Question

我想创建一个能够从MINST数据库中猜测数字的神经网络。我正在使用npmjs.org中的库（npm install --save mnist）

我只是无法让我的神经网络学习数字，我还尝试了一个基本的xor问题，但仍然无法正常工作。没有人知道为什么吗？

System.loadLibrary()

我无法让NeuronalNetwork学习。.损失降低了自己。

输出：

import * as tf from '@tensorflow/tfjs-node';
import * as mnist from 'mnist';

const ACTIVATION = "sigmoid"

const input = tf.input({
  shape: [784],
});

const denseLayer1 = tf.layers.dense({
  units: 30,
  activation: ACTIVATION
});

const denseLayer2 = tf.layers.dense({
  units: 30,
  activation: ACTIVATION
});

const output = tf.layers.dense({
  units: 10,
  activation: ACTIVATION
});

const model = tf.model({
  inputs: input,
  outputs: output.apply(denseLayer2.apply(denseLayer1.apply(input)))
});

model.compile({
  optimizer: tf.train.sgd(0.1),
  loss: tf.losses.meanSquaredError
});


let coolSet = mnist.set(100, 300);
let inputs = [];
let outputs = [];
coolSet.training.forEach((oneTraining, index) => {
  inputs.push(oneTraining.input);
  outputs.push(oneTraining.output)
});
outputs = tf.tensor2d(outputs);
inputs = tf.tensor2d(inputs);

let testInputs = [];
let testOutputs = [];
coolSet.test.forEach(oneTest => {
  testInputs.push(oneTest.input);
  testOutputs.push(oneTest.output)
});

train().then(() => {
  testInputs.forEach((x, index) => {
    const predictedOutput = model.predict(tf.tensor2d([x]));
    console.log(`Excpected Output: ${testOutputs[index]}
  Output: ${predictedOutput.toString()}`)
  });
});

async function train() {
  for (let i = 0; i < 100; i++) {
    const config = {
      shuffle: true,
      epochs: 10
    };
    const response = await model.fit(inputs, outputs, config);
    console.log(response.history.loss[0]);
  }
}

Answer 1

此神经网络需要注意两点。

要解决的问题是分类问题。这意味着在给定输入的情况下，输出是在不同标签中选择类别的选择。输出为概率（范围为0-1）。输出的总和应为1。通常，在分类问题中，最后一层是softmax激活，它接受输入层并输出一个得分，该得分指示每种可能类别的概率。

对于损失，最好的选择是binaryCrossentropy或categoricalCrossEntropy。人们并没有真正计算出预测输出与期望输出之间的欧几里得距离。与当它是回归问题时相反，这里的意义较小。

const ACTIVATION = "sigmoid"

const input = tf.input({
  shape: [784],
});

const denseLayer1 = tf.layers.dense({
  units: 30,
  activation: ACTIVATION
});

const denseLayer2 = tf.layers.dense({
  units: 30,
  activation: ACTIVATION
});

const output = tf.layers.dense({
  units: 10,
  activation: 'softmax'
});

const model = tf.model({
  inputs: input,
  outputs: output.apply(denseLayer2.apply(denseLayer1.apply(input)))
});

model.compile({
  optimizer: 'adam',
  loss: 'categoricalCrossentropy'
});


let coolSet = mnist.set(100, 300);
let inputs = [];
let outputs = [];
coolSet.training.forEach((oneTraining, index) => {
  inputs.push(oneTraining.input);
  outputs.push(oneTraining.output)
});
outputs = tf.tensor(outputs);
inputs = tf.tensor(inputs, [100, 784]);

let testInputs = [];
let testOutputs = [];
coolSet.test.forEach(oneTest => {
  testInputs.push(oneTest.input);
  testOutputs.push(oneTest.output)
});

train().then(() => {
  testInputs.slice(0, 10).forEach((x, index) => {
    const predictedOutput = model.predict(tf.tensor([x]));
    console.log(`Excpected Output: ${testOutputs[index]}
  Output: ${predictedOutput.equal(predictedOutput.max(1)).toString()}`)
  });
});

async function train() {
    const config = {
      shuffle: true,
      epochs: 1000, 
      callbacks: {
        onEpochEnd: async (_, l) => {console.log(l.loss)}
      }
    };
    const response = await model.fit(inputs, outputs, config);
}

<script src="https://cdnjs.cloudflare.com/ajax/libs/mnist/1.1.0/mnist.js"></script>
<script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs@0.14.1/dist/tf.min.js"></script>

Answer 2

答案很简单：培训更多！我以为10个纪元和100个迭代就足够了，但是我用1000知道就尝试了，现在可以了！

神经元网络无法正确训练

2 个答案: