tensorflow.js

여러개의 변수

수업소개

독립변수와 종속변수의 숫자가 여러개일 때는 어떻게 학습을 시켜야 하는 것일까요? 

 

 

 

강의1

 

 

 

강의2

 

 

 

강의3

 

10.3.html

<!DOCTYPE html>
<html>

<head>
    <title>TensorFlow.js Tutorial - boston housing </title>

    <!-- Import TensorFlow.js -->
    <script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs@1.0.0/dist/tf.min.js"></script>
    <script src="10.3.js"></script>
</head>

<body>
    <script>
        /*
        var 보스톤_원인 = [
            [0.00632,18,2.31,0,0.538,6.575,65.2,4.09,1,296,15.3,396.9,4.98],
            [0.02731,0,7.07,0,0.469,6.421,78.9,4.9671,2,242,17.8,396.9,9.14]
        ];
        var 보스톤_결과 = [
            [24], 
            [21.6]
        ];
        */
    
        // 1. 과거의 데이터를 준비합니다. 
        var 원인 = tf.tensor(보스톤_원인);
        var 결과 = tf.tensor(보스톤_결과);

        // 2. 모델의 모양을 만듭니다. 
        var X = tf.input({ shape: [13] });
        var Y = tf.layers.dense({ units: 1 }).apply(X);
        var model = tf.model({ inputs: X, outputs: Y });
        var compileParam = { optimizer: tf.train.adam(), loss: tf.losses.meanSquaredError }
        model.compile(compileParam);

        // 3. 데이터로 모델을 학습시킵니다. 
//         var fitParam = {epochs: 100}
        var fitParam = { 
          epochs: 100, 
          callbacks:{
            onEpochEnd:
              function(epoch, logs){
                console.log('epoch', epoch, logs, 'RMSE=>', Math.sqrt(logs.loss));
              }
          }
        } // loss 추가 예제
        model.fit(원인, 결과, fitParam).then(function (result) {
            
            // 4. 모델을 이용합니다. 
            // 4.1 기존의 데이터를 이용
            var 예측한결과 = model.predict(원인);
            예측한결과.print();

        });  

        // 4.2 새로운 데이터를 이용
        // var 다음주온도 = [15,16,17,18,19]
        // var 다음주원인 = tf.tensor(다음주온도);
        // var 다음주결과 = model.predict(다음주원인);
        // 다음주결과.print();
    </script>
</body>

</html>

 

