XOR神經網絡返回垃圾

Question

我在Tensorflow.js中的XOR“神經網絡”不斷返回垃圾預測 ，並且損失始終停留在0.25 。 我不知道我做錯了什么。 謝謝您的幫助！

 const model = tf.sequential(); model.add(tf.layers.dense({units: 2, activation: 'sigmoid', inputShape: [2]})); model.add(tf.layers.dense({units: 1, activation: 'sigmoid'})); model.compile({loss:'meanSquaredError', optimizer: 'sgd'}); tf.train.sgd(0.5); const xs = tf.tensor2d([[0,0],[0,1],[1,0],[1,1]]); const ys = tf.tensor2d([[0],[1],[1],[0]]); async function train() { for(let i = 0; i < 200; i++){ const history = await model.fit(xs, ys, {epochs: 20, shuffle: true}); console.log("loss: " + history.history.loss[19] + " on " + i + ". iteration."); } } train().then(() => { console.log("trained with " + tf.memory().numTensors + "tensors"); model.predict(xs).print(); }); 

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Answer 1

我已經更改了優化程序，並且可以預期。

    [[0.0156993],
     [0.985333 ],
     [0.9862437],
     [0.0150503]]

 const model = tf.sequential(); model.add(tf.layers.dense({units: 2, activation: 'sigmoid', inputShape: [2]})); model.add(tf.layers.dense({units: 1, activation: 'sigmoid'})); const optimizer = tf.train.adam(0.01); model.compile({loss:'meanSquaredError', optimizer: optimizer }); const xs = tf.tensor2d([[0,0],[0,1],[1,0],[1,1]]); const ys = tf.tensor2d([[0],[1],[1],[0]]); async function train() { for(let i = 0; i < 200; i++){ const history = await model.fit(xs, ys, {epochs: 20, shuffle: true}); console.log("loss: " + history.history.loss[19] + " on " + i + ". iteration."); } } train().then(() => { console.log("trained with " + tf.memory().numTensors + "tensors"); model.predict(xs).print(); }); 

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