000] (line) cost: 0.2485, acc: 60.00% | (sigmoid) cost: 0.1918, acc: 100.00%\n"
]
},
{
"data": {
"image/png": 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Gz1rMu09OCRsnr2zzc3b22WndfXkR6f669ZM6zewrwA/c/avh8B0A7v7jhHlm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\nyP1/uEbFjVHixHMAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fcea43faf10>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[5000] (line) cost: 0.2376, acc: 60.00% | (sigmoid) cost: 0.1741, acc: 100.00%\n"
]
},
{
"data": {
"image/png": 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KMAYUDmBwn8EMLhrMkD5DOLTPoRze73CG9hvK9Y9OpbH3xlbbyt1ZRv1PV7X7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\nAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fcef8792590>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[6000] (line) cost: 0.2299, acc: 60.00% | (sigmoid) cost: 0.1608, acc: 100.00%\n"
]
},
{
"data": {
"image/png": 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DquGsKijZBPVlsGQW5btmsHFjtyEE5Ws7by8vp0+UF5G+r68PEDgSSPx6fB3w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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fcec06e9590>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[7000] (line) cost: 0.2242, acc: 60.00% | (sigmoid) cost: 0.1504, acc: 100.00%\n"
]
},
{
"data": {
"image/png": 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psLBtuJSm+d1mpaXJexalaX6mM+7tRUQg3gECdcCYhMejw2VJ24Sn0UoI+gnp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\nERGRyClsREQkcgobERGJ3P8HxDLsQd/Z90YAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fcf00697dd0>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[8000] (line) cost: 0.2202, acc: 60.00% | (sigmoid) cost: 0.1419, acc: 100.00%\n"
]
},
{
"data": {
"image/png": 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VlibvWZSmeOdz3O1FRCDeGwS2AOMS1seGZUnrhMNoJcD2FNumRcWEKmje+9Zj\nmouD8hRUVUHx2r0v8BevnUZVincuV1UF10gSFRfTZ9qLiADBRfA4XgS9qnXAwUAB8BLwkXZ1vgnc\nEy5PBR4Olz8S1i8M268Dcrs75gknnOD74hv/Nctzry1zbjbPvbbMv/Ffs3rUftYs97Iyd7PgfVbP\nmvf59iLStwG13svv/FifszGzc4BfALnAfe5eZWa3hv+wuWZWBMwEjgPeBqa6+7qwbSXwVaAF+I67\nP9Hd8eJ4zkZEpK/r03ejxUHJRkSk5/r8Q50iIjIwKNmIiEjklGxERCRySjYiIhI5JRsREYmcko2I\niEROyUZERCKnZCMiIpFTshERkcgp2YiISOSUbEREJHJKNiIiEjklGxERiZySjYiIRE7JRkREIqdk\nIyIikVOyERGRyCnZiIhI5GJJNma2n5k9ZWarw/cRSeoca2YvmtkKM1tmZhcmbPutmb1mZkvD17GZ\n/ReIiEhPxNWzuR5Y4O6HAgvC9fYagC+7+0eAycAvzGx4wvbvu/ux4Wtp9CGLiMi+iivZTAHuD5fv\nBz7TvoK7r3L31eHyP4BtwP4Zi1BERNImrmQz2t1fD5e3AqO7qmxmJwEFwNqE4qpweO3nZlYYUZwi\nIpIGeVHt2MyeBg5IsqkyccXd3cy8i/2MAWYCl7j77rD4hwRJqgCoBq4Dbu2kfQVQAVBaWtrDf4WI\niKRDZMnG3Sd1ts3M3jCzMe7+ephMtnVSbxjwJ6DS3Rcm7LutV9RoZr8Bru0ijmqChER5eXmnSU1E\nRKIT1zDaXOCScPkS4H/aVzCzAuAR4AF3n9Nu25jw3Qiu97wcabQiItIrcSWbnwJnmdlqYFK4jpmV\nm9mvwjpfAE4HvpLkFucaM1sOLAdGAT/ObPgiItIT5j5wRpbKy8