TFJS 模型仅预测二元分类任务的相同值

Posted 2023-03-12

【中文标题】TFJS 模型仅预测二元分类任务的相同值

【英文标题】：TFJS model only predicting the same value for binary classification task

【发布时间】：2021-04-09 10:21:15

【问题描述】：

我有一个大小为 [299,13] 的数据集（包含数据和标签），并且模型不断输出/预测相同的值。这是一个二元分类任务。如何让我的模型预测不总是相同的值？

这是代码（带有一些虚拟数据）：

//X is the data and y is the label

    
   var Dataset = tf.tensor([[1,0.491821360184978,9,314,0.504585169147173,542,1231,3213,1,0.267304071302649,3,0.615917680092409,0],
        [0,0.72959029133292,3,758,0.402582737085955,400,1788,4599,0,0.532702887951197,4,0.18630897965037,1],
        [1,0.198764110760428,5,787,0.65507860022684,887,192,4831,1,0.739456077544426,3,0.100068056951143,1],
        [0,0.583574833590476,5,596,0.933996451580092,631,331,811,0,0.258445986493932,7,0.811276729811182,0],
        [1,0.701499878184206,8,854,0.0326334179806069,845,470,4930,1,0.825469683527519,1,0.448086959665654,1],
        [0,0.954482878414911,2,468,0.736300149681564,557,3110,739,0,0.325783042694677,5,0.43488580142501,1],
        [1,0.384845877769,2,662,0.265402742189238,649,384,1158,1,0.484884260891815,2,0.915444292219105,0],
        [1,0.379266474923531,9,551,0.275982850450116,1022,3329,1413,1,0.237295089390298,4,0.817104709627837,1],
        [1,0.691365367558705,8,549,0.479627221800976,796,3381,495,1,0.37129382411555,9,0.332832739155564,1],
        [0,0.433042848178662,5,529,0.545178403950882,842,4768,506,0,0.386370525896832,9,0.189942077251933,0],
        [1,0.611272282663452,4,823,0.737901576655264,839,2724,1787,1,0.365032317656007,6,0.884073622694046,0],
        [0,0.0084315409129881,5,352,0.76858549557176,476,685,4796,0,0.302944943656102,1,0.849655932794213,1],
        [0,0.977380232874908,6,701,0.588833228576897,999,2897,3325,0,0.418024491281536,2,0.631872118440871,1],
        [1,0.419601058571829,10,384,0.0157052616592944,1009,4438,113,1,0.909015627566542,1,0.0297684897733232,0],
        [0,0.739471449044276,4,836,0.0430176780439737,1030,1456,3932,0,0.331426481315121,6,0.734008754824423,0],
        [1,0.00209807072438295,4,352,0.499622407429238,418,1912,4452,1,0.727130871883893,8,0.157427964683612,0],
        [1,0.956533819923862,10,681,0.196708599930969,829,4562,1718,1,0.233193195569506,7,0.60582783922237,0],
        [1,0.504637155233183,8,809,0.608861975627751,717,130,4194,1,0.134197560919101,6,0.375188428842507,0],
        [0,0.747363884375055,1,522,0.868234577182028,849,3529,1192,0,0.0322641640468155,5,0.185973206518818,0],
        [0,0.244142898027225,10,402,0.0280582030746698,315,3576,3882,0,0.724916254371562,8,0.062229775169706,1],
        [0,0.858414851618448,8,459,0.367325906336267,616,930,3892,0,0.177388425930446,10,0.859824526007041,1],
        [1,0.921555604905976,2,863,0.821166873626313,528,1624,1289,1,0.366243396916411,5,0.453840754701258,1],
        [1,0.171321120311715,1,524,0.177251413832862,468,1608,3123,1,0.192861821442111,8,0.122983286410146,0],
        [0,0.539946042901786,6,692,0.817780349862711,392,1053,4891,0,0.409578972921785,3,0.0453862502541893,1],
        [1,0.996848843212564,5,549,0.877740438211017,762,3046,843,1,0.888578696082088,8,0.877971306478434,1],
        [0,0.218116987741582,3,655,0.240496962520226,407,1001,1474,0,0.976212355833712,2,0.936396547703282,1]])
    function onBatchEnd(batch, logs) 
        console.log('Accuracy', logs.acc);
    
    
    var x = Dataset.slice([0, 0], [-1, 12])
    const y = Dataset.slice([0, 12], [-1, 1])
    
    const model = tf.sequential(
        layers: [
            tf.layers.dense( inputShape: [12], units: 12, activation: "sigmoid" ),
            tf.layers.dense( units: 8, activation: "relu" ),
            tf.layers.dense( units: 4, activation: "tanh" ),
            tf.layers.dense( units: 1, activation: "sigmoid" )
        ]
    )
    
    model.compile(
        optimizer: tf.train.adam(0.001),
        loss: "binaryCrossentropy",
        metrics: ["accuracy"]
    )
    
    model.fit(x, y, 
        shuffle: true,
        epochs: 100,
        //validationSplit: 0.1,
        callbacks:  onBatchEnd 
    ).then(info => 
        var predictions = model.predict(x)
        console.log('Final accuracy', info.history.acc);
        console.log("Predictions: ")
        console.log(predictions.dataSync());
    )

【问题讨论】：

299 个数据样本可能不够

谢谢，我只是希望我的模型能够预测一个不总是相同的值。你知道怎么做吗？

使用带有更多标签的更大数据集。此外，您的问题可能与编程问题有关

那个编程问题是我试图用这个问题解决的问题

这能回答你的问题吗？ Model is not learning

【参考方案1】：

模型正在使用sigmoid 激活进行二元分类。因此，最后一个单元应该是2。

tf.layers.dense( units: 2, activation: "sigmoid" )

标签张量 y 的最里面的维度大小为 1，值为 0 或 1。此张量应为 onehot 编码。

const x = Dataset.slice([0, 0], [-1, 12])
const y = Dataset.slice([0, 12], [-1, 1])

z = y.cast('int32').reshape([-1]).oneHot(2)
z.print()
console.log(z.shape) // [26, 2]
// now use z instead of y

【参考方案2】：

   tf.layers.dense( units: 1, activation: "sigmoid" )

您只有一个类预测，但对于二元分类，您需要两个目标类和 softmax 激活。请参阅 Linear Classification 的 cs321n 注释。