{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "##### 교재 페이지 (213-218) #####" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "6) 퍼셉트론(XOR)-실습1" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "WARNING:tensorflow:From C:\\Users\\jsdata00010\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\framework\\op_def_library.py:263: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n", "Instructions for updating:\n", "Colocations handled automatically by placer.\n", "WARNING:tensorflow:From C:\\Users\\jsdata00010\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\ops\\math_ops.py:3066: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n", "Instructions for updating:\n", "Use tf.cast instead.\n", "step : 0 \n", "cost : 0.7058966 \n", "Weight :\n", " 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[0.04908374]] \n", "Correct:\n", " [[0.]\n", " [1.]\n", " [1.]\n", " [0.]] \n", "Accuracy: 1.0\n", "step : 9500 \n", "cost : 0.034847904 \n", "Weight :\n", " [array([[3.9809434, 6.490061 ],\n", " [3.980885 , 6.489506 ]], dtype=float32), array([[-8.919894],\n", " [ 8.509673]], dtype=float32)] \n", "bias :\n", " [array([-6.039699 , -2.6507537], dtype=float32), array([-3.9131722], dtype=float32)]\n", "Hypothesis:\n", " [[0.03314286]\n", " [0.96794766]\n", " [0.96794915]\n", " [0.03972358]] \n", "Correct:\n", " [[0.]\n", " [1.]\n", " [1.]\n", " [0.]] \n", "Accuracy: 1.0\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "step : 10000 \n", "cost : 0.029762499 \n", "Weight :\n", " [array([[4.1038847, 6.55583 ],\n", " [4.103827 , 6.5553102]], dtype=float32), array([[-9.236728],\n", " [ 8.7547 ]], dtype=float32)] \n", "bias :\n", " [array([-6.2363 , -2.7203562], dtype=float32), array([-4.0180316], dtype=float32)]\n", "Hypothesis:\n", " [[0.02944958]\n", " [0.9726853 ]\n", " [0.9726864 ]\n", " [0.03320596]] \n", "Correct:\n", " [[0.]\n", " [1.]\n", " [1.]\n", " [0.]] \n", "Accuracy: 1.0\n" ] } ], "source": [ "import tensorflow as tf\n", "import numpy as np\n", "\n", "x_data = np.array([[0, 0], [0, 1], [1, 0], [1, 1]], dtype=np.float32)\n", "y_data = np.array([[0], [1], [1], [0]], dtype=np.float32)\n", "X = tf.placeholder(tf.float32)\n", "Y = tf.placeholder(tf.float32)\n", "W1 = tf.Variable(tf.random_normal([2, 2]), name='weight1')\n", "b1 = tf.Variable(tf.random_normal([2]), name='bias1')\n", "layer1 = tf.sigmoid(tf.matmul(X, W1) + b1)\n", "\n", "W2 = tf.Variable(tf.random_normal([2, 1]), name='weight2')\n", "b2 = tf.Variable(tf.random_normal([1]), name='bias2')\n", "hypothesis = tf.sigmoid(tf.matmul(layer1, W2) + b2)\n", "\n", "# cost/loss function\n", "cost = -tf.reduce_mean(Y * tf.log(hypothesis) + (1 - Y) * tf.log(1 - hypothesis))\n", "train = tf.train.GradientDescentOptimizer(learning_rate=0.1).minimize(cost)\n", "# Accuracy computation\n", "# True if hypothesis>0.2 else False\n", "predicted = tf.cast(hypothesis > 0.2, dtype=tf.float32)\n", "accuracy = tf.reduce_mean(tf.cast(tf.equal(predicted, Y), dtype=tf.float32))\n", "# Launch graph\n", "with tf.Session() as sess:\n", " sess.run(tf.global_variables_initializer())\n", " for step in range(10001):\n", " sess.run(train, feed_dict={X: x_data, Y: y_data})\n", " if step % 500 == 0:\n", " print(\"step :\",step, \"\\ncost :\", sess.run(cost, feed_dict={X: x_data, Y: y_data}), \n", " \"\\nWeight :\\n\", sess.run([W1, W2]), \"\\nbias :\\n\", sess.run([b1, b2]))\n", " h, c, a = sess.run([hypothesis, predicted, accuracy], feed_dict={X: x_data, Y: y_data})\n", " print(\"Hypothesis:\\n\", h, \"\\nCorrect:\\n \", c, \"\\nAccuracy: \", a)" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "step : 0 \n", "cost : 0.83947134 \n", "Weight :\n", " [array([[ 1.1775712 , 0.11065076],\n", " [-0.17623563, 1.1256512 ]], 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1.2250036 ],\n", " [-0.39247876]], dtype=float32)] \n", "bias :\n", " [array([ 0.32849675, -0.33936903], dtype=float32), array([-0.7466493], dtype=float32)]\n", "Hypothesis:\n", " [[0.45073617]\n", " [0.49823207]\n", " [0.5361537 ]\n", " [0.5378568 ]] \n", "Correct:\n", " [[1.]\n", " [1.]\n", " [1.]\n", " [1.]] \n", "Accuracy: 0.5\n", "step : 1500 \n", "cost : 0.63466346 \n", "Weight :\n", " [array([[2.14403 , 0.09113888],\n", " [2.046004 , 1.1510817 ]], dtype=float32), array([[ 1.8939075 ],\n", " [-0.73136616]], dtype=float32)] \n", "bias :\n", " [array([ 0.05087023, -0.33117354], dtype=float32), array([-1.1104271], dtype=float32)]\n", "Hypothesis:\n", " [[0.39053333]\n", " [0.5171328 ]\n", " [0.56750745]\n", " [0.55847734]] \n", "Correct:\n", " [[1.]\n", " [1.]\n", " [1.]\n", " [1.]] \n", "Accuracy: 0.5\n", "step : 2000 \n", "cost : 0.5669718 \n", "Weight :\n", " [array([[2.961324 , 0.22651072],\n", " [3.0236864 , 1.3219783 ]], dtype=float32), array([[ 2.8586113],\n", " [-1.3100039]], dtype=float32)] \n", "bias :\n", " [array([-0.44747543, -0.3995287 ], dtype=float32), array([-1.532309], dtype=float32)]\n", "Hypothesis:\n", " [[0.28020787]\n", " [0.54658437]\n", " [0.62568116]\n", " [0.579417 ]] \n", "Correct:\n", " [[1.]\n", " [1.]\n", " [1.]\n", " [1.]] \n", "Accuracy: 0.5\n", "step : 2500 \n", "cost : 0.4880554 \n", "Weight :\n", " [array([[3.7509255, 0.5904746],\n", " [3.935724 , 1.5142827]], dtype=float32), array([[ 3.838536 ],\n", " [-2.1778083]], dtype=float32)] \n", "bias :\n", " [array([-0.86778843, -0.87578267], dtype=float32), array([-1.9048926], dtype=float32)]\n", "Hypothesis:\n", " [[0.1962071 ]\n", " [0.5836642 ]\n", " [0.68903536]\n", " [0.5608507 ]] \n", "Correct:\n", " [[0.]\n", " [1.]\n", " [1.]\n", " [1.]] \n", "Accuracy: 0.75\n", "step : 3000 \n", "cost : 0.35617423 \n", "Weight :\n", " [array([[4.4188194, 1.4900854],\n", " [4.63149 , 1.7220628]], dtype=float32), array([[ 4.732727],\n", " [-3.577252]], dtype=float32)] \n", "bias :\n", " [array([-1.2762487, -2.1409667], dtype=float32), array([-2.3083324], dtype=float32)]\n", "Hypothesis:\n", " [[0.16084546]\n", " [0.69960326]\n", " [0.73150676]\n", " [0.4397913 ]] \n", "Correct:\n", " [[0.]\n", " [1.]\n", " [1.]\n", " [1.]] \n", "Accuracy: 0.75\n", "step : 3500 \n", "cost : 0.20610176 \n", "Weight :\n", " [array([[4.952432 , 2.3844113],\n", " [5.1004543, 2.4166331]], dtype=float32), array([[ 5.646111],\n", " [-5.193532]], dtype=float32)] \n", "bias :\n", " [array([-1.6812843, -3.536402 ], dtype=float32), array([-2.6643724], dtype=float32)]\n", "Hypothesis:\n", " [[0.12727174]\n", " [0.82125247]\n", " [0.82173336]\n", " [0.2554773 ]] \n", "Correct:\n", " [[0.]\n", " [1.]\n", " [1.]