10.3.js

var 보스톤_원인 = [
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    [13.0751,0,18.1,0,0.58,5.713,56.7,2.8237,24,666,20.2,396.9,14.76],
    [4.34879,0,18.1,0,0.58,6.167,84,3.0334,24,666,20.2,396.9,16.29],
    [4.03841,0,18.1,0,0.532,6.229,90.7,3.0993,24,666,20.2,395.33,12.87],
    [3.56868,0,18.1,0,0.58,6.437,75,2.8965,24,666,20.2,393.37,14.36],
    [4.64689,0,18.1,0,0.614,6.98,67.6,2.5329,24,666,20.2,374.68,11.66],
    [8.05579,0,18.1,0,0.584,5.427,95.4,2.4298,24,666,20.2,352.58,18.14],
    [6.39312,0,18.1,0,0.584,6.162,97.4,2.206,24,666,20.2,302.76,24.1],
    [4.87141,0,18.1,0,0.614,6.484,93.6,2.3053,24,666,20.2,396.21,18.68],
    [15.0234,0,18.1,0,0.614,5.304,97.3,2.1007,24,666,20.2,349.48,24.91],
    [10.233,0,18.1,0,0.614,6.185,96.7,2.1705,24,666,20.2,379.7,18.03],
    [14.3337,0,18.1,0,0.614,6.229,88,1.9512,24,666,20.2,383.32,13.11],
    [5.82401,0,18.1,0,0.532,6.242,64.7,3.4242,24,666,20.2,396.9,10.74],
    [5.70818,0,18.1,0,0.532,6.75,74.9,3.3317,24,666,20.2,393.07,7.74],
    [5.73116,0,18.1,0,0.532,7.061,77,3.4106,24,666,20.2,395.28,7.01],
    [2.81838,0,18.1,0,0.532,5.762,40.3,4.0983,24,666,20.2,392.92,10.42],
    [2.37857,0,18.1,0,0.583,5.871,41.9,3.724,24,666,20.2,370.73,13.34],
    [3.67367,0,18.1,0,0.583,6.312,51.9,3.9917,24,666,20.2,388.62,10.58],
    [5.69175,0,18.1,0,0.583,6.114,79.8,3.5459,24,666,20.2,392.68,14.98],
    [4.83567,0,18.1,0,0.583,5.905,53.2,3.1523,24,666,20.2,388.22,11.45],
    [0.15086,0,27.74,0,0.609,5.454,92.7,1.8209,4,711,20.1,395.09,18.06],
    [0.18337,0,27.74,0,0.609,5.414,98.3,1.7554,4,711,20.1,344.05,23.97],
    [0.20746,0,27.74,0,0.609,5.093,98,1.8226,4,711,20.1,318.43,29.68],
    [0.10574,0,27.74,0,0.609,5.983,98.8,1.8681,4,711,20.1,390.11,18.07],
    [0.11132,0,27.74,0,0.609,5.983,83.5,2.1099,4,711,20.1,396.9,13.35],
    [0.17331,0,9.69,0,0.585,5.707,54,2.3817,6,391,19.2,396.9,12.01],
    [0.27957,0,9.69,0,0.585,5.926,42.6,2.3817,6,391,19.2,396.9,13.59],
    [0.17899,0,9.69,0,0.585,5.67,28.8,2.7986,6,391,19.2,393.29,17.6],
    [0.2896,0,9.69,0,0.585,5.39,72.9,2.7986,6,391,19.2,396.9,21.14],
    [0.26838,0,9.69,0,0.585,5.794,70.6,2.8927,6,391,19.2,396.9,14.1],
    [0.23912,0,9.69,0,0.585,6.019,65.3,2.4091,6,391,19.2,396.9,12.92],
    [0.17783,0,9.69,0,0.585,5.569,73.5,2.3999,6,391,19.2,395.77,15.1],
    [0.22438,0,9.69,0,0.585,6.027,79.7,2.4982,6,391,19.2,396.9,14.33],
    [0.06263,0,11.93,0,0.573,6.593,69.1,2.4786,1,273,21,391.99,9.67],
    [0.04527,0,11.93,0,0.573,6.12,76.7,2.2875,1,273,21,396.9,9.08],
    [0.06076,0,11.93,0,0.573,6.976,91,2.1675,1,273,21,396.9,5.64],
    [0.10959,0,11.93,0,0.573,6.794,89.3,2.3889,1,273,21,393.45,6.48],
    [0.04741,0,11.93,0,0.573,6.03,80.8,2.505,1,273,21,396.9,7.88]
    ]
    