u9trY27jBERPoUM1vs7uW92Ydm\nEBARkcgp2YiISOSUbEREJHJKNiIiEjklGxERiZySjYiIRE7JRkREIqdkIyIikVOyERGRyCnZiIhI\n5JRsREQkcko2IiISOSUbERGJnJKNiIhETslGREQip2QjIiKRU7IREZHIKdmIiEjklGxERCRysSQb\nM9vPzJ4ys9Xh+4hO6rWa2dLwNTeh/GAz+6uZrTGzh8ysIHPRi4hIT8XVs7keWODuhwILwvVkdrr7\nseHrvITy24Gfu/shwDvAZdGGKyIivRFXspkC3B8u3w98JtWGZmbAmcCcfWkvIiKZlxfTcUe7++vh\n8lZgdCcEMgK1AAAGjklEQVT1isysFmgBfurujwIjgXfdvSWssxk4qLMDmVkFUBGu1pvZq72IexTw\nVi/aRy2b48vm2EDx9Zbi651sj+/w3u4gsmRjZk8DByTZVJm44u5uZt7JbsrcfYuZTQCeMbPlQF1P\n4nD3aqC6J206Y2a17l6ejn1FIZvjy+bYQPH1luLrnb4QX2/3EVmycfdJnW0zszfMbIy7v25mY4Bt\nnexjS/i+zsyeA44D/gAMN7O8sHczFtiS9n+AiIikTVzXbOYCl4TLlwD/076CmY0ws8JweRRwKrDS\n3R14Frigq/YiIpI94ko2PwXOMrPVwKRwHTMrN7NfhXWOBGrN7CWC5PJTd18ZbrsOuMbM1hBcw/l1\nhuJOy3BchLI5vmyODRRfbym+3un38VnQURAREYmOZhAQEZHIKdmIiEjklGySMLPJZvZqOB1Oh9kN\nzKwwnCZnTThtzvgMxTXOzJ41s5VmtsLMvp2kzhlmVpcwzc+PMhFbwvHXm9ny8Ngdbpe0wC/Dc7fM\nzI7PYGyHJ5yXpWa2w8y+065ORs+fmd1nZtvM7OWEslSnc7okrLPazC5JViei+GaY2d/D/36PmNnw\nTtp2+VmIML7pZrYl4b/hOZ207fLvPML4HkqIbb2ZLe2kbaTnr7Pvk8g+f+6uV8ILyAXWAhOAAuAl\n4Kh2da4E7gmXpwIPZSi2McDx4fJQYFWS2M4AHo/x/K0HRnWx/RzgCcCAfwb+GuN/560Ez3LFdv6A\n04HjgZcTyv4duD5cvh64PUm7/YB14fuIcHlEhuI7G8gLl29PFl8qn4UI45sOXJvCf/8u/86jiq/d\n9v8D/CiO89fZ90lUnz/1bDo6CVjj7uvcvQn4HcH0OokSp9uZA0w0M4s6MHd/3d2XhMvvAa/QxewJ\nWWoK8IAHFhI8MzUmhjgmAmvdfUMMx97D3f8MvN2uOJXpnD4JPOXub7v7O8BTwORMxOfu8/2DGTwW\nEjzrFotOzl8qUvk777Wu4gu/M74AzE73cVPRxfdJJJ8/JZuODgI2Jawnmw5nT53wj66O4BbsjAmH\n7o4D/ppk8ylm9pKZPWFmH8lkXIAD881ssQVTBbWXyvnNhKl0/kce5/mD1KZzypbz+FWCnmoy3X0W\nonRVOMx3XyfDQNlw/j4GvOHuqzvZnrHz1+77JJLPn5JNH2RmQwhmUviOu+9ot3kJwdDQPwF3AI9m\nOLzT3P144F+Bb5rZ6Rk+frcs+EmK84DfJ9kc9/nbiwdjFln5fIKZVRLMW1jTSZW4Pgt3Ax8GjgVe\nJxiqykYX0XWvJiPnr6vvk3R+/pRsOtoCjEtYTzYdzp46ZpYHlADbMxGcmeUTfDBq3P2P7be7+w53\nrw+X5wH5FszAkBH+wRRD24BHCIYrEqVyfqP2r8ASd3+j/Ya4z1/ojbahRet8OqdYz6OZfQU4F5gW\nfiF1kMJnIRLu/oa7t7r7buC/Ozlu3OcvD/gs8FBndTJx/jr5Ponk86dk09Ei4FALfqCtgGC4ZW67\nOonT7VwAPNPZH1w6hWO8vwZecff/6KTOAW3Xj8zsJIL/xplKhIPNbGjbMsGF5JfbVZsLfNkC/wzU\nJXTZM6XT/6OM8/wl6HY6J+BJ4GwLpnUaQXCun8xEcGY2GfgBcJ67N3RSJ5XPQlTxJV4DPL+T46by\ndx6lScDf3X1zso2ZOH9dfJ9E8/mL6k6HvvwiuGNqFcHdKpVh2a0Ef1wARQRDMGuAvwETMhTXaQRd\n2mXA0vB1DnAFcEVY5ypgBcHdNQuBf8ngeZsQHvelMIa2c5cYnwF3hed2OVCe4f+2gwmSR0lCWWzn\njyDpvQ40E4x7X0Zw/W8BsBp4GtgvrFsO/Cqh7VfDz+Aa4NIMxreGYLy+7TPYdmfmgcC8rj4LGYpv\nZvjZWkbwxTmmfXzheoe/80zEF5b/tu0zl1A3o+evi++TSD5/mq5GREQip2E0ERGJnJKNiIhETslG\nREQip2QjIiKRU7IREZHIKdmIiEjklGxERCRySjYiMTKzE8MJI4vCp8ZXmNnRccclkm56qFMkZmb2\nY4JZKQYBm939JzGHJJJ2SjYiMQvn5loE7CKYHqc15pBE0k7DaCLxGwkMIfi1xKKYYxGJhHo2IjEz\ns7kEvxR5MMGkkVfFHJJI2uXFHYDIQGZmXwaa3f1BM8sFXjCzM939mbhjE0kn9WxERCRyumYjIiKR\nU7IREZHIKdmIiEjklGxERCRySjYiIhI5JRsREYmcko2IiETu/wPrXVJU8stF2AAAAABJRU5ErkJg\ngg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fcf2d81c210>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[9000] (line) cost: 0.2173, acc: 60.00% | (sigmoid) cost: 0.1349, acc: 100.00%\n"