\n", " [1.]] \n", "Accuracy: 0.75\n", "step : 4000 \n", "cost : 0.12293056 \n", "Weight :\n", " [array([[5.3130145, 2.978807 ],\n", " [5.419607 , 2.9947853]], dtype=float32), array([[ 6.42288 ],\n", " [-6.413818]], dtype=float32)] \n", "bias :\n", " [array([-1.9849229, -4.495025 ], dtype=float32), array([-2.9592419], dtype=float32)]\n", "Hypothesis:\n", " [[0.09498736]\n", " [0.89024484]\n", " [0.88960075]\n", " [0.14672604]] \n", "Correct:\n", " [[0.]\n", " [1.]\n", " [1.]\n", " [0.]] \n", "Accuracy: 1.0\n", "step : 4500 \n", "cost : 0.082764655 \n", "Weight :\n", " [array([[5.5620584, 3.366551 ],\n", " [5.645983 , 3.3790548]], dtype=float32), array([[ 7.02787 ],\n", " [-7.265032]], dtype=float32)] \n", "bias :\n", " [array([-2.1924276, -5.1144376], dtype=float32), array([-3.2059712], dtype=float32)]\n", "Hypothesis:\n", " [[0.07286152]\n", " [0.9253508 ]\n", " [0.92488575]\n", " [0.09492624]] \n", "Correct:\n", " [[0.]\n", " [1.]\n", " [1.]\n", " [0.]] \n", "Accuracy: 1.0\n", "step : 5000 \n", "cost : 0.06106762 \n", "Weight :\n", " [array([[5.7464046, 3.6376102],\n", " [5.816439 , 3.6484103]], dtype=float32), array([[ 7.5072317],\n", " [-7.8897734]], dtype=float32)] \n", "bias :\n", " [array([-2.339563, -5.544045], dtype=float32), array([-3.41296], dtype=float32)]\n", "Hypothesis:\n", " [[0.05820492]\n", " [0.9447365 ]\n", " [0.9444138 ]\n", " [0.06785122]] \n", "Correct:\n", " [[0.]\n", " [1.]\n", " [1.]\n", " [0.]] \n", "Accuracy: 1.0\n", "step : 5500 \n", "cost : 0.047910262 \n", "Weight :\n", " [array([[5.890626 , 3.8403544],\n", " [5.951297 , 3.850025 ]], dtype=float32), array([[ 7.8995733],\n", " [-8.374982 ]], dtype=float32)] \n", "bias :\n", " [array([-2.4497056, -5.8634467], dtype=float32), array([-3.5888672], dtype=float32)]\n", "Hypothesis:\n", " [[0.04811287]\n", " [0.9566001 ]\n", " [0.95636606]\n", " [0.05194989]] \n", "Correct:\n", " [[0.]\n", " [1.]\n", " [1.]\n", " [0.]] \n", "Accuracy: 1.0\n", "step : 6000 \n", "cost : 0.03920842 \n", "Weight :\n", " [array([[6.008082 , 3.9998994],\n", " [6.061999 , 4.0087337]], dtype=float32), array([[ 8.229937],\n", " [-8.76861 ]], dtype=float32)] \n", "bias :\n", " [array([-2.5360289, -6.113587 ], dtype=float32), array([-3.7407615], dtype=float32)]\n", "Hypothesis:\n", " [[0.04084438]\n", " [0.9644784 ]\n", " [0.964301 ]\n", " [0.04171696]] \n", "Correct:\n", " [[0.]\n", " [1.]\n", " [1.]\n", " [0.]] \n", "Accuracy: 1.0\n", "step : 6500 \n", "cost : 0.033075273 \n", "Weight :\n", " [array([[6.106615 , 4.130222 ],\n", " [6.1554165, 4.1384025]], dtype=float32), array([[ 8.514464],\n", " [-9.098425]], dtype=float32)] \n", "bias :\n", " [array([-2.6061506, -6.317107 ], dtype=float32), array([-3.8739095], dtype=float32)]\n", "Hypothesis:\n", " [[0.03540042]\n", " [0.9700413 ]\n", " [0.9699023 ]\n", " [0.03466716]] \n", "Correct:\n", " [[0.]\n", " [1.]\n", " [1.]\n", " [0.]] \n", "Accuracy: 1.0\n", "step : 7000 \n", "cost : 0.028541476 \n", "Weight :\n", " [array([[6.191156 , 4.2396975],\n", " [6.235937 , 4.2473454]], dtype=float32), array([[ 8.763926],\n", " [-9.381582]], dtype=float32)] \n", "bias :\n", " [array([-2.664715 , -6.4875073], dtype=float32), array([-3.9921608], dtype=float32)]\n", "Hypothesis:\n", " [[0.0311892 ]\n", " [0.97415686]\n", " [0.9740448 ]\n", " [0.02955353]] \n", "Correct:\n", " [[0.]\n", " [1.]\n", " [1.]\n", " [0.]] \n", "Accuracy: 1.0\n", "step : 7500 \n", "cost : 0.025064439 \n", "Weight :\n", " [array([[6.2649794, 4.333667 ],\n", " [6.3065085, 4.3408656]], dtype=float32), array([[ 8.985798],\n", " [-9.629295]], dtype=float32)] \n", "bias :\n", " [array([-2.7147074, -6.6333547], dtype=float32), array([-4.0983586], dtype=float32)]\n", "Hypothesis:\n", " [[0.0278441 ]\n", " [0.97731423]\n", " [0.9772216 ]\n", " [0.02569395]] \n", "Correct:\n", " [[0.]\n", " [1.]\n", " [1.]\n", " [0.]] \n", "Accuracy: 1.0\n", "step : 8000 \n", "cost : 0.022319213 \n", "Weight :\n", " [array([[6.330358 , 4.4157085],\n", " [6.3691955, 4.4225264]], dtype=float32), array([[ 9.185439],\n", " [-9.84925 ]], dtype=float32)] \n", "bias :\n", " [array([-2.7581332, -6.760386 ], dtype=float32), array([-4.1946425], dtype=float32)]\n", "Hypothesis:\n", " [[0.02512798]\n", " [0.9798071 ]\n", " [0.9797291 ]\n", " [0.02268767]] \n", "Correct:\n", " [[0.]\n", " [1.]\n", " [1.]\n", " [0.]] \n", "Accuracy: 1.0\n", "step : 8500 \n", "cost : 0.020100199 \n", "Weight :\n", " [array([[6.3889275, 4.48833 ],\n", " [6.4254966, 4.4948173]], dtype=float32), array([[ 9.366816],\n", " [-10.04691 ]], dtype=float32)] \n", "bias :\n", " [array([-2.7963972, -6.872593 ], dtype=float32), array([-4.282646], dtype=float32)]\n", "Hypothesis:\n", " [[0.02288169]\n", " [0.9818218 ]\n", " [0.98175514]\n", " [0.02028587]] \n", "Correct:\n", " [[0.]\n", " [1.]\n", " [1.]\n", " [0.]] \n", "Accuracy: 1.0\n", "step : 9000 \n", "cost : 0.018271558 \n", "Weight :\n", " [array([[6.4419007, 4.553339 ],\n", " [6.4765263, 4.5595407]], dtype=float32), array([[ 9.532927],\n", " [-10.226299]], dtype=float32)] \n", "bias :\n", " [array([-2.8305087, -6.972855 ], dtype=float32), array([-4.3636413], dtype=float32)]\n", "Hypothesis:\n", " [[0.02099499]\n", " [0.98348165]\n", " [0.98342395]\n", " [0.0183264 ]] \n", "Correct:\n", " [[0.]\n", " [1.]\n", " [1.]\n", " [0.]] \n", "Accuracy: 1.0\n", "step : 9500 \n", "cost : 0.016739931 \n", "Weight :\n", " [array([[6.4902005, 4.6120887],\n", " [6.523142 , 4.6180344]], dtype=float32), array([[ 9.686105],\n", " [-10.390456]], dtype=float32)] \n", "bias :\n", " [array([-2.86122 , -7.0633154], dtype=float32), array([-4.4386325], dtype=float32)]\n", "Hypothesis:\n", " [[0.01938924]\n", " [0.9848716 ]\n", " [0.984821 ]\n", " [0.01669967]] \n", "Correct:\n", " [[0.]\n", " [1.]\n", " [1.]\n", " [0.]] \n", "Accuracy: 1.0\n", "step : 10000 \n", "cost : 0.015439347 \n", "Weight :\n", " [array([[6.534546 , 4.6656065],\n", " [6.5660086, 4.6713257]], dtype=float32), array([[ 9.828192],\n", " [-10.541728]], dtype=float32)] \n", "bias :\n", " [array([-2.889104 , -7.1456027], dtype=float32), array([-4.5084295], dtype=float32)]\n", "Hypothesis:\n", " [[0.01800671]\n", " [0.9860515 ]\n", " [0.98600686]\n", " [0.01532924]] \n", "Correct:\n", " [[0.]\n", " [1.]\n", " [1.]