    var 보스톤_결과 = [[24],
    [21.6],
    [34.7],
    [33.4],
    [36.2],
    [28.7],
    [22.9],
    [27.1],
    [16.5],
    [18.9],
    [15],
    [18.9],
    [21.7],
    [20.4],
    [18.2],
    [19.9],
    [23.1],
    [17.5],
    [20.2],
    [18.2],
    [13.6],
    [19.6],
    [15.2],
    [14.5],
    [15.6],
    [13.9],
    [16.6],
    [14.8],
    [18.4],
    [21],
    [12.7],
    [14.5],
    [13.2],
    [13.1],
    [13.5],
    [18.9],
    [20],
    [21],
    [24.7],
    [30.8],
    [34.9],
    [26.6],
    [25.3],
    [24.7],
    [21.2],
    [19.3],
    [20],
    [16.6],
    [14.4],
    [19.4],
    [19.7],
    [20.5],
    [25],
    [23.4],
    [18.9],
    [35.4],
    [24.7],
    [31.6],
    [23.3],
    [19.6],
    [18.7],
    [16],
    [22.2],
    [25],
    [33],
    [23.5],
    [19.4],
    [22],
    [17.4],
    [20.9],
    [24.2],
    [21.7],
    [22.8],
    [23.4],
    [24.1],
    [21.4],
    [20],
    [20.8],
    [21.2],
    [20.3],
    [28],
    [23.9],
    [24.8],
    [22.9],
    [23.9],
    [26.6],
    [22.5],
    [22.2],
    [23.6],
    [28.7],
    [22.6],
    [22],
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    [25],
    [20.6],
    [28.4],
    [21.4],
    [38.7],
    [43.8],
    [33.2],
    [27.5],
    [26.5],
    [18.6],
    [19.3],
    [20.1],
    [19.5],
    [19.5],
    [20.4],
    [19.8],
    [19.4],
    [21.7],
    [22.8],
    [18.8],
    [18.7],
    [18.5],
    [18.3],
    [21.2],
    [19.2],
    [20.4],
    [19.3],
    [22],
    [20.3],
    [20.5],
    [17.3],
    [18.8],
    [21.4],
    [15.7],
    [16.2],
    [18],
    [14.3],
    [19.2],
    [19.6],
    [23],
    [18.4],
    [15.6],
    [18.1],
    [17.4],
    [17.1],
    [13.3],
    [17.8],
    [14],
    [14.4],
    [13.4],
    [15.6],
    [11.8],
    [13.8],
    [15.6],
    [14.6],
    [17.8],
    [15.4],
    [21.5],
    [19.6],
    [15.3],
    [19.4],
    [17],
    [15.6],
    [13.1],
    [41.3],
    [24.3],
    [23.3],
    [27],
    [50],
    [50],
    [50],
    [22.7],
    [25],
    [50],
    [23.8],
    [23.8],
    [22.3],
    [17.4],
    [19.1],
    [23.1],
    [23.6],
    [22.6],
    [29.4],
    [23.2],
    [24.6],
    [29.9],
    [37.2],
    [39.8],
    [36.2],
    [37.9],
    [32.5],
    [26.4],
    [29.6],
    [50],
    [32],
    [29.8],
    [34.9],
    [37],
    [30.5],
    [36.4],
    [31.1],
    [29.1],
    [50],
    [33.3],
    [30.3],
    [34.6],
    [34.9],
    [32.9],
    [24.1],
    [42.3],
    [48.5],
    [50],
    [22.6],
    [24.4],
    [22.5],
    [24.4],
    [20],
    [21.7],
    [19.3],
    [22.4],
    [28.1],
    [23.7],
    [25],
    [23.3],
    [28.7],
    [21.5],
    [23],
    [26.7],
    [21.7],
    [27.5],
    [30.1],
    [44.8],
    [50],
    [37.6],
    [31.6],
    [46.7],
    [31.5],
    [24.3],
    [31.7],
    [41.7],
    [48.3],
    [29],
    [24],
    [25.1],
    [31.5],
    [23.7],
    [23.3],
    [22],
    [20.1],
    [22.2],
    [23.7],
    [17.6],
    [18.5],
    [24.3],
    [20.5],
    [24.5],
    [26.2],
    [24.4],
    [24.8],
    [29.6],
    [42.8],
    [21.9],
    [20.9],
    [44],
    [50],
    [36],
    [30.1],
    [33.8],
    [43.1],
    [48.8],
    [31],
    [36.5],
    [22.8],
    [30.7],
    [50],
    [43.5],
    [20.7],
    [21.1],
    [25.2],
    [24.4],
    [35.2],
    [32.4],
    [32],
    [33.2],
    [33.1],
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    [35.1],
    [45.4],
    [35.4],
    [46],
    [50],
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    [18.5],
    [20.6],
    [19],
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    [32.7],
    [16.5],
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    [31.2],
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    [26.6],
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    [24.1],
    [18.6],
    [30.1],
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    [11.3],
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    [10.5],
    [7.4],
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    [11.5],
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    [13.3],
    [16.7],
    [12],
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    [23],
    [23.7],
    [25],
    [21.8],
    [20.6],
    [21.2],
    [19.1],
    [20.6],
    [15.2],
    [7],
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    [13.6],
    [20.1],
    [21.8],
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    [23.1],
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    [22.4],
    [20.6],
    [23.9],
    [22],
    [11.9]];

 

 

 

강의4

 

10.4.html

<!DOCTYPE html>
<html>

<head>
    <title>TensorFlow.js Tutorial - boston housing</title>

    <!-- Import TensorFlow.js -->
    <script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs@1.0.0/dist/tf.min.js"></script>
    <script src="10.4.js"></script>
</head>

<body>
    <script>
    
        // 1. 과거의 데이터를 준비합니다. 
        var 원인 = tf.tensor(보스톤_원인);
        var 결과 = tf.tensor(보스톤_결과);

        // 2. 모델의 모양을 만듭니다. 
        var X = tf.input({ shape: [12] });
        var Y = tf.layers.dense({ units: 2 }).apply(X);
        var model = tf.model({ inputs: X, outputs: Y });
        var compileParam = { optimizer: tf.train.adam(), loss: tf.losses.meanSquaredError }
        model.compile(compileParam);

        // 3. 데이터로 모델을 학습시킵니다. 
//         var fitParam = {epochs: 100}
        var fitParam = { 
          epochs: 100, 
          callbacks:{
            onEpochEnd:
              function(epoch, logs){
                console.log('epoch', epoch, logs, 'RMSE=>', Math.sqrt(logs.loss));
              }
          }
        } // loss 추가 예제
        model.fit(원인, 결과, fitParam).then(function (result) {
            