]
},
{
"data": {
"image/png": 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ggIC7VwFVAOXl5V4zs6Zb9fO+XUbL0I7/tc/dWcqkcZO6rF+6bTq1TwOTZkJx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\n74dGCXXJAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fceb4751c50>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[10000] (line) cost: 0.2152, acc: 60.00% | (sigmoid) cost: 0.1289, acc: 100.00%\n"
]
},
{
"data": {
"image/png": 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8mZuPv5mi/KK4yxGRHNdj2JjZfwP3RfBGPglY7O5LwvPcD0wBksMmleOBJ919Tbjvk8Bk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\nRE5hIyIikVPYiIhI5P4fROfUEAscJvQAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fcea47ed310>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sess = tf.Session()\n",
"sess.run(tf.global_variables_initializer())\n",
"\n",
"hx_l = np.linspace(xlim[0], xlim[1], 100)\n",
"hx_s = np.linspace(xlim[0], xlim[1], 100)\n",
"\n",
"for step in range(10001):\n",
" \"\"\"\n",
" cost_val, _ = sess.run([cost, train], feed_dict={X: x_data, Y: y_data})\n",
" if step % interval == 0:\n",
" print(step, cost_val)\n",
" \"\"\"\n",
" xt, yt, _1, _2, _3, _4 = sess.run([X, Y, cost_line, train_line, cost_sigmoid, train_sigmoid],\n",
" {X: x_data, Y: y_data})\n",
"\n",
" # Accuracy report\n",
" W_l, b_l, h_l, c_l, a_l = sess.run([W_line, b_line, hypo_line, cost_line, accuracy_line], \n",
" {X: x_data, Y: y_data})\n",
" W_s, b_s, h_s, c_s, a_s = sess.run([W_sigmoid, b_sigmoid, hypo_sigmoid, cost_sigmoid, accuracy_sigmoid], \n",
" {X: x_data, Y: y_data})\n",
"\n",
" if step % 1000 == 0:\n",
" print(\"[{}] (line) cost: {:.4f}, acc: {:.2%} | (sigmoid) cost: {:.4f}, acc: {:.2%}\".format(step, c_l, a_l, c_s, a_s))\n",
" plt.plot(x_data, y_data, 'bo')\n",
" plt.plot(x_data, h_l, 'ro')\n",
" plt.plot(x_data, h_s, 'go')\n",
"\n",
" # learning linear function\n",
" hy_l = W_l * hx_l + b_l\n",
" plt.plot(hx_l, hy_l[0], 'r')\n",
" # learning sigmoid function\n",
" hy_s = 1 / (1 + np.exp(-(W_s * hx_s + b_s)))\n",
" plt.plot(hx_s, hy_s[0], 'g')\n",
" # y = 0.5\n",
" plt.plot(hx_s, np.full(hx_s.shape[0], 0.5), 'black')\n",
"\n",
" # \n",
" plt.xlabel('x')\n",
" plt.ylabel('y')\n",
" plt.xlim(xlim[0], xlim[1])\n",
" plt.ylim(ylim[0], ylim[1])\n",
" plt.legend()\n",
" plt.show()\n",
"\n",
" if a_l == 1.0 and a_s == 1.0:\n",
" pass #break\n",
" elif math.isnan(c_l) and math.isnan(c_s):\n",
" break"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2 - tf 1.1",
"language": "python",
"name": "python2-tf1.1"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.6"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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