\n", " [0.]] \n", "Accuracy: 1.0\n" ] } ], "source": [ "#XOR 게이트\n", "x_data = np.array([[0, 0], [0, 1], [1, 0], [1, 1]], dtype=np.float32)\n", "y_data = np.array([[0], [1], [1], [0]], dtype=np.float32)\n", "X = tf.placeholder(tf.float32)\n", "Y = tf.placeholder(tf.float32)\n", "W1 = tf.Variable(tf.random_normal([2, 2]), name='weight1')\n", "b1 = tf.Variable(tf.random_normal([2]), name='bias1')\n", "layer1 = tf.sigmoid(tf.matmul(X, W1) + b1)\n", "\n", "W2 = tf.Variable(tf.random_normal([2, 1]), name='weight2')\n", "b2 = tf.Variable(tf.random_normal([1]), name='bias2')\n", "hypothesis = tf.sigmoid(tf.matmul(layer1, W2) + b2)\n", "\n", "# cost/loss function\n", "cost = -tf.reduce_mean(Y * tf.log(hypothesis) + (1 - Y) * tf.log(1 - hypothesis))\n", "train = tf.train.GradientDescentOptimizer(learning_rate=0.1).minimize(cost)\n", "# Accuracy computation\n", "# True if hypothesis>0.2 else False\n", "predicted = tf.cast(hypothesis > 0.2, dtype=tf.float32)\n", "accuracy = tf.reduce_mean(tf.cast(tf.equal(predicted, Y), dtype=tf.float32))\n", "# Launch graph\n", "with tf.Session() as sess:\n", " sess.run(tf.global_variables_initializer())\n", " for step in range(10001):\n", " sess.run(train, feed_dict={X: x_data, Y: y_data})\n", " if step % 500 == 0:\n", " print(\"step :\",step, \"\\ncost :\", sess.run(cost, feed_dict={X: x_data, Y: y_data}), \n", " \"\\nWeight :\\n\", sess.run([W1, W2]), \"\\nbias :\\n\", sess.run([b1, b2]))\n", " h, c, a = sess.run([hypothesis, predicted, accuracy], feed_dict={X: x_data, Y: y_data})\n", " print(\"Hypothesis:\\n\", h, \"\\nCorrect:\\n \", c, \"\\nAccuracy: \", a)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "step : 0 \n", "cost : 0.9907976 \n", "Weight :\n", " [array([[ 1.0732632 , 0.19082014, 1.943241 , -0.5049198 , 1.3837659 ,\n", " 1.0604074 , 0.21013658],\n", " [-1.9482528 , 0.80715346, 0.25208944, -0.3027498 , 0.26468045,\n", " 0.18057732, -0.05539773]], dtype=float32), array([[ 1.4852211 , -0.42772183, -0.3356586 , -0.5573063 , -1.2012299 ,\n", " -0.17510414, -0.8808167 ],\n", " [ 0.83968633, -0.09776691, 2.0293431 , 0.02381345, 1.4575558 ,\n", " -0.62406915, 1.0998192 ],\n", " [-0.21487391, 0.34920853, -0.16367784, -1.0683519 , -0.4143009 ,\n", " 0.5421552 , -2.0971737 ],\n", " [-0.26807487, -1.194651 , 2.026955 , 0.47140154, 1.0885543 ,\n", " -0.8363566 , 0.21133967],\n", " [-1.4818541 , 0.43110526, -1.4053344 , -0.04024884, 0.9375388 ,\n", " -1.3324509 , -0.07942262],\n", " [ 0.6632619 , 1.4366772 , -1.5464754 , -0.70668715, -0.8930268 ,\n", " -1.3043828 , -1.7850258 ],\n", " [-1.204453 , -0.45174494, 0.6835732 , 3.0165563 , 0.54434514,\n", " 1.069582 , 0.3435125 ]], dtype=float32), array([[-0.22084375, 1.19308 , 1.0764315 , -0.79938704, -0.8273015 ,\n", " 0.07231029, -0.10095999],\n", " [-0.64977443, 1.0622138 , -0.58985525, 0.2865051 , -1.525514 ,\n", " 0.09642287, 0.81217414],\n", " [ 1.5582045 , -0.8539799 , -0.03085944, 1.2140232 , -0.394913 ,\n", " 0.400767 , -1.8174227 ],\n", " [-2.0260592 , -1.3303446 , 0.71930826, -0.21744005, 1.0208452 ,\n", " -0.82503694, 0.57048666],\n", " [-1.7982751 , -0.30153832, -0.4145481 , -0.0541993 , -0.1627168 ,\n", " 0.7470903 , 1.4361944 ],\n", " [ 0.3634403 , 1.4051269 , -0.97946626, 0.09148227, -1.430804 ,\n", " -0.24036065, -0.66670746],\n", " [ 0.11337901, 1.0605444 , -1.1655362 , -2.0237215 , -0.33581075,\n", " -0.3110631 , -0.07203282]], dtype=float32)] \n", "bias :\n", " [array([ 0.40977874, -1.604372 , -0.24382702, -0.7397975 , -0.572092 ,\n", " 1.0909759 , -0.8968171 ], dtype=float32), array([ 1.2592614 , -0.00527734, -1.0526279 , 1.6974074 , -0.66167605,\n", " 1.3265486 , -2.4676158 ], dtype=float32), array([ 0.12481381, -0.56403226, 1.6089032 , -1.0958793 , -0.55614376,\n", " -1.080449 , -0.39116025], dtype=float32)]\n", "Hypothesis:\n", " [[0.1056568 0.63710123 0.89382017 0.17155668 0.09843913 0.18282974\n", " 0.5663442 ]\n", " [0.08212814 0.6085869 0.8762902 0.1858395 0.09542793 0.19877529\n", " 0.62631863]\n", " [0.1142526 0.6802196 0.8885054 0.16853103 0.09055057 0.1905666\n", " 0.6049796 ]\n", " [0.08785236 0.6412722 0.8726486 0.18179524 0.09273511 0.20396945\n", " 0.6609347 ]] \n", "Correct:\n", " [[0. 1. 1. 0. 0. 0. 1.]\n", " [0. 1. 1. 0. 0. 0. 1.]\n", " [0. 1. 1. 0. 0. 0. 1.]\n", " [0. 1. 1. 0. 0. 1. 1.]] \n", "Accuracy: 0.4642857\n", "step : 500 \n", "cost : 0.693305 \n", "Weight :\n", " [array([[ 1.0093862 , 0.17028497, 1.9555346 , -0.4530039 , 1.4053316 ,\n", " 1.0482056 , 0.23864773],\n", " [-1.9942786 , 0.8051655 , 0.30577677, -0.26446462, 0.20725632,\n", " 0.21892859, -0.02717793]], dtype=float32), array([[ 1.3632146 , -0.47152275, -0.2492828 , -0.61980736, -1.2178972 ,\n", " -0.19479544, -0.8769709 ],\n", " [ 0.7747631 , -0.12757917, 2.0931826 , -0.00655223, 1.4168589 ,\n", " -0.61900014, 1.096611 ],\n", " [-0.3878552 , 0.27449158, -0.01187012, -1.1578506 , -0.4937607 ,\n", " 0.52430046, -2.1005151 ],\n", " [-0.3236884 , -1.2163929 , 2.0816164 , 0.4402561 , 1.0653309 ,\n", " -0.8319082 , 0.2103541 ],\n", " [-1.632536 , 0.36646906, -1.2801391 , -0.10890425, 0.87167615,\n", " -1.3427676 , -0.08193848],\n", " [ 0.46523342, 1.3518212 , -1.3610028 , -0.81594414, -0.98696905,\n", " -1.3115058 , -1.7896534 ],\n", " [-1.2778523 , -0.48247385, 0.75060594, 2.978138 , 0.5116415 ,\n", " 1.0680863 , 0.34210348]], dtype=float32), array([[ 0.25582978, 1.0993521 , 0.5831375 , -0.4325661 , -0.25847927,\n", " 0.45295268, -0.12283897],\n", " [-0.17354594, 0.9814199 , -1.0394197 , 0.63471067, -0.9690516 ,\n", " 0.4589213 , 0.76640725],\n", " [ 1.6635244 , -0.8608046 , -0.13388288, 1.2823906 , -0.27839497,\n", " 0.47801816, -1.8171643 ],\n", " [-1.5723214 , -1.4365139 , 0.2107762 , 0.10082563, 1.5479707 ,\n", " -0.4864922 , 0.5098756 ],\n", " [-1.6108315 , -0.32093757, -0.58075815, 0.06578603, 0.03877397,\n", " 0.8767353 , 1.4134678 ],\n", " [ 0.6458899 , 1.3516501 , -1.2778666 , 0.29640004, -1.0998229 ,\n", " -0.02294552, -0.6868237 ],\n", " [ 0.11851817, 1.0616176 , -1.1680497 , -2.0201051 , -0.3305452 ,\n", " -0.30711672, -0.07083476]], dtype=float32)] \n", "bias :\n", " [array([ 0.28646952, -1.5858501 , -0.19788796, -0.6564019 , -0.5966334 ,\n", " 1.0411631 , -0.8843379 ], dtype=float32), array([ 1.0276277 , -0.10327177, -0.8309906 , 1.5683311 , -0.77130353,\n", " 1.3268847 , -2.4732158 ], dtype=float32), array([ 0.7950764 , -0.7037855 , 0.908429 , -0.60473925, 0.23842043,\n", " -0.56523967, -0.45907703], dtype=float32)]\n", "Hypothesis:\n", " [[0.52365893 0.49769804 0.53073484 0.4911998 0.51282454 0.4881123\n", " 0.44833642]\n", " [0.4703257 0.46138808 0.47896856 0.5266549 0.50875556 0.5121677\n", " 0.49846926]\n", " [0.5277867 0.548842 0.528173 0.46969002 0.47211143 0.4838129\n", " 0.50115 ]\n", " [0.4638788 0.4942034 0.47966215 0.50543976 0.48322517 0.5007533\n", " 0.5518155 ]] \n", "Correct:\n", " [[1. 1. 1. 1. 1. 1. 1.]\n", " [1. 1. 1. 1. 1. 1. 1.]\n", " [1. 1. 1. 1. 1. 1. 1.]\n", " [1. 