Tensorflow.js是Google开发的开源库,用于在浏览器或节点环境中运行机器学习模型和深度学习神经网络。
.minimize() 方法执行给定的函数 f() 并尝试通过计算 y 相对于由 varList 表示的给定可训练变量列表的梯度来最小化 f() 的标量输出。如果没有提供列表,它会计算所有可训练变量的梯度。
用法:
Optimizer.minimize (f, returnCost?, varList?)
参数:
- f (() => tf.Scalar):它指定要执行的函数及其输出要最小化。
- returnCost (boolean):指定是否返回执行 f() 产生的标量成本值。
- varList (tf.Variable[]):它指定了可训练变量的列表。
返回值:tf.Scalar |无效的
范例1:
Javascript
// Importing tensorflow
import tensorflow as tf
const xs = tf.tensor1d([0, 1, 2]);
const ys = tf.tensor1d([1.3, 2.5, 3.7]);
const x = tf.scalar(Math.random()).variable();
const y = tf.scalar(Math.random()).variable();
// Define a function f(x, y) = x + y.
const f = x => x.add(y);
const loss = (pred, label) =>
pred.sub(label).square().mean();
const learningRate = 0.05;
// Create adagrad optimizer
const optimizer =
tf.train.adagrad(learningRate);
// Train the model.
for (let i = 0; i < 5; i++) {
optimizer.minimize(() => loss(f(xs), ys));
}
// Make predictions.
console.log(
`x:${x.dataSync()}, y:${y.dataSync()}`);
const preds = f(xs).dataSync();
preds.forEach((pred, i) => {
console.log(`x:${i}, pred:${pred}`);
});
输出
x:0.9395854473114014, y:1.0498266220092773 x:0, pred:1.0498266220092773 x:1, pred:2.0498266220092773 x:2, pred:3.0498266220092773
范例2:
Javascript
// Importing tensorflow
import * as tf from "@tensorflow/tfjs"
const xs = tf.tensor1d([0, 1, 2, 3]);
const ys = tf.tensor1d([1.1, 5.9, 16.8, 33.9]);
// Choosing random coefficients
const a = tf.scalar(Math.random()).variable();
const b = tf.scalar(Math.random()).variable();
const c = tf.scalar(Math.random()).variable();
// Defing function f = (a*x^2 + b*x + c)
const f = x => a.mul(x.square()).add(b.mul(x)).add(c);
const loss = (pred, label) => pred.sub(label).square().mean();
// Setting congigurations for our optimizer
const learningRate = 0.01;
const decay = 0.1;
const momentum = 1;
const epsilon = 0.5;
const centered = true;
// Create the ptimizer
const optimizer = tf.train.rmsprop(learningRate,
decay, momentum, epsilon, centered);
// Train the model.
for (let i = 0; i < 8; i++) {
optimizer.minimize(() => loss(f(xs), ys));
}
// Make predictions.
console.log(`a:${a.dataSync()},
b:${b.dataSync()}, c:${c.dataSync()}`);
const preds = f(xs).dataSync();
preds.forEach((pred, i) => {
console.log(`x:${i}, pred:${pred}`);
});
输出
a:3.6799352169036865, b:4.26292610168457, c:4.544136047363281 x:0, pred:4.544136047363281 x:1, pred:12.486997604370117 x:2, pred:27.789730072021484 x:3, pred:50.45233154296875
参考:https://js.tensorflow.org/api/latest/#tf.train.Optimizer.minimize
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注:本文由纯净天空筛选整理自abhinavjain194大神的英文原创作品 Tensorflow.js tf.train.Optimizer class .minimize() Method。非经特殊声明,原始代码版权归原作者所有,本译文未经允许或授权,请勿转载或复制。