Tensorflow.js tf.train.Optimizer.computeGradients()用法及代码示例

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。

执行 f() 并计算 f() 的标量输出相对于 varList 提供的可训练变量列表的梯度。如果未提供列表，则默认为所有可训练变量。

用法：

Optimizer.computeGradients(f, varList?);

参数：

f ( ( ) => tf.Scalar):要执行的函数及其输出用于计算关于变量的梯度。
varLIst( tf.Variable[ ] ):用于计算梯度的可选变量列表。如果指定，则只有可训练变量是 varList 才会有关于的梯度计算。默认为所有可训练变量。

返回值：{ value:tf.Scalar, grads:{ [ name:string ]:tf.Tensor } }

范例1：

Javascript


// Importing tensorflow
import * as tf from "@tensorflow/tfjs"
     
const xs = tf.tensor1d([3, 4, 5]);
const ys = tf.tensor1d([3.5, 4.7, 5.3]);
     
const x = tf.scalar(Math.random()).variable();
const y = tf.scalar(Math.random()).variable();
     
// Define a function f(x, y) = ( x^2 ) -  y.
const f = x => (x.square()).sub(y);
const loss = (pred, label) =>
    pred.sub(label).square().mean();
     
const learningRate = 0.05;
     
// Create adam optimizer
const optimizer =
tf.train.adam(learningRate);
     
// Train the model.
for (let i = 0; i < 6; i++) {
optimizer.computeGradients(() => 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:0.38272422552108765, y:0.7651948928833008
x:0, pred:8.2348051071167
x:1, pred:15.2348051071167
x:2, pred:24.234806060791016

范例2：

Javascript


// Importing tensorflow
import * as tf from "@tensorflow/tfjs"
     
const xs = tf.tensor1d([0, 1, 2, 3]);
const ys = tf.tensor1d([1.3, 3.7, 12.4, 26.6]);
     
// 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.mul(3)).add(b.square(x)).add(c);
const loss = (pred, label) => pred.sub(label).square().mean();
     
// Setting congigurations for our optimizer
const learningRate = 0.01;
const initialAccumulatorValue = 10;
 
     
// Create the Optimizer
const optimizer = tf.train.adagrad(learningRate,
        initialAccumulatorValue);
     
// Train the model.
for (let i = 0; i < 5; i++) {
optimizer.computeGradients(() => 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:0.22211307287216187,
b:0.2304522693157196,
c:0.42621928453445435
x:0, pred:0.479327529668808
x:1, pred:1.1456668376922607
x:2, pred:1.8120059967041016
x:3, pred:2.4783451557159424

参考：https://js.tensorflow.org/api/latest/#tf.train.Optimizer.computeGradients

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注：本文由纯净天空筛选整理自satyam00so大神的英文原创作品 Tensorflow.js tf.train.Optimizer class .computeGradients() 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。