1. 1. 1. 1. 1. 1.]] \n", "Accuracy: 0.5\n", "step : 1000 \n", "cost : 0.6925191 \n", "Weight :\n", " [array([[ 0.97636974, 0.14102896, 1.9563034 , -0.43934494, 1.4128233 ,\n", " 1.0557679 , 0.26631585],\n", " [-2.0277193 , 0.7904373 , 0.335072 , -0.27737668, 0.15684101,\n", " 0.2918566 , -0.00972637]], dtype=float32), array([[ 1.3716154e+00, -4.5846337e-01, -2.6527864e-01, -6.1796421e-01,\n", " -1.1991183e+00, -2.1329151e-01, -8.7174547e-01],\n", " [ 7.7589983e-01, -1.2908939e-01, 2.0967307e+00, -3.6686931e-03,\n", " 1.4011285e+00, -6.1529285e-01, 1.0945755e+00],\n", " [-3.8388813e-01, 2.7890828e-01, -2.4730482e-03, -1.1570734e+00,\n", " -5.1723546e-01, 5.0995684e-01, -2.1015582e+00],\n", " [-3.1567252e-01, -1.2124243e+00, 2.0719955e+00, 4.4366857e-01,\n", " 1.0651569e+00, -8.2918906e-01, 2.1061541e-01],\n", " [-1.6414154e+00, 3.6531070e-01, -1.2731242e+00, -1.0131940e-01,\n", " 8.5350811e-01, -1.3495314e+00, -8.2205519e-02],\n", " [ 4.8230135e-01, 1.3605253e+00, -1.3645788e+00, -8.1332392e-01,\n", " -1.0080930e+00, -1.3179998e+00, -1.7910036e+00],\n", " [-1.2743890e+00, -4.8011124e-01, 7.4815166e-01, 2.9811914e+00,\n", " 5.0494146e-01, 1.0668461e+00, 3.4193254e-01]], dtype=float32), array([[ 0.26629034, 1.1105499 , 0.58661413, -0.40595657, -0.23677321,\n", " 0.48079896, -0.08839551],\n", " [-0.1467048 , 0.99149865, -1.0226619 , 0.6518247 , -0.9422959 ,\n", " 0.47915757, 0.7637979 ],\n", " [ 1.6668798 , -0.85217017, -0.13302557, 1.2769154 , -0.28302693,\n", " 0.47674757, -1.8102603 ],\n", " [-1.5914444 , -1.4560035 , 0.1822732 , 0.07211088, 1.5224041 ,\n", " -0.50853467, 0.48977923],\n", " [-1.6017755 , -0.31360713, -0.57588977, 0.05462446, 0.0346022 ,\n", " 0.87094045, 1.4041002 ],\n", " [ 0.64458406, 1.3515651 , -1.2846007 , 0.29303968, -1.1028601 ,\n", " -0.02267499, -0.6825675 ],\n", " [ 0.1195332 , 1.0629566 , -1.1670984 , -2.0195267 , -0.32994446,\n", " -0.30635044, -0.06958427]], dtype=float32)] \n", "bias :\n", " [array([ 0.19805901, -1.5937855 , -0.19528745, -0.67532265, 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[-2.0721028 , 0.776664 , 0.3667028 , -0.29047087, 0.11609502,\n", " 0.37010327, 0.01905561]], dtype=float32), array([[ 1.3906882e+00, -4.4397631e-01, -2.7978542e-01, -6.3018072e-01,\n", " -1.1816677e+00, -2.3209481e-01, -8.6657184e-01],\n", " [ 7.7634662e-01, -1.2980580e-01, 2.0991352e+00, -6.4620737e-04,\n", " 1.3877800e+00, -6.1278963e-01, 1.0927628e+00],\n", " [-3.7867302e-01, 2.8663954e-01, 5.4792729e-03, -1.1640537e+00,\n", " -5.3847468e-01, 4.9604478e-01, -2.1023731e+00],\n", " [-3.0797276e-01, -1.2102964e+00, 2.0623999e+00, 4.4676206e-01,\n", " 1.0657368e+00, -8.2689393e-01, 2.1113354e-01],\n", " [-1.6526619e+00, 3.6554059e-01, -1.2677578e+00, -9.7873412e-02,\n", " 8.3830309e-01, -1.3546755e+00, -8.2034126e-02],\n", " [ 5.0147325e-01, 1.3702167e+00, -1.3692038e+00, -8.1699216e-01,\n", " -1.0268971e+00, -1.3258133e+00, -1.7920042e+00],\n", " [-1.2717665e+00, -4.7818428e-01, 7.4552703e-01, 2.9827363e+00,\n", " 4.9886900e-01, 1.0657475e+00, 3.4192491e-01]], dtype=float32), array([[ 0.28073364, 1.1274589 , 0.60188913, -0.37325257, -0.21173069,\n", " 0.51118475, -0.048626 ],\n", " [-0.12355902, 1.0009619 , -1.0010273 , 0.66799766, -0.9198753 ,\n", " 0.49629056, 0.76099795],\n", " [ 1.6672335 , -0.8464816 , -0.13192241, 1.2705436 , -0.2889839 ,\n", " 0.4732732 , -1.8048067 ],\n", " [-1.6199226 , -1.483495 , 0.15388665, 0.03795552, 1.4888426 ,\n", " -0.5394644 , 0.46278682],\n", " [-1.6010553 , -0.31351715, -0.5753092 , 0.03893607, 0.02453662,\n", " 0.85949636, 1.3907813 ],\n", " [ 0.63844275, 1.3476439 , -1.2903419 , 0.28634447, -1.1106054 ,\n", " -0.02782803, -0.68216944],\n", " [ 0.12011717, 1.0638541 , -1.1664674 , -2.0192707 , -0.32967213,\n", " -0.30595127, -0.06867295]], dtype=float32)] \n", "bias :\n", " [array([ 0.10513565, -1.60157 , -0.19362834, -0.6927981 , -0.6267763 ,\n", " 1.0606017 , -0.8948419 ], dtype=float32), array([ 1.071684 , -0.08393024, -0.85950166, 1.5786653 , -0.8072031 ,\n", " 1.3255489 , -2.4748886 ], dtype=float32), array([ 0.79741764, 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-2.9253829e-01, -6.5632558e-01,\n", " -1.1645159e+00, -2.5257531e-01, -8.6143565e-01],\n", " [ 7.7637947e-01, -1.2950206e-01, 2.1008644e+00, 2.2568968e-03,\n", " 1.3763648e+00, -6.1115390e-01, 1.0911729e+00],\n", " [-3.7266478e-01, 2.9793474e-01, 1.2750874e-02, -1.1787148e+00,\n", " -5.5697274e-01, 4.8188612e-01, -2.1029453e+00],\n", " [-3.0041739e-01, -1.2098879e+00, 2.0531297e+00, 4.4980934e-01,\n", " 1.0671337e+00, -8.2459635e-01, 2.1192639e-01],\n", " [-1.6672170e+00, 3.6704454e-01, -1.2632650e+00, -9.7976081e-02,\n", " 8.2654941e-01, -1.3584203e+00, -8.1455417e-02],\n", " [ 5.2299404e-01, 1.3814983e+00, -1.3738587e+00, -8.2702845e-01,\n", " -1.0431877e+00, -1.3350264e+00, -1.7925992e+00],\n", " [-1.2705089e+00, -4.7657040e-01, 7.4312776e-01, 2.9832036e+00,\n", " 4.9357888e-01, 1.0650065e+00, 3.4207416e-01]], dtype=float32), array([[ 3.0239159e-01, 1.1508156e+00, 6.2532765e-01, -3.3281723e-01,\n", " -1.7736614e-01, 5.4750896e-01, -1.7267241e-03],\n", " [-9.9983051e-02, 1.0111496e+00, -9.7753394e-01, 6.8519753e-01,\n", " -8.9520758e-01, 5.1412064e-01, 7.5986868e-01],\n", " [ 1.6657165e+00, -8.4324044e-01, -1.3201943e-01, 1.2632861e+00,\n", " -2.9527140e-01, 4.6837997e-01, -1.8007087e+00],\n", " [-1.6547490e+00, -1.5183377e+00, 1.2068001e-01, -1.9708944e-03,\n", " 1.4514083e+00, -5.7718599e-01, 4.2912593e-01],\n", " [-1.6064860e+00, -3.1945273e-01, -5.7998151e-01, 1.9209148e-02,\n", " 1.0391655e-02, 8.4375376e-01, 1.3738192e+00],\n", " [ 6.2940210e-01, 1.3402295e+00, -1.2980485e+00, 2.7659276e-01,\n", " -1.1200345e+00, -3.6683280e-02, -6.8518877e-01],\n", " [ 1.2031425e-01, 1.0643393e+00, -1.1661998e+00, -2.0193267e+00,\n", " -3.2970950e-01, -3.0589673e-01, -6.8101838e-02]], dtype=float32)] \n", "bias :\n", " [array([ 0.00506047, -1.6089917 , -0.19222176, -0.7086573 , -0.63846195,\n", " 1.0625694 , -0.90373904], dtype=float32), array([ 1.0923765 , -0.07707047, -0.8745608 , 1.5790989 , -0.81973946,\n", " 1.3243041 , -2.4745638 ], dtype=float32), array([ 0.79404265, -0.73669374, 0.89367294, -0.6007224 , 0.2518312 ,\n", " -0.557573 , -0.48525253], dtype=float32)]\n", "Hypothesis:\n", " [[0.5220351 0.499799 0.52402526 0.49202865 0.5153159 0.49668863\n", " 0.4581033 ]\n", " [0.48422858 0.47462434 0.47524726 0.5228649 0.50467473 0.51184773\n", " 0.49165508]\n", " [0.5291009 0.5460941 0.5284473 0.4762244 0.4844254 0.49428335\n", " 0.50978047]\n", " [0.46688798 0.487256 0.47355324 0.5089146 0.4933579 0.498208\n", " 0.54629683]] \n", "Correct:\n", " [[1. 