我有一个数据集（包含数据和标签），大小为[299,13]，但模型总是输出/预测相同的值。这是一个二分类任务。我该如何让模型预测不总是相同的值呢？
这是代码（带有一些虚拟数据）：
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]])var x = Dataset.slice([0, 0], [-1, 12]) var y = Dataset.slice([0, 12], [-1, 1]) y = y.cast('int32').reshape([-1]).oneHot(2) y.print()const model = tf.sequential({ layers: [ tf.layers.dense({ inputShape: [12], units: 12, activation: "relu6" }), tf.layers.dense({ units: 56, activation: "tanh" }), tf.layers.dense({ units: 28, activation: "tanh" }), tf.layers.dense({ units: 14, activation: "sigmoid" }), tf.layers.dense({ units: 58, activation: "tanh" }), tf.layers.dense({ units: 2, activation: "softmax" }) ] }) model.summary()model.compile({ optimizer: tf.train.adam(), loss: 'categoricalCrossentropy', metrics: ['accuracy'], });model.fit(x, y, { batchSize: 3, epochs: 10, shuffle: true }).then(h => { console.log("Training Complete") var predictions = model.predict(x) predictions.print() });回答：
有299个样本，每个样本有13个特征。这可能不足以让模型进行泛化。在你的隐藏层中你使用了tanh和sigmoid。我建议你使用relu。你还对标签进行了独热编码以使用softmax，这是可以理解的，但你可能想使用sigmoid。
如果你使用sigmoid而不进行独热编码，那么你将有机会根据你的业务问题设定某个阈值。
tf.layers.dense({ units: 1, activation: "sigmoid" })假设你为预测设定了0.5的阈值，这意味着如果你的预测值大于0.5，那么它将属于第二类。但你可以调整它，例如设为0.4，看看会发生什么。你可以通过解释AUC-ROC曲线来得出结论。
另一件事是关于特征，它们没有被正确地缩放：
[1,0.00209807072438295,4,352,0.499622407429238,418,1912,4452,1,0.727130871883893,8,0.157427964683612,0]如果它们没有在某个范围内被正确缩放，那么模型可能会对某些特征赋予比其他特征更多的重要性，或者可能会出现一些意想不到的行为。
