@tensorflow-models/coco-ssd/.yalc/@tensorflow/tfjs-core/dist/gradients.d.ts

Object detection model (coco-ssd) in TensorFlow.js

/**
 * @license
 * Copyright 2018 Google Inc. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */
import { CustomGradientFunc, ScopeFn } from './engine';
import { Scalar, Tensor, Variable } from './tensor';
import { NamedTensorMap, TensorContainer } from './tensor_types';
/**
 * Create a new gradient scope. Similar to scope, but forces all inner scopes
 * to not clean up so that gradient operations can be used inside of this
 * scope.
 * @param nameOrScopeFn The name of the scope, or the function to execute.
 *     If a name is provided, the 2nd argument should be the function.
 *     If a name is provided, and debug mode is on, the timing and the memory
 *     usage of the function will be tracked and displayed on the console
 *     using the provided name.
 * @param scopeFn The function to execute.
 */
declare function gradScope<T extends TensorContainer>(nameOrScopeFn: string | ScopeFn<T>, scopeFn?: ScopeFn<T>): T;
/**
 * Provided `f(x)`, returns another function `g(x, dy?)`, which gives the
 * gradient of `f(x)` with respect to `x`.
 *
 * If `dy` is provided, the gradient of `f(x).mul(dy).sum()` with respect to
 * `x` is computed instead. `f(x)` must take a single tensor `x` and return a
 * single tensor `y`. If `f()` takes multiple inputs, use `tf.grads` instead.
 *
 * ```js
 * // f(x) = x ^ 2
 * const f = x => x.square();
 * // f'(x) = 2x
 * const g = tf.grad(f);
 *
 * const x = tf.tensor1d([2, 3]);
 * g(x).print();
 * ```
 *
 * ```js
 * // f(x) = x ^ 3
 * const f = x => x.pow(tf.scalar(3, 'int32'));
 * // f'(x) = 3x ^ 2
 * const g = tf.grad(f);
 * // f''(x) = 6x
 * const gg = tf.grad(g);
 *
 * const x = tf.tensor1d([2, 3]);
 * gg(x).print();
 * ```
 *
 * @param f The function f(x), to compute gradient for.
 */
/** @doc {heading: 'Training', subheading: 'Gradients'} */
declare function grad<I extends Tensor, O extends Tensor>(f: (x: I) => O): (x: I, dy?: O) => I;
/**
 * Provided `f(x1, x2,...)`, returns another function `g([x1, x2,...], dy?)`,
 * which gives an array of gradients of `f()` with respect to each input
 * [`x1`,`x2`,...].
 *
 * If `dy` is passed when calling `g()`, the gradient of
 * `f(x1,...).mul(dy).sum()` with respect to each input is computed instead.
 * The provided `f` must take one or more tensors and return a single tensor
 * `y`. If `f()` takes a single input, we recommend using `tf.grad` instead.
 *
 * ```js
 * // f(a, b) = a * b
 * const f = (a, b) => a.mul(b);
 * // df / da = b, df / db = a
 * const g = tf.grads(f);
 *
 * const a = tf.tensor1d([2, 3]);
 * const b = tf.tensor1d([-2, -3]);
 * const [da, db] = g([a, b]);
 * console.log('da');
 * da.print();
 * console.log('db');
 * db.print();
 * ```
 *
 * @param f The function `f(x1, x2,...)` to compute gradients for.
 */
/** @doc {heading: 'Training', subheading: 'Gradients'} */
declare function grads<O extends Tensor>(f: (...args: Tensor[]) => O): (args: Tensor[], dy?: O) => Tensor[];
/**
 * Like `tf.grad`, but also returns the value of `f()`. Useful when `f()`
 * returns a metric you want to show.
 *
 * The result is a rich object with the following properties:
 * - grad: The gradient of `f(x)` w.r.t `x` (result of `tf.grad`).
 * - value: The value returned by `f(x)`.