1. 1. 1. 1. 1. 1.]\n", " [1. 1. 1. 1. 1. 1. 1.]\n", " [1. 1. 1. 1. 1. 1. 1.]\n", " [1. 1. 1. 1. 1. 1. 1.]] \n", "Accuracy: 0.5\n", "step : 2500 \n", "cost : 0.68896717 \n", "Weight :\n", " [array([[ 0.95539075, 0.03960338, 1.9793928 , -0.42464134, 1.4525082 ,\n", " 1.1198447 , 0.343871 ],\n", " [-2.1988657 , 0.7504173 , 0.44933692, -0.32349584, 0.05575022,\n", " 0.5527802 , 0.10807133]], dtype=float32), array([[ 1.4645009 , -0.4106524 , -0.303656 , -0.6976535 , -1.1471584 ,\n", " -0.27624893, -0.85633963],\n", " [ 0.7763705 , -0.1281139 , 2.102023 , 0.00460308, 1.3664998 ,\n", " -0.6102572 , 1.0898122 ],\n", " [-0.36586297, 0.31297323, 0.01939553, -1.2022318 , -0.5724737 ,\n", " 0.4666284 , -2.1032453 ],\n", " [-0.2929162 , -1.2114099 , 2.0441704 , 0.45312417, 1.0695592 ,\n", " -0.82200855, 0.21303046],\n", " [-1.6863183 , 0.36946136, -1.2594513 , -0.10150261, 0.818794 ,\n", " -1.3609421 , -0.08047116],\n", " [ 0.5474972 , 1.3945917 , -1.3785675 , -0.8446037 , -1.0568622 ,\n", " -1.3460897 , -1.7927108 ],\n", " [-1.2714095 , -0.47532716, 0.7410029 , 2.982888 , 0.4892893 ,\n", " 1.0647651 , 0.34239215]], dtype=float32), array([[ 0.3314494 , 1.1809064 , 0.6572529 , -0.28376994, -0.13274318,\n", " 0.59047407, 0.05326637],\n", " [-0.07542367, 1.0225309 , -0.95157254, 0.70422274, -0.8673448 ,\n", " 0.53334415, 0.7610399 ],\n", " [ 1.6621693 , -0.84247416, -0.13375315, 1.2547222 , -0.30256116,\n", " 0.46179068, -1.7983106 ],\n", " [-1.6973338 , -1.5615891 , 0.08100211, -0.04967355, 1.4079592 ,\n", " -0.6233447 , 0.38721344],\n", " [-1.6178356 , -0.33119765, -0.5900443 , -0.00487586, -0.00832338,\n", " 0.82351 , 1.3527529 ],\n", " [ 0.6169791 , 1.3289845 , -1.3083317 , 0.26320145, -1.1318619 ,\n", " -0.04965162, -0.69207305],\n", " [ 0.12010676, 1.0644001 , -1.1663275 , -2.0197344 , -0.330102 ,\n", " -0.30621535, -0.06790314]], dtype=float32)] \n", "bias :\n", " [array([-0.10452357, -1.6161325 , -0.1905173 , -0.72282434, -0.64844376,\n", " 1.0566818 , -0.91563016], dtype=float32), array([ 1.112066 , -0.07239025, -0.88968015, 1.5767602 , -0.82817197,\n", " 1.3232939 , -2.473328 ], dtype=float32), array([ 0.78926396, -0.7514565 , 0.8915981 , -0.6009613 , 0.25674152,\n", " -0.5602934 , -0.49592507], dtype=float32)]\n", "Hypothesis:\n", " [[0.52067405 0.49982232 0.52355206 0.4921104 0.514822 0.4983733\n", " 0.46028516]\n", " [0.48493132 0.47615036 0.4747068 0.5210364 0.5027339 0.5097353\n", " 0.48960912]\n", " [0.5322828 0.5491293 0.53106654 0.47835457 0.48492733 0.4985282\n", " 0.51226574]\n", " [0.46525645 0.48369035 0.47172934 0.50872654 0.4942034 0.49539632\n", " 0.54394853]] \n", "Correct:\n", " [[1. 1. 1. 1. 1. 1. 1.]\n", " [1. 1. 1. 1. 1. 1. 1.]\n", " [1. 1. 1. 1. 1. 1. 1.]\n", " [1. 1. 1. 1. 1. 1. 1.]] \n", "Accuracy: 0.5\n", "step : 3000 \n", "cost : 0.6866953 \n", "Weight :\n", " [array([[ 9.9284136e-01, 1.4110280e-03, 1.9959162e+00, -4.2677781e-01,\n", " 1.4782344e+00, 1.1606576e+00, 3.8103366e-01],\n", " [-2.2855382e+00, 7.3720807e-01, 5.0873673e-01, -3.4685656e-01,\n", " 3.3626080e-02, 6.6376072e-01, 1.6677481e-01]], dtype=float32), array([[ 1.5231469 , -0.39146024, -0.3131832 , -0.7568545 , -1.1294174 ,\n", " -0.30488032, -0.851287 ],\n", " [ 0.77677387, -0.12562789, 2.102632 , 0.0057962 , 1.35791 ,\n", " -0.61009073, 1.0887073 ],\n", " [-0.357949 , 0.33216587, 0.02540628, -1.2368563 , -0.58473605,\n", " 0.44926307, -2.1032326 ],\n", " [-0.28560418, -1.2152835 , 2.0354843 , 0.4572138 , 1.0734663 ,\n", " -0.81879795, 0.21450575],\n", " [-1.7118797 , 0.37235156, -1.256136 , -0.10827813, 0.8158917 ,\n", " -1.3621813 , -0.0790512 ],\n", " [ 0.57571065, 1.4097329 , -1.3833026 , -0.87182516, -1.0676277 ,\n", " -1.3596494 , -1.7922198 ],\n", " [-1.2758065 , -0.47454286, 0.7392032 , 2.9821277 , 0.4864215 ,\n", " 1.0652535 , 0.34291366]], dtype=float32), array([[ 0.36851543, 1.218473 , 0.6986839 , -0.224641 , -0.07614474,\n", " 0.64119494, 0.11790608],\n", " [-0.04939738, 1.035563 , -0.9223247 , 0.7259681 , -0.83506984,\n", " 0.554634 , 0.7652421 ],\n", " [ 1.6562021 , -0.84446305, -0.13765216, 1.244309 , -0.31155473,\n", " 0.4530465 , -1.7980684 ],\n", " [-1.750009 , -1.6152326 , 0.03254876, -0.10792857, 1.3557051 ,\n", " -0.68042374, 0.33467445],\n", " [-1.6355363 , -0.34919372, -0.6061136 , -0.03410277, -0.03246435,\n", " 0.7980348 , 1.3266994 ],\n", " [ 0.60047597, 1.3133689 , -1.3218507 , 0.2454076 , -1.1468831 ,\n", " -0.06737259, -0.7034257 ],\n", " [ 0.11944377, 1.0639901 , -1.1669104 , -2.0205534 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"output_type": "stream", "text": [ "step : 3500 \n", "cost : 0.68315846 \n", "Weight :\n", " [array([[ 1.0682366 , -0.03941236, 2.0193923 , -0.43255156, 1.5140363 ,\n", " 1.2163426 , 0.4325249 ],\n", " [-2.393019 , 0.72374177, 0.58944833, -0.37764144, 0.01522263,\n", " 0.794179 , 0.23250963]], dtype=float32), array([[ 1.6006407 , -0.36942452, -0.32109675, -0.83852506, -1.1115116 ,\n", " -0.34078565, -0.846293 ],\n", " [ 0.77817607, -0.12209892, 2.1026511 , 0.00493295, 1.3504419 ,\n", " -0.6107816 , 1.0879042 ],\n", " [-0.34788975, 0.3562661 , 0.03067161, -1.2861384 , -0.59352845,\n", " 0.4284013 , -2.102825 ],\n", " [-0.27900267, -1.2222117 , 2.027099 , 0.46294236, 1.0796702 ,\n", " -0.81448495, 0.2164469 ],\n", " [-1.7468244 , 0.3751158 , -1.2531193 , -0.11739378, 0.81922513,\n", " -1.3617039 , -0.07712205],\n", " [ 0.6085302 , 1.4272151 , -1.3882133 , -0.9120284 , -1.0748887 ,\n", " -1.3766649 , -1.7909484 ],\n", " [-1.285872 , -0.4743617 , 0.7377885 , 2.981493 , 0.48575744,\n", " 1.0668678 , 0.3437081 ]], dtype=float32), array([[ 0.4149259 , 1.264981 , 0.7516285 , -0.15305352, -0.00465091,\n", " 0.7014937 , 0.19466783],\n", " [-0.02134901, 1.0507696 , -0.8886197 , 0.7515347 , -0.7966443 ,\n", " 0.5787362 , 0.7733859 ],\n", " [ 1.647134 , -0.8497963 , -0.14444418, 1.2312776 , -0.3230939 ,\n", " 0.4414124 , -1.8006508 ],\n", " [-1.8164082 , -1.6825722 , -0.02817002, -0.18086717, 1.2906082 ,\n", " -0.7522537 , 0.26777294],\n", " [-1.6608543 , -0.37471688, -0.6295105 , -0.06997264, -0.06349886,\n", " 0.7658272 , 1.2940828 ],\n", " [ 0.5788608 , 1.2925044 , -1.339485 , 0.22208439, -1.1661342 ,\n", " -0.09087191, -0.7201841 ],\n", " [ 0.11823197, 1.063021 , -1.1680465 , -2.0218825 , -0.33226427,\n", " -0.3082442 , -0.06889188]], dtype=float32)] \n", "bias :\n", " [array([-0.36710125, -1.6293224 , -0.18354672, -0.74396163, -0.6626378 ,\n", " 1.0037634 , -0.9549012 ], dtype=float32), array([ 1.1447405 , -0.07336594, -0.9200298 , 1.5647073 , -0.8275052 ,\n", " 1.3240731 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dtype=float32)]\n", "Hypothesis:\n", " [[0.5134782 0.4941251 0.5202529 0.49241754 0.51509863 0.5018111\n", " 0.46404672]\n", " [0.48390856 0.47903067 0.47018054 0.51400316 0.4951462 0.50088495\n", " 0.48260444]\n", " [0.55902106 0.5753595 0.549449 0.49124002 0.4871693 0.5229448\n", " 0.52562046]\n", " [0.45179248 0.46535695 0.4601758 0.5032642 0.49345645 0.4810583\n", " 0.5349333 ]] \n", "Correct:\n", " [[1. 