tensorflow.js模型不学习
tensorflow.js model does not learn
我的模型没有学习..它应该在最后进行 softmax 计算。结果我想要一个分类(退出或不退出)。该模型应该预测客户是否会退出。我将退出列作为标签并具有 196 个输入特征。
我的遮阳板说根本没有学习。但是我不确定,如果我的模型学习了,遮阳板将如何获取信息。我是 javascript 的新手,非常感谢任何帮助。
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'] }
)
});
}
最后一层的单元数就是类别数。有两个类别 quit
和 no-quit
。此外,您的标签应该是单热编码的。可以找到关于模型为何不学习的更一般性答案 。
我的模型没有学习..它应该在最后进行 softmax 计算。结果我想要一个分类(退出或不退出)。该模型应该预测客户是否会退出。我将退出列作为标签并具有 196 个输入特征。
我的遮阳板说根本没有学习。但是我不确定,如果我的模型学习了,遮阳板将如何获取信息。我是 javascript 的新手,非常感谢任何帮助。
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'] }
)
});
}
最后一层的单元数就是类别数。有两个类别 quit
和 no-quit
。此外,您的标签应该是单热编码的。可以找到关于模型为何不学习的更一般性答案