            // 4. 모델을 이용합니다. 
            // 4.1 기존의 데이터를 이용
            var 예측한결과 = model.predict(원인);
            예측한결과.print();

        });  

        // 4.2 새로운 데이터를 이용
        // var 다음주온도 = [15,16,17,18,19]
        // var 다음주원인 = tf.tensor(다음주온도);
        // var 다음주결과 = model.predict(다음주원인);
        // 다음주결과.print();
    </script>
</body>

</html>


10.4.js

var 보스톤_원인 = [
  [0.00632, 18, 2.31, 0, 0.538, 6.575, 65.2, 4.09, 1, 296, 15.3, 396.9],
  [0.02731, 0, 7.07, 0, 0.469, 6.421, 78.9, 4.9671, 2, 242, 17.8, 396.9],
  [0.02729, 0, 7.07, 0, 0.469, 7.185, 61.1, 4.9671, 2, 242, 17.8, 392.83],
  [0.03237, 0, 2.18, 0, 0.458, 6.998, 45.8, 6.0622, 3, 222, 18.7, 394.63],
  [0.06905, 0, 2.18, 0, 0.458, 7.147, 54.2, 6.0622, 3, 222, 18.7, 396.9],
  [0.02985, 0, 2.18, 0, 0.458, 6.43, 58.7, 6.0622, 3, 222, 18.7, 394.12],
  [0.08829, 12.5, 7.87, 0, 0.524, 6.012, 66.6, 5.5605, 5, 311, 15.2, 395.6],
  [0.14455, 12.5, 7.87, 0, 0.524, 6.172, 96.1, 5.9505, 5, 311, 15.2, 396.9],
  [0.21124, 12.5, 7.87, 0, 0.524, 5.631, 100, 6.0821, 5, 311, 15.2, 386.63],
  [0.17004, 12.5, 7.87, 0, 0.524, 6.004, 85.9, 6.5921, 5, 311, 15.2, 386.71],
  [0.22489, 12.5, 7.87, 0, 0.524, 6.377, 94.3, 6.3467, 5, 311, 15.2, 392.52],
  [0.11747, 12.5, 7.87, 0, 0.524, 6.009, 82.9, 6.2267, 5, 311, 15.2, 396.9],
  [0.09378, 12.5, 7.87, 0, 0.524, 5.889, 39, 5.4509, 5, 311, 15.2, 390.5],
  [0.62976, 0, 8.14, 0, 0.538, 5.949, 61.8, 4.7075, 4, 307, 21, 396.9],
  [0.63796, 0, 8.14, 0, 0.538, 6.096, 84.5, 4.4619, 4, 307, 21, 380.02],
  [0.62739, 0, 8.14, 0, 0.538, 5.834, 56.5, 4.4986, 4, 307, 21, 395.62],
  [1.05393, 0, 8.14, 0, 0.538, 5.935, 29.3, 4.4986, 4, 307, 21, 386.85],
  [0.7842, 0, 8.14, 0, 0.538, 5.99, 81.7, 4.2579, 4, 307, 21, 386.75],
  [0.80271, 0, 8.14, 0, 0.538, 5.456, 36.6, 3.7965, 4, 307, 21, 288.99],
  [0.7258, 0, 8.14, 0, 0.538, 5.727, 69.5, 3.7965, 4, 307, 21, 390.95],
  [1.25179, 0, 8.14, 0, 0.538, 5.57, 98.1, 3.7979, 4, 307, 21, 376.57],
  [0.85204, 0, 8.14, 0, 0.538, 5.965, 89.2, 4.0123, 4, 307, 21, 392.53],
  [1.23247, 0, 8.14, 0, 0.538, 6.142, 91.7, 3.9769, 4, 307, 21, 396.9],
  [0.98843, 0, 8.14, 0, 0.538, 5.813, 100, 4.0952, 4, 307, 21, 394.54],
  [0.75026, 0, 8.14, 0, 0.538, 5.924, 94.1, 4.3996, 4, 307, 21, 394.33],
  [0.84054, 0, 8.14, 0, 0.538, 5.599, 85.7, 4.4546, 4, 307, 21, 303.42],
  [0.67191, 0, 8.14, 0, 0.538, 5.813, 90.3, 4.682, 4, 307, 21, 376.88],
  [0.95577, 0, 8.14, 0, 0.538, 6.047, 88.8, 4.4534, 4, 307, 21, 306.38],
  [0.77299, 0, 8.14, 0, 0.538, 6.495, 94.4, 4.4547, 4, 307, 21, 387.94],
  [1.00245, 0, 8.14, 0, 0.538, 6.674, 87.3, 4.239, 4, 307, 21, 380.23],