1. 1. 1. 1. 1. 1.]\n", " [1. 1. 1. 1. 1. 1. 1.]\n", " [1. 1. 1. 1. 1. 1. 1.]\n", " [1. 1. 1. 1. 1. 1. 1.]] \n", "Accuracy: 0.5\n", "step : 4500 \n", "cost : 0.66664416 \n", "Weight :\n", " [array([[ 1.3965714 , -0.13071877, 2.1004772 , -0.45781377, 1.6268282 ,\n", " 1.3971139 , 0.6228839 ],\n", " [-2.7030072 , 0.69744426, 0.87329656, -0.47606483, -0.01890871,\n", " 1.1504341 , 0.3608926 ]], dtype=float32), array([[ 1.8393847 , -0.30580384, -0.3318641 , -1.1023512 , -1.0815032 ,\n", " -0.44897118, -0.8366368 ],\n", " [ 0.7871532 , -0.11285987, 2.1002178 , -0.01024135, 1.3394601 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0.4905978 0.47257823\n", " 0.5303126 ]] \n", "Correct:\n", " [[1. 1. 1. 1. 1. 1. 1.]\n", " [1. 1. 1. 1. 1. 1. 1.]\n", " [1. 1. 1. 1. 1. 1. 1.]\n", " [1. 1. 1. 1. 1. 1. 1.]] \n", "Accuracy: 0.5\n", "step : 5000 \n", "cost : 0.64581954 \n", "Weight :\n", " [array([[ 1.696527 , -0.18205191, 2.170005 , -0.48070377, 1.7089443 ,\n", " 1.5462413 , 0.80493593],\n", " [-2.933203 , 0.68729794, 1.1271838 , -0.5563286 , -0.04431127,\n", " 1.4132109 , 0.40220687]], dtype=float32), array([[ 2.027881 , -0.25184307, -0.334973 , -1.3123767 , -1.081312 ,\n", " -0.53305393, -0.8321189 ],\n", " [ 0.7963315 , -0.10846 , 2.0971045 , -0.03139375, 1.3375219 ,\n", " -0.62265164, 1.0884238 ],\n", " [-0.25438708, 0.476532 , 0.03608282, -1.5966275 , -0.59999496,\n", " 0.31430992, -2.0969977 ],\n", " [-0.2865024 , -1.2770219 , 2.0063448 , 0.51315904, 1.1339897 ,\n", " -0.78251505, 0.22716928],\n", " [-1.9645033 , 0.37040877, -1.2412279 , -0.09312471, 0.9007229 ,\n", " -1.329035 , -0.06616639],\n", " [ 0.7561709 , 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0.43770245 0.44859636 0.3981269\n", " 0.48515248]] \n", "Correct:\n", " [[1. 1. 1. 1. 1. 1. 1.]\n", " [1. 1. 1. 1. 1. 1. 1.]\n", " [1. 1. 1. 1. 1. 1. 1.]\n", " [1. 1. 1. 1. 1. 1. 1.]] \n", "Accuracy: 0.5\n", "step : 6500 \n", "cost : 0.3765629 \n", "Weight :\n", " [array([[ 3.314224 , -0.36174715, 2.65059 , -0.6805376 , 2.2145417 ,\n", " 2.5983386 , 1.9750103 ],\n", " [-4.151422 , 0.7047174 , 2.4384532 , -1.0239779 , -0.45867044,\n", " 2.9252944 , 0.5399653 ]], dtype=float32), array([[ 3.1870563 , 0.14865606, -0.3606245 , -2.5629492 , -1.4379995 ,\n", " -1.0215521 , -0.82075745],\n", " [ 0.8402061 , -0.10693803, 2.0783408 , -0.14530373, 1.3464887 ,\n", " -0.66657573, 1.0993924 ],\n", " [ 0.29116508, 0.7665576 , -0.01728174, -2.322715 , -0.72617227,\n", " -0.00661892, -2.0706832 ],\n", " [-0.53250104, -1.4534223 , 2.0283122 , 0.70097846, 1.3239446 ,\n", " -0.6464629 , 0.2591668 ],\n", " [-2.623448 , 0.26631692, -1.1712121 , 0.58793944, 1.2596596 ,\n", " -1.0215096 , -0.03107809],\n", " 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, -1.1970096 , -1.56273 , -1.1633922 , -1.2419182 ,\n", " -0.25237536, 0.19480588],\n", " [ 0.0073885 , 0.74340403, -1.9346597 , -0.5331311 , -1.9451858 ,\n", " -0.8017257 , -1.4253465 ],\n", " [ 0.07027779, 1.02159 , -1.2212934 , -2.0773213 , -0.39668417,\n", " -0.36168185, -0.11766872]], dtype=float32)] \n", "bias :\n", " [array([-2.358925 , -1.6087583 , -0.6632088 , -0.39168718, 0.297989 ,\n", " -1.0723662 , -2.4295812 ], dtype=float32), array([ 0.2604916 , -0.61631626, -0.9104067 , 2.0145888 , -0.09401182,\n", " 1.7432829 , -2.2966588 ], dtype=float32), array([ 0.92331547, -0.7597561 , 1.1115121 , -0.454541 , 0.56174505,\n", " -0.53426176, -0.5147437 ], dtype=float32)]\n", "Hypothesis:\n", " [[0.0762282 0.05613703 0.09429953 0.09656656 0.12875524 0.0910435\n", " 0.08686867]\n", " [0.8869704 0.88823926 0.8396491 0.8339577 0.7841859 0.85059655\n", " 0.80202514]\n", " [0.90211535 0.90780044 0.89855397 0.8587712 0.85654294 0.8833585\n", " 0.87161046]\n", " [0.1074923 0.1298475 0.13827115 0.18224943 0.19878489 0.15128288\n", " 0.21711141]] \n", "Correct:\n", " [[0. 0. 0. 0. 0. 0. 0.]\n", " [1. 1. 1. 1. 1. 1. 1.]\n", " [1. 1. 1. 1. 1. 1. 1.]\n", " [0. 0. 0. 0. 0. 0. 1.]] \n", "Accuracy: 0.96428573\n", "step : 8000 \n", "cost : 0.092464276 \n", "Weight :\n", " [array([[ 4.3436294 , -0.4796036 , 3.1574438 , -0.9664433 , 3.1824467 ,\n", " 3.579034 , 2.894377 ],\n", " [-5.197512 , 0.74384224, 3.2758143 , -1.4360069 , -1.4618231 ,\n", " 4.0490294 , 1.180014 ]], dtype=float32), array([[ 4.348173 , 0.57857233, -0.40851927, -3.724979 , -2.088753 ,\n", " -1.5403913 , -0.81727624],\n", " [ 0.8838709 , -0.11873764, 2.0556157 , -0.22725435, 1.3289146 ,\n", " -0.72485983, 1.1113158 ],\n", " [ 0.88560474, 0.96472156, -0.10355926, -2.759402 , -0.9410668 ,\n", " -0.26471183, -2.0473905 ],\n", " [-0.79840493, -1.6107352 , 2.0676255 , 0.74331427, 1.4043633 ,\n", " -0.5503744 , 0.29185438],\n", " [-3.5750396 , -0.08332489, -1.0374537 , 1.6043427 , 1.8339425 ,\n", " -0.3343238 , 0.01623518],\n", " [ 2.1288116 , 2.0485673 , -1.5837585 , -2.5303762 , -1.4936651 ,\n", " -2.1632671 , -1.7329462 ],\n", " [-2.5060048 , -0.7148367 , 0.8488632 , 4.427037 , 1.2282469 ,\n", " 1.8278477 , 0.38026986]], dtype=float32), array([[ 2.1994607 , 3.004768 , 2.823842 , 2.2196257 , 2.6270807 ,\n", " 2.744712 , 2.645919 ],\n", " [ 0.8468787 , 1.6922351 , 0.12623382, 1.6732539 , 0.40342546,\n", " 1.4011894 , 1.4618933 ],\n", " [ 1.3581042 , -1.0789782 , -0.43939126, 0.8816281 , -0.7109865 ,\n", " 0.102134 , -2.0498989 ],\n", " [-3.7522197 , -3.5906918 , -2.1808677 , -2.677832 , -1.4041886 ,\n", " -3.0640693 , -2.267983 ],\n", " [-2.5946746 , -1.2904676 , -1.6803228 , -1.3038893 , -1.4090859 ,\n", " -0.37689438, 0.04294967],\n", " [-0.05624869, 0.67806 , -2.012225 , -0.6311184 , -2.0578563 ,\n", " -0.8876557 , -1.5288048 ],\n", " [ 0.06618766, 1.0186019 , -1.2262753 , -2.0824068 , -0.40342185,\n", " -0.36652324, -0.12220052]], dtype=float32)] \n", "bias :\n", " [array([-2.4374266 , -1.6079239 , -0.7236121 , -0.38879478, 0.455931 ,\n", " -1.162537 , -2.7776985 ], dtype=float32), array([ 0.17790787, -0.68818635, -0.9064174 , 2.0461373 , -0.05573883,\n", " 1.7794006 , -2.279158 ], dtype=float32), array([ 0.9654255 , -0.7303016 , 1.1568851 , -0.4077257 , 0.61240983,\n", " -0.49535564, -0.47604987], dtype=float32)]\n", "Hypothesis:\n", " [[0.04924548 0.03510374 0.0599055 0.05883437 0.07860321 0.05689174\n", " 0.0526492 ]\n", " [0.924604 0.92306006 0.89336324 0.887625 0.8563331 0.89814603\n", " 0.8635917 ]\n", " [0.9336392 0.936069 0.9326779 0.9052776 0.90707326 0.9210594\n", " 0.91411257]\n", " [0.07206059 0.09043986 0.09219936 0.12423027 0.13296783 0.10394654\n", " 0.14899147]] \n", "Correct:\n", " [[0. 