tensorflow.js model does not learn - machine-learning

Question

My model doesn´t learn.. It is supposed to do a softmax calculation in the end. I want as a result a classification (quit or no-quit). The model should predict if the customer will quit. I am giving the quit-column as label and have 196 input-features.
My visor says there is no learning at all. But then I am not certain, how the visor will get information, if my model learns. I am very new to javascript and would appreciate any help.
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'] }
)
});
}

Answer 1

The number of units for the last layer is the number of categories. There are two categories quit and no-quit. Additionnaly your labels should be one-hot encoded. More general answers on why a model is not learning can be found here.