  [1.13081, 0, 8.14, 0, 0.538, 5.713, 94.1, 4.233, 4, 307, 21, 360.17],
  [1.35472, 0, 8.14, 0, 0.538, 6.072, 100, 4.175, 4, 307, 21, 376.73],
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  [0.37578, 0, 10.59, 1, 0.489, 5.404, 88.6, 3.665, 4, 277, 18.6, 395.24],
  [0.21719, 0, 10.59, 1, 0.489, 5.807, 53.8, 3.6526, 4, 277, 18.6, 390.94],
  [0.14052, 0, 10.59, 0, 0.489, 6.375, 32.3, 3.9454, 4, 277, 18.6, 385.81],
  [0.28955, 0, 10.59, 0, 0.489, 5.412, 9.8, 3.5875, 4, 277, 18.6, 348.93],
  [0.19802, 0, 10.59, 0, 0.489, 6.182, 42.4, 3.9454, 4, 277, 18.6, 393.63],
  [0.0456, 0, 13.89, 1, 0.55, 5.888, 56, 3.1121, 5, 276, 16.4, 392.8],
  [0.07013, 0, 13.89, 0, 0.55, 6.642, 85.1, 3.4211, 5, 276, 16.4, 392.78],
  [0.11069, 0, 13.89, 1, 0.55, 5.951, 93.8, 2.8893, 5, 276, 16.4, 396.9],
  [0.11425, 0, 13.89, 1, 0.55, 6.373, 92.4, 3.3633, 5, 276, 16.4, 393.74],
  [0.35809, 0, 6.2, 1, 0.507, 6.951, 88.5, 2.8617, 8, 307, 17.4, 391.7],
  [0.40771, 0, 6.2, 1, 0.507, 6.164, 91.3, 3.048, 8, 307, 17.4, 395.24],
  [0.62356, 0, 6.2, 1, 0.507, 6.879, 77.7, 3.2721, 8, 307, 17.4, 390.39],
  [0.6147, 0, 6.2, 0, 0.507, 6.618, 80.8, 3.2721, 8, 307, 17.4, 396.9],
  [0.31533, 0, 6.2, 0, 0.504, 8.266, 78.3, 2.8944, 8, 307, 17.4, 385.05],
  [0.52693, 0, 6.2, 0, 0.504, 8.725, 83, 2.8944, 8, 307, 17.4, 382],
  [0.38214, 0, 6.2, 0, 0.504, 8.04, 86.5, 3.2157, 8, 307, 17.4, 387.38],
  [0.41238, 0, 6.2, 0, 0.504, 7.163, 79.9, 3.2157, 8, 307, 17.4, 372.08],
  [0.29819, 0, 6.2, 0, 0.504, 7.686, 17, 3.3751, 8, 307, 17.4, 377.51],
  [0.44178, 0, 6.2, 0, 0.504, 6.552, 21.4, 3.3751, 8, 307, 17.4, 380.34],
  [0.537, 0, 6.2, 0, 0.504, 5.981, 68.1, 3.6715, 8, 307, 17.4, 378.35],
  [0.46296, 0, 6.2, 0, 0.504, 7.412, 76.9, 3.6715, 8, 307, 17.4, 376.14],
  [0.57529, 0, 6.2, 0, 0.507, 8.337, 73.3, 3.8384, 8, 307, 17.4, 385.91],
  [0.33147, 0, 6.2, 0, 0.507, 8.247, 70.4, 3.6519, 8, 307, 17.4, 378.95],
  [0.44791, 0, 6.2, 1, 0.507, 6.726, 66.5, 3.6519, 8, 307, 17.4, 360.2],
  [0.33045, 0, 6.2, 0, 0.507, 6.086, 61.5, 3.6519, 8, 307, 17.4, 376.75],
  [0.52058, 0, 6.2, 1, 0.507, 6.631, 76.5, 4.148, 8, 307, 17.4, 388.45],
  [0.51183, 0, 6.2, 0, 0.507, 7.358, 71.6, 4.148, 8, 307, 17.4, 390.07],
  [0.08244, 30, 4.93, 0, 0.428, 6.481, 18.5, 6.1899, 6, 300, 16.6, 379.41],
  [0.09252, 30, 4.93, 0, 0.428, 6.606, 42.2, 6.1899, 6, 300, 16.6, 383.78],
  [0.11329, 30, 4.93, 0, 0.428, 6.897, 54.3, 6.3361, 6, 300, 16.6, 391.25],
  [0.10612, 30, 4.93, 0, 0.428, 6.095, 65.1, 6.3361, 6, 300, 16.6, 394.62]
						

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