0. 0. 0. 0. 0. 0.]\n", " [1. 1. 1. 1. 1. 1. 1.]\n", " [1. 1. 1. 1. 1. 1. 1.]\n", " [0. 0. 0. 0. 0. 0. 0.]] \n", "Accuracy: 1.0\n", "step : 8500 \n", "cost : 0.06512736 \n", "Weight :\n", " [array([[ 4.473006 , -0.49337253, 3.2355552 , -1.019607 , 3.380333 ,\n", " 3.7147334 , 3.0203135 ],\n", " [-5.3499274 , 0.75023675, 3.390059 , -1.5013485 , -1.6691927 ,\n", " 4.197186 , 1.3601213 ]], dtype=float32), array([[ 4.530087 , 0.6645632 , -0.41648918, -3.8852475 , -2.220476 ,\n", " -1.639146 , -0.8170016 ],\n", " [ 0.89243555, -0.12186106, 2.0506089 , -0.24366868, 1.3216693 ,\n", " -0.7405215 , 1.1131506 ],\n", " [ 0.9714162 , 0.9806989 , -0.11982674, -2.825697 , -0.98135924,\n", " -0.3160929 , -2.0441172 ],\n", " [-0.82848287, -1.631027 , 2.0725946 , 0.74766135, 1.4065287 ,\n", " -0.5420549 , 0.29689416],\n", " [-3.7563016 , -0.19010022, -1.0093966 , 1.7906871 , 1.9648283 ,\n", " -0.17987067, 0.02515302],\n", " [ 2.2443001 , 2.0793865 , -1.6040195 , -2.6196349 , -1.5503206 ,\n", " -2.232899 , -1.7309302 ],\n", " [-2.6413665 , -0.7918232 , 0.86537975, 4.5958223 , 1.3521925 ,\n", " 1.9563177 , 0.38187245]], dtype=float32), array([[ 2.3557835 , 3.1626163 , 3.012973 , 2.447041 , 2.8814075 ,\n", " 2.9436061 , 2.8902578 ],\n", " [ 0.9162476 , 1.7458464 , 0.20563552, 1.7574205 , 0.5025984 ,\n", " 1.4777222 , 1.5383215 ],\n", " [ 1.3477311 , -1.084588 , -0.45040852, 0.8712065 , -0.7252181 ,\n", " 0.0913503 , -2.056027 ],\n", " [-3.8804276 , -3.7246382 , -2.3404171 , -2.8697097 , -1.6243262 ,\n", " -3.2341588 , -2.479559 ],\n", " [-2.6680362 , -1.3650764 , -1.7720495 , -1.411842 , -1.5357616 ,\n", " -0.4739307 , -0.07490217],\n", " [-0.10788679, 0.6244394 , -2.0748973 , -0.70823866, -2.1456916 ,\n", " -0.9562994 , -1.6114396 ],\n", " [ 0.063191 , 1.0164745 , -1.2298577 , -2.0859017 , -0.40804884,\n", " -0.36993566, -0.12529238]], dtype=float32)] \n", "bias :\n", " [array([-2.487381 , -1.6070894 , -0.7640991 , -0.39350766, 0.56149966,\n", " -1.219423 , -3.0431209 ], dtype=float32), array([ 0.1285153 , -0.7441375 , -0.9052855 , 2.0636382 , -0.03555185,\n", " 1.7967334 , -2.2669473 ], dtype=float32), array([ 0.9978326 , -0.7043508 , 1.1908585 , -0.36819237, 0.65194046,\n", " -0.46245512, -0.44133043], dtype=float32)]\n", "Hypothesis:\n", " [[0.0351226 0.0244998 0.04183713 0.03950381 0.05250424 0.03909135\n", " 0.03505883]\n", " [0.9451871 0.9424704 0.92382044 0.91895676 0.8988768 0.9255488\n", " 0.90099233]\n", " [0.9513289 0.952143 0.951821 0.9320153 0.9354292 0.9425568\n", " 0.93868774]\n", " [0.05220601 0.0670501 0.06598595 0.08912078 0.09365451 0.0757682\n", " 0.10702357]] \n", "Correct:\n", " [[0. 0. 0. 0. 0. 0. 0.]\n", " [1. 1. 1. 1. 1. 1. 1.]\n", " [1. 1. 1. 1. 1. 1. 1.]\n", " [0. 0. 0. 0. 0. 0. 0.]] \n", "Accuracy: 1.0\n", "step : 9000 \n", "cost : 0.048985116 \n", "Weight :\n", " [array([[ 4.563725 , -0.501935 , 3.2922244 , -1.0603005 , 3.5299892 ,\n", " 3.8112295 , 3.1118853 ],\n", " [-5.4584813 , 0.75540453, 3.4718258 , -1.5495132 , -1.8256904 ,\n", " 4.302113 , 1.5039808 ]], dtype=float32), array([[ 4.661333 , 0.73624206, -0.42230132, -3.9956803 , -2.3237205 ,\n", " -1.7151251 , -0.8168268 ],\n", " [ 0.89892447, -0.12469868, 2.046676 , -0.25655973, 1.3151416 ,\n", " -0.75378346, 1.1145108 ],\n", " [ 1.032942 , 0.9870261 , -0.13216111, -2.875384 , -1.0144944 ,\n", " -0.35876837, -2.0417395 ],\n", " [-0.84877914, -1.6439965 , 2.076024 , 0.75190866, 1.4068627 ,\n", " -0.5365215 , 0.30061117],\n", " [-3.8910422 , -0.28491 , -0.9875658 , 1.9303132 , 2.0687675 ,\n", " -0.05817122, 0.03202373],\n", " [ 2.3266366 , 2.0969713 , -1.619225 , -2.6857426 , -1.5958216 ,\n", " -2.289316 , -1.7294908 ],\n", " [-2.7459197 , -0.8672343 , 0.8784858 , 4.7214646 , 1.4543657 ,\n", " 2.0592191 , 0.38303444]], dtype=float32), array([[ 2.4797308 , 3.2907393 , 3.1597002 , 2.6245086 , 3.0747716 ,\n", " 3.101114 , 3.0809398 ],\n", " [ 0.97250724, 1.7922485 , 0.26828042, 1.8261794 , 0.57988596,\n", " 1.5406827 , 1.6020179 ],\n", " [ 1.3398657 , -1.0884981 , -0.4586717 , 0.8641329 , -0.7351327 ,\n", " 0.08378313, -2.0598917 ],\n", " [-3.980943 , -3.830452 , -2.463656 , -3.0157723 , -1.789542 ,\n", " -3.3657784 , -2.640832 ],\n", " [-2.727995 , -1.4263241 , -1.8460851 , -1.4971555 , -1.6347338 ,\n", " -0.55185664, -0.16841166],\n", " [-0.1510636 , 0.57958907, -2.1269848 , -0.77035946, -2.2160394 ,\n", " -1.0125458 , -1.6784776 ],\n", " [ 0.06085873, 1.0148536 , -1.2325921 , -2.0884645 , -0.41142777,\n", " -0.37249178, -0.12753929]], dtype=float32)] \n", "bias :\n", " [array([-2.5219522 , -1.6062398 , -0.7931216 , -0.40064114, 0.63730866,\n", " -1.2587374 , -3.2469916 ], dtype=float32), array([ 0.09656407, -0.7895534 , -0.9052514 , 2.0740492 , -0.02474749,\n", " 1.8048419 , -2.2578938 ], dtype=float32), array([ 1.0240115 , -0.6806457 , 1.2170087 , -0.33429918, 0.68317235,\n", " -0.43404627, -0.41072112], dtype=float32)]\n", "Hypothesis:\n", " [[0.02687904 0.01848182 0.03136131 0.02869377 0.03785285 0.02892128\n", " 0.0252457 ]\n", " [0.9575282 0.95442647 0.9421765 0.93821216 0.92465055 0.9424453\n", " 0.9244598 ]\n", " [0.9620708 0.96209455 0.96328676 0.94824207 0.9521174 0.9556546\n", " 0.95357895]\n", " [0.04017571 0.05224267 0.05019137 0.06716719 0.06970772 0.05809212\n", " 0.080607 ]] \n", "Correct:\n", " [[0. 0. 0. 0. 0. 0. 0.]\n", " [1. 1. 1. 1. 1. 1. 1.]\n", " [1. 1. 1. 1. 1. 1. 1.]\n", " [0. 0. 0. 0. 0. 0. 