本文整理匯總了TypeScript中deeplearn.setBackend函數的典型用法代碼示例。如果您正苦於以下問題：TypeScript setBackend函數的具體用法？TypeScript setBackend怎麽用？TypeScript setBackend使用的例子？那麽, 這裏精選的函數代碼示例或許可以為您提供幫助。
在下文中一共展示了setBackend函數的6個代碼示例，這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚，您的評價將有助於係統推薦出更棒的TypeScript代碼示例。
示例1: run
async run(size: number, option: string) {
dl.setBackend('webgl');
const input: dl.Tensor2D = dl.randomUniform([size, size], -1, 1);
const op = getUnaryOp(option);
const benchmark = () => op(input);
const time = await benchmark_util.warmupAndBenchmarkGPU(benchmark);
input.dispose();
return time;
}示例2: run
async run(size: number, option: string): Promise<number> {
dl.setBackend('cpu');
const input: dl.Tensor2D = dl.randomUniform([size, size], -1, 1);
const op = getReductionOp(option);
const start = performance.now();
dl.tidy(() => {
op(input).get();
});
const end = performance.now();
return end - start;
}示例3: run
async run(size: number): Promise<number> {
dl.setBackend('webgl');
const a: dl.Tensor2D = dl.randomNormal([size, size]);
const b: dl.Tensor2D = dl.randomNormal([size, size]);
const benchmark = () => dl.matMul(a, b);
const time = await benchmark_util.warmupAndBenchmarkGPU(benchmark);
a.dispose();
b.dispose();
return time;
}示例4: run
async run(size: number, opType: string, params: ConvParams): Promise<number> {
dl.setBackend('webgl');
const inDepth = params.inDepth;
const inShape: [number, number, number] = [size, size, inDepth];
const filterSize = params.filterSize;
const stride = params.stride;
const pad = params.pad;
let x: dl.Tensor3D = dl.randomUniform(inShape, -1, 1);
let W: dl.Tensor4D;
let benchmark: () => dl.Tensor;
if (opType === 'regular') {
const regParams = params as RegularConvParams;
const wShape: [number, number, number, number] =
[filterSize, filterSize, inDepth, regParams.outDepth];
W = dl.randomUniform(wShape, -1, 1);
benchmark = () => x.conv2d(W, stride, pad);
} else if (opType === 'transposed') {
const regParams = params as RegularConvParams;
const wShape: [number, number, number, number] =
[filterSize, filterSize, inDepth, regParams.outDepth];
W = dl.randomUniform(wShape, -1, 1);
x = dl.randomUniform([size, size, regParams.outDepth], -1, 1);
benchmark = () =>
x.conv2dTranspose(W, [size, size, inDepth], stride, pad);
} else if (opType === 'depthwise') {
const depthwiseParams = params as DepthwiseConvParams;
const wShape: [number, number, number, number] =
[filterSize, filterSize, inDepth, depthwiseParams.channelMul];
W = dl.randomUniform(wShape, -1, 1);
benchmark = () => x.depthwiseConv2D(W, stride, pad);
} else {
throw new Error(`Unknown option ${opType}`);
}
const time = await benchmark_util.warmupAndBenchmarkGPU(benchmark);
x.dispose();
W.dispose();
return time;
}示例5: run
async run(size: number, option: string, params: PoolBenchmarkParams):
Promise<number> {
dl.setBackend('webgl');
const outputDepth = params.depth;
const xShape: [number, number, number] = [size, size, outputDepth];
const fieldSize = params.fieldSize;
const stride = params.stride;
const x: dl.Tensor3D = dl.randomUniform(xShape, -1, 1);
const op = getPoolingOp(option);
const benchmark = () => op(x, fieldSize, stride);
const time = await benchmark_util.warmupAndBenchmarkGPU(benchmark);
x.dispose();
return time;
}示例6: run
async run(size: number) {
dl.setBackend('webgl');
const x: dl.Tensor3D = dl.randomUniform([size, size, 8], -1, 1);
const mean = dl.tensor1d([0]);
const variance = dl.tensor1d([1]);
const varianceEpsilon = .001;
const benchmark = () =>
x.batchNormalization(mean, variance, varianceEpsilon);
const time = await benchmark_util.warmupAndBenchmarkGPU(benchmark);
x.dispose();
mean.dispose();
variance.dispose();
return time;
}