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  • TensorFlow多层感知器

    数据集Advertising.csv

    ,TV,radio,newspaper,sales
    1,230.1,37.8,69.2,22.1
    2,44.5,39.3,45.1,10.4
    3,17.2,45.9,69.3,9.3
    4,151.5,41.3,58.5,18.5
    5,180.8,10.8,58.4,12.9
    6,8.7,48.9,75,7.2
    7,57.5,32.8,23.5,11.8
    8,120.2,19.6,11.6,13.2
    9,8.6,2.1,1,4.8
    10,199.8,2.6,21.2,10.6
    11,66.1,5.8,24.2,8.6
    12,214.7,24,4,17.4
    13,23.8,35.1,65.9,9.2
    14,97.5,7.6,7.2,9.7
    15,204.1,32.9,46,19
    16,195.4,47.7,52.9,22.4
    17,67.8,36.6,114,12.5
    18,281.4,39.6,55.8,24.4
    19,69.2,20.5,18.3,11.3
    20,147.3,23.9,19.1,14.6
    21,218.4,27.7,53.4,18
    22,237.4,5.1,23.5,12.5
    23,13.2,15.9,49.6,5.6
    24,228.3,16.9,26.2,15.5
    25,62.3,12.6,18.3,9.7
    26,262.9,3.5,19.5,12
    27,142.9,29.3,12.6,15
    28,240.1,16.7,22.9,15.9
    29,248.8,27.1,22.9,18.9
    30,70.6,16,40.8,10.5
    31,292.9,28.3,43.2,21.4
    32,112.9,17.4,38.6,11.9
    33,97.2,1.5,30,9.6
    34,265.6,20,0.3,17.4
    35,95.7,1.4,7.4,9.5
    36,290.7,4.1,8.5,12.8
    37,266.9,43.8,5,25.4
    38,74.7,49.4,45.7,14.7
    39,43.1,26.7,35.1,10.1
    40,228,37.7,32,21.5
    41,202.5,22.3,31.6,16.6
    42,177,33.4,38.7,17.1
    43,293.6,27.7,1.8,20.7
    44,206.9,8.4,26.4,12.9
    45,25.1,25.7,43.3,8.5
    46,175.1,22.5,31.5,14.9
    47,89.7,9.9,35.7,10.6
    48,239.9,41.5,18.5,23.2
    49,227.2,15.8,49.9,14.8
    50,66.9,11.7,36.8,9.7
    51,199.8,3.1,34.6,11.4
    52,100.4,9.6,3.6,10.7
    53,216.4,41.7,39.6,22.6
    54,182.6,46.2,58.7,21.2
    55,262.7,28.8,15.9,20.2
    56,198.9,49.4,60,23.7
    57,7.3,28.1,41.4,5.5
    58,136.2,19.2,16.6,13.2
    59,210.8,49.6,37.7,23.8
    60,210.7,29.5,9.3,18.4
    61,53.5,2,21.4,8.1
    62,261.3,42.7,54.7,24.2
    63,239.3,15.5,27.3,15.7
    64,102.7,29.6,8.4,14
    65,131.1,42.8,28.9,18
    66,69,9.3,0.9,9.3
    67,31.5,24.6,2.2,9.5
    68,139.3,14.5,10.2,13.4
    69,237.4,27.5,11,18.9
    70,216.8,43.9,27.2,22.3
    71,199.1,30.6,38.7,18.3
    72,109.8,14.3,31.7,12.4
    73,26.8,33,19.3,8.8
    74,129.4,5.7,31.3,11
    75,213.4,24.6,13.1,17
    76,16.9,43.7,89.4,8.7
    77,27.5,1.6,20.7,6.9
    78,120.5,28.5,14.2,14.2
    79,5.4,29.9,9.4,5.3
    80,116,7.7,23.1,11
    81,76.4,26.7,22.3,11.8
    82,239.8,4.1,36.9,12.3
    83,75.3,20.3,32.5,11.3
    84,68.4,44.5,35.6,13.6
    85,213.5,43,33.8,21.7
    86,193.2,18.4,65.7,15.2
    87,76.3,27.5,16,12
    88,110.7,40.6,63.2,16
    89,88.3,25.5,73.4,12.9
    90,109.8,47.8,51.4,16.7
    91,134.3,4.9,9.3,11.2
    92,28.6,1.5,33,7.3
    93,217.7,33.5,59,19.4
    94,250.9,36.5,72.3,22.2
    95,107.4,14,10.9,11.5
    96,163.3,31.6,52.9,16.9
    97,197.6,3.5,5.9,11.7
    98,184.9,21,22,15.5
    99,289.7,42.3,51.2,25.4
    100,135.2,41.7,45.9,17.2
    101,222.4,4.3,49.8,11.7
    102,296.4,36.3,100.9,23.8
    103,280.2,10.1,21.4,14.8
    104,187.9,17.2,17.9,14.7
    105,238.2,34.3,5.3,20.7
    106,137.9,46.4,59,19.2
    107,25,11,29.7,7.2
    108,90.4,0.3,23.2,8.7
    109,13.1,0.4,25.6,5.3
    110,255.4,26.9,5.5,19.8
    111,225.8,8.2,56.5,13.4
    112,241.7,38,23.2,21.8
    113,175.7,15.4,2.4,14.1
    114,209.6,20.6,10.7,15.9
    115,78.2,46.8,34.5,14.6
    116,75.1,35,52.7,12.6
    117,139.2,14.3,25.6,12.2
    118,76.4,0.8,14.8,9.4
    119,125.7,36.9,79.2,15.9
    120,19.4,16,22.3,6.6
    121,141.3,26.8,46.2,15.5
    122,18.8,21.7,50.4,7
    123,224,2.4,15.6,11.6
    124,123.1,34.6,12.4,15.2
    125,229.5,32.3,74.2,19.7
    126,87.2,11.8,25.9,10.6
    127,7.8,38.9,50.6,6.6
    128,80.2,0,9.2,8.8
    129,220.3,49,3.2,24.7
    130,59.6,12,43.1,9.7
    131,0.7,39.6,8.7,1.6
    132,265.2,2.9,43,12.7
    133,8.4,27.2,2.1,5.7
    134,219.8,33.5,45.1,19.6
    135,36.9,38.6,65.6,10.8
    136,48.3,47,8.5,11.6
    137,25.6,39,9.3,9.5
    138,273.7,28.9,59.7,20.8
    139,43,25.9,20.5,9.6
    140,184.9,43.9,1.7,20.7
    141,73.4,17,12.9,10.9
    142,193.7,35.4,75.6,19.2
    143,220.5,33.2,37.9,20.1
    144,104.6,5.7,34.4,10.4
    145,96.2,14.8,38.9,11.4
    146,140.3,1.9,9,10.3
    147,240.1,7.3,8.7,13.2
    148,243.2,49,44.3,25.4
    149,38,40.3,11.9,10.9
    150,44.7,25.8,20.6,10.1
    151,280.7,13.9,37,16.1
    152,121,8.4,48.7,11.6
    153,197.6,23.3,14.2,16.6
    154,171.3,39.7,37.7,19
    155,187.8,21.1,9.5,15.6
    156,4.1,11.6,5.7,3.2
    157,93.9,43.5,50.5,15.3
    158,149.8,1.3,24.3,10.1
    159,11.7,36.9,45.2,7.3
    160,131.7,18.4,34.6,12.9
    161,172.5,18.1,30.7,14.4
    162,85.7,35.8,49.3,13.3
    163,188.4,18.1,25.6,14.9
    164,163.5,36.8,7.4,18
    165,117.2,14.7,5.4,11.9
    166,234.5,3.4,84.8,11.9
    167,17.9,37.6,21.6,8
    168,206.8,5.2,19.4,12.2
    169,215.4,23.6,57.6,17.1
    170,284.3,10.6,6.4,15
    171,50,11.6,18.4,8.4
    172,164.5,20.9,47.4,14.5
    173,19.6,20.1,17,7.6
    174,168.4,7.1,12.8,11.7
    175,222.4,3.4,13.1,11.5
    176,276.9,48.9,41.8,27
    177,248.4,30.2,20.3,20.2
    178,170.2,7.8,35.2,11.7
    179,276.7,2.3,23.7,11.8
    180,165.6,10,17.6,12.6
    181,156.6,2.6,8.3,10.5
    182,218.5,5.4,27.4,12.2
    183,56.2,5.7,29.7,8.7
    184,287.6,43,71.8,26.2
    185,253.8,21.3,30,17.6
    186,205,45.1,19.6,22.6
    187,139.5,2.1,26.6,10.3
    188,191.1,28.7,18.2,17.3
    189,286,13.9,3.7,15.9
    190,18.7,12.1,23.4,6.7
    191,39.5,41.1,5.8,10.8
    192,75.5,10.8,6,9.9
    193,17.2,4.1,31.6,5.9
    194,166.8,42,3.6,19.6
    195,149.7,35.6,6,17.3
    196,38.2,3.7,13.8,7.6
    197,94.2,4.9,8.1,9.7
    198,177,9.3,6.4,12.8
    199,283.6,42,66.2,25.5
    200,232.1,8.6,8.7,13.4
    View Code

    代码:

    import tensorflow as tf
    import pandas as pd
    data = pd.read_csv("../data/Advertising.csv")
    x = data.iloc[:,1:-1]
    y = data.iloc[:,-1]
    model = tf.keras.Sequential([tf.keras.layers.Dense(10,input_shape=(3,),activation='relu'),tf.keras.layers.Dense(1)])
    model.compile(optimizer='adam',loss='mse')
    model.fit(x,y,epochs=100)
    test = data.iloc[:10,1:-1]
    print(model.predict(test))
    print(data.iloc[:10,-1])
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  • 原文地址:https://www.cnblogs.com/xhj1074376195/p/14292053.html
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