0.]] \n", "Accuracy: 1.0\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "step : 9500 \n", "cost : 0.03866991 \n", "Weight :\n", " [array([[ 4.63152 , -0.50752497, 3.335587 , -1.0928184 , 3.6470993 ,\n", " 3.8840423 , 3.1824121 ],\n", " [-5.540094 , 0.7597736 , 3.5337048 , -1.5867559 , -1.9479879 ,\n", " 4.3809314 , 1.6205211 ]], dtype=float32), array([[ 4.760727 , 0.79843557, -0.42676044, -4.076307 , -2.4071338 ,\n", " -1.775692 , -0.8167027 ],\n", " [ 0.904011 , -0.12726317, 2.043514 , -0.26692447, 1.3093715 ,\n", " -0.76503795, 1.115573 ],\n", " [ 1.0797167 , 0.98814315, -0.14188392, -2.9144228 , -1.0426313 ,\n", " -0.3949265 , -2.0399125 ],\n", " [-0.8636228 , -1.6524043 , 2.0785658 , 0.7559583 , 1.4066422 ,\n", " -0.5323362 , 0.3034982 ],\n", " [-3.9949362 , -0.36956933, -0.9700634 , 2.0387375 , 2.1532032 ,\n", " 0.04033 , 0.03752951],\n", " [ 2.3888502 , 2.1070058 , -1.6311231 , -2.7371356 , -1.6337125 ,\n", " -2.336275 , -1.7283993 ],\n", " [-2.8287876 , -0.93971926, 0.88913035, 4.818363 , 1.5395883 ,\n", " 2.1433933 , 0.3839294 ]], dtype=float32), array([[ 2.5816495 , 3.398008 , 3.2778206 , 2.7676785 , 3.2273095 ,\n", " 3.229938 , 3.2343822 ],\n", " [ 1.0200694 , 1.8337398 , 0.3198638 , 1.8845598 , 0.64282906,\n", " 1.5945001 , 1.6568401 ],\n", " [ 1.3335674 , -1.0914171 , -0.4652415 , 0.8589524 , -0.7425744 ,\n", " 0.07809984, -2.0625675 ],\n", " [-4.0627894 , -3.9168 , -2.562662 , -3.130999 , -1.9186502 ,\n", " -3.4712543 , -2.7679517 ],\n", " [-2.7784765 , -1.4778804 , -1.9077 , -1.5665662 , -1.7146702 ,\n", " -0.6162304 , -0.24457544],\n", " [-0.1880697 , 0.5413238 , -2.17135 , -0.8216448 , -2.274013 ,\n", " -1.0597469 , -1.7340164 ],\n", " [ 0.05895874, 1.0135535 , -1.2347786 , -2.0904481 , -0.41403157,\n", " -0.37450537, -0.12926477]], dtype=float32)] \n", "bias :\n", " [array([-2.5475314 , -1.6054053 , -0.81518686, -0.40842256, 0.6952545 ,\n", " -1.2878875 , -3.4080985 ], dtype=float32), array([ 0.07438785, -0.82738495, -0.9056396 , 2.0806692 , -0.01896052,\n", " 1.8083661 , -2.2508273 ], dtype=float32), array([ 1.0460206 , -0.65868294, 1.2379193 , -0.3046292 , 0.70861137,\n", " -0.4089685 , -0.38355425], dtype=float32)]\n", "Hypothesis:\n", " [[0.02160963 0.01472223 0.02473211 0.02210277 0.02891144 0.02259409\n", " 0.01929116]\n", " [0.965569 0.962435 0.95404035 0.95082897 0.9411837 0.95361906\n", " 0.9399942 ]\n", " [0.96912056 0.9687587 0.970675 0.9587775 0.96263945 0.9642335\n", " 0.9632072 ]\n", " [0.0322898 0.04221854 0.03997308 0.05265939 0.05429527 0.04628602\n", " 0.06313187]] \n", "Correct:\n", " [[0. 0. 0. 0. 0. 0. 0.]\n", " [1. 1. 1. 1. 1. 1. 1.]\n", " [1. 1. 1. 1. 1. 1. 1.]\n", " [0. 0. 0. 0. 0. 0. 0.]] \n", "Accuracy: 1.0\n", "step : 10000 \n", "cost : 0.03163675 \n", "Weight :\n", " [array([[ 4.6846833, -0.5113278, 3.3701909, -1.1197423, 3.7416697,\n", " 3.9415472, 3.2390163],\n", " [-5.6041527, 0.7635643, 3.5825908, -1.6167018, -2.0467224,\n", " 4.4428587, 1.7169263]], dtype=float32), array([[ 4.839032 , 0.8539025 , -0.43032357, -4.1379833 , -2.4763014 ,\n", " -1.8254635 , -0.8166133 ],\n", " [ 0.9081191 , -0.12951472, 2.0409055 , -0.27544746, 1.3042855 ,\n", " -0.7746815 , 1.1164372 ],\n", " [ 1.1168997 , 0.9867177 , -0.14981036, -2.946207 , -1.0670437 ,\n", " -0.42609933, -2.0384545 ],\n", " [-0.87515926, -1.65785 , 2.0805595 , 0.75977665, 1.4063442 ,\n", " -0.5288781 , 0.30583343],\n", " [-4.07779 , -0.44556135, -0.955638 , 2.1256719 , 2.2233863 ,\n", " 0.12210748, 0.04208824],\n", " [ 2.4380243 , 2.1129024 , -1.6407702 , -2.7786117 , -1.6660872 ,\n", " -2.3762426 , -1.7275335 ],\n", " [-2.8961787 , -1.0086212 , 0.897972 , 4.895509 , 1.6117839 ,\n", " 2.2137542 , 0.38464844]], dtype=float32), array([[ 2.6678762 , 3.4899764 , 3.3758876 , 2.886501 , 3.3516302 ,\n", " 3.3381517 , 3.3612952 ],\n", " [ 1.0615056 , 1.8715713 , 0.36379915, 1.9355407 , 0.6959325 ,\n", " 1.6417819 , 1.705189 ],\n", " [ 1.3283284 , -1.0937145 , -0.47067314, 0.8549355 , -0.7484627 ,\n", " 0.07360963, -2.0645642 ],\n", " [-4.1314063 , -3.9891138 , -2.6447258 , -3.2246923 , -2.02305 ,\n", " -3.5582368 , -2.8711436 ],\n", " [-2.8219645 , -1.5221698 , -1.9602479 , -1.6244583 , -1.7810847 ,\n", " -0.6706614 , -0.30808473],\n", " [-0.22040279, 0.5081144 , -2.2098918 , -0.8649086 , -2.322968 ,\n", " -1.1001583 , -1.7809477 ],\n", " [ 0.05735826, 1.0124702 , -1.23659 , -2.0920522 , -0.41612414,\n", " -0.3761543 , -0.13064885]], dtype=float32)] \n", "bias :\n", " [array([-2.5674498 , -1.6046046 , -0.83278424, -0.4161905 , 0.74169105,\n", " -1.3107039 , -3.539169 ], dtype=float32), array([ 0.05808429, -0.85923606, -0.906181 , 2.085151 , -0.01596162,\n", " 1.8095319 , -2.2450848 ], dtype=float32), array([ 1.0650831 , -0.6382021 , 1.255226 , -0.2782059 , 0.72995204,\n", " -0.38645583, -0.3591867 ], dtype=float32)]\n", "Hypothesis:\n", " [[0.01799995 0.01219648 0.02023846 0.0177888 0.02305454 0.01837891\n", " 0.0154143 ]\n", " [0.97116256 0.968151 0.9621829 0.95957744 0.9523983 0.9614513\n", " 0.9508137 ]\n", " [0.97404873 0.9735073 0.97574157 0.9660389 0.96970415 0.9702073\n", " 0.9698117 ]\n", " [0.02678439 0.03506058 0.03294206 0.04257685 0.04381442 0.03797334\n", " 0.05100262]] \n", "Correct:\n", " [[0. 0. 0. 0. 0. 0. 0.]\n", " [1. 1. 1. 1. 1. 1. 1.]\n", " [1. 1. 1. 1. 1. 1. 1.]\n", " [0. 0. 0. 0. 0. 0. 0.]] \n", "Accuracy: 1.0\n" ] } ], "source": [ "#XOR 게이트\n", "x_data = np.array([[0, 0], [0, 1], [1, 0], [1, 1]], dtype=np.float32)\n", "y_data = np.array([[0], [1], [1], [0]], dtype=np.float32)\n", "X = tf.placeholder(tf.float32)\n", "Y = tf.placeholder(tf.float32)\n", "W1 = tf.Variable(tf.random_normal([2, 7]), name='weight1')\n", "b1 = tf.Variable(tf.random_normal([7]), name='bias1')\n", "layer1 = tf.sigmoid(tf.matmul(X, W1) + b1)\n", "\n", "W2 = tf.Variable(tf.random_normal([7, 7]), name='weight2')\n", "b2 = tf.Variable(tf.random_normal([7]), name='bias2')\n", "layer2 = tf.sigmoid(tf.matmul(layer1, W2) + b2)\n", "\n", "W3 = tf.Variable(tf.random_normal([7, 7]), name='weight3')\n", "b3 = tf.Variable(tf.random_normal([7]), name='bias3')\n", "hypothesis = tf.sigmoid(tf.matmul(layer2, W3) + b3)\n", "\n", "# cost/loss function\n", "cost = -tf.reduce_mean(Y * tf.log(hypothesis) + (1 - Y) * tf.log(1 - hypothesis))\n", "train = tf.train.GradientDescentOptimizer(learning_rate=0.1).minimize(cost)\n", "# Accuracy computation\n", "# True if hypothesis>0.2 else False\n", "predicted = tf.cast(hypothesis > 0.2, dtype=tf.float32)\n", "accuracy = tf.reduce_mean(tf.cast(tf.equal(predicted, Y), dtype=tf.float32))\n", "# Launch graph\n", "with tf.Session() as sess:\n", " sess.run(tf.global_variables_initializer())\n", " for step in range(10001):\n", " sess.run(train, feed_dict={X: x_data, Y: y_data})\n", " if step % 500 == 0:\n", " print(\"step :\",step, \"\\ncost :\", sess.run(cost, feed_dict={X: x_data, Y: y_data}), \n", " \"\\nWeight :\\n\", sess.run([W1, W2, W3]), \"\\nbias :\\n\", sess.run([b1, b2, b3]))\n", " h, c, a = sess.run([hypothesis, predicted, accuracy], feed_dict={X: x_data, Y: y_data})\n", " print(\"Hypothesis:\\n\", h, \"\\nCorrect:\\n \", c, \"\\nAccuracy: \", a)\n" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.3" } }, "nbformat": 4, "nbformat_minor": 2 }