本文整理匯總了TypeScript中@tensorflow/tfjs-core.div函數的典型用法代碼示例。如果您正苦於以下問題：TypeScript div函數的具體用法？TypeScript div怎麽用？TypeScript div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div函數的4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:
return tf.tidy(() => {
const avg_r = tf.fill([1, 150, 150, 1], 122.782);
const avg_g = tf.fill([1, 150, 150, 1], 117.001);
const avg_b = tf.fill([1, 150, 150, 1], 104.298);
const avg_rgb = tf.concat([avg_r, avg_g, avg_b], 3)
return tf.div(tf.sub(x, avg_rgb), tf.scalar(256))
})示例2: getCenterCoordinatesAndSizesLayer
function getCenterCoordinatesAndSizesLayer(x: tf.Tensor2D) {
const vec = tf.unstack(tf.transpose(x, [1, 0]))
const sizes = [
tf.sub(vec[2], vec[0]),
tf.sub(vec[3], vec[1])
]
const centers = [
tf.add(vec[0], tf.div(sizes[0], tf.scalar(2))),
tf.add(vec[1], tf.div(sizes[1], tf.scalar(2)))
]
return {
sizes,
centers
}
}示例3: decodeBoxesLayer
function decodeBoxesLayer(x0: tf.Tensor2D, x1: tf.Tensor2D) {
const {
sizes,
centers
} = getCenterCoordinatesAndSizesLayer(x0)
const vec = tf.unstack(tf.transpose(x1, [1, 0]))
const div0_out = tf.div(tf.mul(tf.exp(tf.div(vec[2], tf.scalar(5))), sizes[0]), tf.scalar(2))
const add0_out = tf.add(tf.mul(tf.div(vec[0], tf.scalar(10)), sizes[0]), centers[0])
const div1_out = tf.div(tf.mul(tf.exp(tf.div(vec[3], tf.scalar(5))), sizes[1]), tf.scalar(2))
const add1_out = tf.add(tf.mul(tf.div(vec[1], tf.scalar(10)), sizes[1]), centers[1])
return tf.transpose(
tf.stack([
tf.sub(add0_out, div0_out),
tf.sub(add1_out, div1_out),
tf.add(add0_out, div0_out),
tf.add(add1_out, div1_out)
]),
[1, 0]
)
}示例4: switch
//.........這裏部分代碼省略.........
getParamValue('x', node, tensorMap, context) as tfc.Tensor)];
case 'atanh':
return [tfc.atanh(
getParamValue('x', node, tensorMap, context) as tfc.Tensor)];
case 'ceil':
return [tfc.ceil(
getParamValue('x', node, tensorMap, context) as tfc.Tensor)];
case 'cos':
return [tfc.cos(
getParamValue('x', node, tensorMap, context) as tfc.Tensor)];
case 'cosh':
return [tfc.cosh(
getParamValue('x', node, tensorMap, context) as tfc.Tensor)];
case 'elu':
return [tfc.elu(
getParamValue('x', node, tensorMap, context) as tfc.Tensor)];
case 'erf':
return [tfc.erf(
getParamValue('x', node, tensorMap, context) as tfc.Tensor)];
case 'exp':
return [tfc.exp(
getParamValue('x', node, tensorMap, context) as tfc.Tensor)];
case 'expm1': {
return [tfc.expm1(
getParamValue('x', node, tensorMap, context) as tfc.Tensor)];
}
case 'floor':
return [tfc.floor(
getParamValue('x', node, tensorMap, context) as tfc.Tensor)];
case 'log':
return [tfc.log(
getParamValue('x', node, tensorMap, context) as tfc.Tensor)];
case 'log1p': {
return [tfc.log1p(
getParamValue('x', node, tensorMap, context) as tfc.Tensor)];
}
case 'neg':
return [tfc.neg(
getParamValue('x', node, tensorMap, context) as tfc.Tensor)];
case 'reciprocal': {
return [tfc.reciprocal(
getParamValue('x', node, tensorMap, context) as tfc.Tensor)];
}
case 'relu':
return [tfc.relu(
getParamValue('x', node, tensorMap, context) as tfc.Tensor)];
case 'round': {
return [tfc.round(
getParamValue('x', node, tensorMap, context) as tfc.Tensor)];
}
case 'selu':
return [tfc.selu(
getParamValue('x', node, tensorMap, context) as tfc.Tensor)];
case 'sigmoid':
return [tfc.sigmoid(
getParamValue('x', node, tensorMap, context) as tfc.Tensor)];
case 'sin':
return [tfc.sin(
getParamValue('x', node, tensorMap, context) as tfc.Tensor)];
case 'sign': {
return [tfc.sign(
getParamValue('x', node, tensorMap, context) as tfc.Tensor)];
}
case 'sinh': {
return [tfc.sinh(
getParamValue('x', node, tensorMap, context) as tfc.Tensor)];
}
case 'softplus': {
return [tfc.softplus(
getParamValue('x', node, tensorMap, context) as tfc.Tensor)];
}
case 'sqrt': {
return [tfc.sqrt(
getParamValue('x', node, tensorMap, context) as tfc.Tensor)];
}
case 'square': {
return [tfc.square(
getParamValue('x', node, tensorMap, context) as tfc.Tensor)];
}
case 'tanh': {
return [tfc.tanh(
getParamValue('x', node, tensorMap, context) as tfc.Tensor)];
}
case 'tan':
return [tfc.tan(
getParamValue('x', node, tensorMap, context) as tfc.Tensor)];
case 'clipByValue':
return [tfc.clipByValue(
getParamValue('x', node, tensorMap, context) as tfc.Tensor,
getParamValue('clipValueMin', node, tensorMap, context) as number,
getParamValue('clipValueMax', node, tensorMap, context) as number)];
case 'rsqrt':
return [tfc.div(
tfc.scalar(1.0, 'float32'),
tfc.sqrt(getTensor(node.inputNames[0], tensorMap, context)))];
default:
throw TypeError(`Node type ${node.op} is not implemented`);
}
};