我的模型不学习。它应该在最后进行softmax计算。我希望得到一个分类结果（退出或不退出）。模型应该预测客户是否会退出。我提供了退出列作为标签，并有196个输入特征。

我的可视化工具显示完全没有学习。但我并不确定，可视化工具如何获取模型是否学习的信息。我对JavaScript很新手，任何帮助都将不胜感激。

ngOnInit() {  this.train();}async train(): Promise<any> {  const csvUrl = '/assets/little.csv';  const csvDataset = tf.data.csv(    csvUrl,    {      columnConfigs: {        quit: {          isLabel: true        }      },      delimiter:','    });  const numOfFeatures = (await csvDataset.columnNames()).length -1;        console.log(numOfFeatures);  const flattenedDataset =  csvDataset  .map(({xs, ys}: any) =>    {      // Convert xs(features) and ys(labels) from object form (keyed by      // column name) to array form.      return {xs:Object.values(xs), ys:Object.values(ys)};    }).batch(10);      console.log(flattenedDataset.toArray());        const model = tf.sequential({    layers: [      tf.layers.dense({inputShape: [196], units: 100, activation: 'relu'}),      tf.layers.dense({units: 100, activation: 'relu'}),      tf.layers.dense({units: 100, activation: 'relu'}),              tf.layers.dense({units: 1, activation: 'softmax'}),            ]  });   await trainModel(model, flattenedDataset);  const surface = { name: 'Model Summary', tab: 'Model Inspection'};  tfvis.show.modelSummary(surface, model);      console.log('Done Training');}async function trainModel(model, flattenedDataset) {  // Prepare the model for training.    model.compile({    optimizer: tf.train.adam(),    loss: tf.losses.sigmoidCrossEntropy,    metrics: ['accuracy']  });  const batchSize = 32;  const epochs = 50;  return await model.fitDataset(flattenedDataset, {    batchSize,    epochs,    shuffle: true,    callbacks: tfvis.show.fitCallbacks(      { name: 'Training Performance' },      ['loss'],      { height: 200, callbacks: ['onEpochEnd'] }    )  });}  

回答：

最后一层的单元数量应等于类别的数量。这里有两个类别quit和no-quit。另外，你的标签应该进行独热编码。关于模型为何不学习的更普遍的答案可以在这里找到这里。