 *
 * ```js
 * // f(x) = x ^ 2
 * const f = x => x.square();
 * // f'(x) = 2x
 * const g = tf.valueAndGrad(f);
 *
 * const x = tf.tensor1d([2, 3]);
 * const {value, grad} = g(x);
 *
 * console.log('value');
 * value.print();
 * console.log('grad');
 * grad.print();
 * ```
 */
/** @doc {heading: 'Training', subheading: 'Gradients'} */
declare function valueAndGrad<I extends Tensor, O extends Tensor>(f: (x: I) => O): (x: I, dy?: O) => {
    value: O;
    grad: I;
};
/**
 * Like `tf.grads`, but returns also the value of `f()`. Useful when `f()`
 * returns a metric you want to show.
 *
 * The result is a rich object with the following properties:
 * - grads: The gradients of `f()` w.r.t each input (result of `tf.grads`).
 * - value: The value returned by `f(x)`.
 *
 * ```js
 * // f(a, b) = a * b
 * const f = (a, b) => a.mul(b);
 * // df/da = b, df/db = a
 * const g = tf.valueAndGrads(f);
 *
 * const a = tf.tensor1d([2, 3]);
 * const b = tf.tensor1d([-2, -3]);
 * const {value, grads} = g([a, b]);
 *
 * const [da, db] = grads;
 *
 * console.log('value');
 * value.print();
 *
 * console.log('da');
 * da.print();
 * console.log('db');
 * db.print();
 * ```
 */
/** @doc {heading: 'Training', subheading: 'Gradients'} */
declare function valueAndGrads<O extends Tensor>(f: (...args: Tensor[]) => O): (args: Tensor[], dy?: O) => {
    grads: Tensor[];
    value: O;
};
/**
 * Computes and returns the gradient of f(x) with respect to the list of
 * trainable variables provided by `varList`. If no list is provided, it
 * defaults to all trainable variables.
 *
 * ```js
 * const a = tf.variable(tf.tensor1d([3, 4]));
 * const b = tf.variable(tf.tensor1d([5, 6]));
 * const x = tf.tensor1d([1, 2]);
 *
 * // f(a, b) = a * x ^ 2 + b * x
 * const f = () => a.mul(x.square()).add(b.mul(x)).sum();
 * // df/da = x ^ 2, df/db = x
 * const {value, grads} = tf.variableGrads(f);
 *
 * Object.keys(grads).forEach(varName => grads[varName].print());
 * ```
 *
 * @param f The function to execute. f() should return a scalar.
 * @param varList The list of trainable variables. Defaults to all variables.
 */
/** @doc {heading: 'Training', subheading: 'Gradients'} */
declare function variableGrads(f: () => Scalar, varList?: Variable[]): {
    value: Scalar;
    grads: NamedTensorMap;
};
/**
 * Overrides the gradient computation of a function `f`.
 *
 * Takes a function
 * `f(...inputs) => {value: Tensor, gradFunc: dy => Tensor[]}` and returns
 * another function `g(...inputs)` which takes the same inputs as `f`. When
 * called, `g` returns `f().value`. In backward mode, custom gradients with
 * respect to each input of `f` are computed using `f().gradFunc`.
 *
 * ```js
 * const customOp = tf.customGrad(x => {
 *   // Override gradient of our custom x ^ 2 op to be dy * abs(x);
 *   return {value: x.square(), gradFunc: dy => [dy.mul(x.abs())]};
 * });
 *
 * const x = tf.tensor1d([-1, -2, 3]);
 * const dx = tf.grad(x => customOp(x));
 *
 * console.log(`f(x):`);
 * customOp(x).print();
 * console.log(`f'(x):`);
 * dx(x).print();
 * ```
 *
 * @param f The function to evaluate in forward mode, which should return
 *     `{value: Tensor, gradFunc: (dy) => Tensor[]}`, where `gradFunc` returns
 *     the custom gradients of `f` with respect to its inputs.
 */
/** @doc {heading: 'Training', subheading: 'Gradients'} */
declare function customGrad<T extends Tensor>(f: CustomGradientFunc<T>): (...args: Tensor[]) => T;
export { gradScope, customGrad, variableGrads, valueAndGrad, valueAndGrads, grad, grads, };