/**
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* @license
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* Copyright 2018 Google LLC
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*
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* Use of this source code is governed by an MIT-style
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* license that can be found in the LICENSE file or at
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* https://opensource.org/licenses/MIT.
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* =============================================================================
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*/
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/**
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* deeplearn.js backend.
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*/
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import * as tfc from '@tensorflow/tfjs-core';
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import { onesLike as coreOnesLike, scalar, tensor1d, tidy, where, zerosLike as coreZerosLike } from '@tensorflow/tfjs-core';
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import { checkDataFormat } from '../common';
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import { NotImplementedError, ValueError } from '../errors';
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import * as math_utils from '../utils/math_utils';
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import { imageDataFormat } from './common';
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// tslint:enable
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/* Setting and getting backend from deeplearn.js. */
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// Default deeplearn.js backend is WebGL (GPU).
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let backend = 'webgl';
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export function setBackend(requestedBackend) {
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tfc.setBackend(requestedBackend);
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backend = requestedBackend;
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}
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export function getBackend() {
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return backend;
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}
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/**
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* Indicates whether the backend is operating symbolically.
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*
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* This function will be used to determine how to interpret user code. If
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* it returns true, calls to the backend construct a symbolic graph; if
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* it returns false, calls to the backend execute immediately.
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*/
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export function isBackendSymbolic() {
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return false;
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}
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/**
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* Get the number of elements in a Tensor.
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* @param x The Tensor.
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* @return Number of elements in `x`.
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*/
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export function countParams(x) {
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const shape = x.shape;
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if (shape.length > 0) {
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return shape.reduce((a, b) => a * b);
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}
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else {
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// Scalar.
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return 1;
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}
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}
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/**
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* Casts a tensor to a different dtype and returns it.
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* @param x Input tensor.
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* @param dtype String: 'float32'|'int32'|'bool'.
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* @returns Tensor of the specified `dtype`.
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*/
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export function cast(x, dtype) {
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return tfc.cast(x, dtype);
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}
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/**
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* Adds a 1-sized dimension at index "axis".
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* @param x Input tensor.
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* @param axis Position where to add the new axis.
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* @returns Result of the dimension expansion.
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*/
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export function expandDims(x, axis = -1) {
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const outShape = x.shape.slice();
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if (axis < 0) {
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axis = outShape.length + axis + 1;
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}
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outShape.splice(axis, 0, 1);
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return tfc.reshape(x, outShape);
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}
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/**
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* Repeats a 2D tensor.
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*
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* If `x` has shape `[samples, dim]` and `n` is 2, for example, the output
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* will have shape `[samples, 2, dim]`.
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*
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* @param x Input tensor.
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* @param n Integer, number of times to repeat.
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* @returns The result of the repeat operation.
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* @throws ValueError: If input tensor is not 2D.
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*/
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export function repeat(x, n) {
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return tidy(() => {
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if (x.shape.length !== 2) {
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throw new ValueError(`repeat() expects a rank-2 tensor, but received a ` +
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`rank-${x.shape.length} tensor.`);
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}
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const y = expandDims(x, 1);
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return tile(y, [1, n, 1]);
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});
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}
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/**
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* Flatten a Tensor into 1D.
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* @param x Input tensor.
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* @return The result of the flattening `x`.
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*/
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export function flatten(x) {
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const newShape = [math_utils.arrayProd(x.shape)];
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return tfc.reshape(x, newShape);
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}
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/**
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* Turn a nD tensor into a 2D tensor with same 0th dimension.
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* In other words, it flattens each data samples of a batch.
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*
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* @param x The tensor to flatten. The rank of this tensor is required to be 2
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* or higher.
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* @return The result of the flattening.
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*/
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export function batchFlatten(x) {
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if (x.rank <= 1) {
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throw new ValueError(`batchFlatten requires a minimum rank of 2. Got rank: ${x.rank}.`);
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}
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const newShape = [x.shape[0], math_utils.arrayProd(x.shape, 1)];
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return tfc.reshape(x, newShape);
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}
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/**
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* Do slicing along the first axis.
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* @param array input `tf.Tensor`.
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* @param start starting index, inclusive.
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* @param size size of the slice along the first axis.
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* @returns result of the slicing.
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* @throws ValueError: If `array` is of an unsupported subtype of `tf.Tensor`.
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*/
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export function sliceAlongFirstAxis(array, start, size) {
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return tidy(() => {
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switch (array.rank) {
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case 1:
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return tfc.slice1d(array, start, size);
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case 2:
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return tfc.slice2d(array, [start, 0], [size, array.shape[1]]);
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case 3:
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return tfc.slice3d(array, [start, 0, 0], [size, array.shape[1], array.shape[2]]);
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case 4:
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return tfc.slice4d(array, [start, 0, 0, 0], [size, array.shape[1], array.shape[2], array.shape[3]]);
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case 5:
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return tfc.slice(array, [start, 0, 0, 0, 0], [
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size, array.shape[1], array.shape[2], array.shape[3], array.shape[4]
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]);
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case 6:
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return tfc.slice(array, [start, 0, 0, 0, 0, 0], [
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size, array.shape[1], array.shape[2], array.shape[3], array.shape[4],
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array.shape[5]
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]);
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default:
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throw new ValueError(`sliceAlongFirstAxis() received an unsupported tensor rank: ` +
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`${array.rank}`);
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}
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});
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}
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/**
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* Do slicing along the last axis.
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* @param array input `tf.Tensor`.
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* @param start starting index, inclusive.
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* @param size size of the slice along the last axis.
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* @returns result of the slicing.
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* @throws ValueError: If `array` is of an unsupported subtype of `tf.Tensor`.
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*/
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export function sliceAlongLastAxis(array, start, size) {
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return tidy(() => {
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switch (array.rank) {
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case 1:
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return tfc.slice1d(array, start, size);
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case 2:
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return tfc.slice2d(array, [0, start], [array.shape[0], size]);
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case 3:
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return tfc.slice3d(array, [0, 0, start], [array.shape[0], array.shape[1], size]);
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case 4:
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return tfc.slice4d(array, [0, 0, 0, start], [array.shape[0], array.shape[1], array.shape[2], size]);
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default:
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throw new ValueError(`sliceAlongLastAxis() received an unsupported tensor rank: ` +
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`${array.rank}`);
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}
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});
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}
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/**
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* Do slicing along the sepcified axis.
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* @param array input `tf.Tensor`.
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* @param start starting index, inclusive.
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* @param size of the slice along the chosen axis.
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* @param choose an axis.
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* @returns result of the slicing.
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* @throws ValueError: If `array` is of an unsupported subtype of `tf.Tensor`.
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*/
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export function sliceAlongAxis(array, start, size, axis) {
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return tidy(() => {
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switch (array.rank) {
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case 1:
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return tfc.slice1d(array, start, size);
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case 2:
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switch (axis) {
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case 1:
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return sliceAlongFirstAxis(array, start, size);
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case 2:
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return sliceAlongLastAxis(array, start, size);
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default:
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throw new ValueError(`The axis is not within the rank of the tensor ` +
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`${axis}`);
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}
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case 3:
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switch (axis) {
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case 1:
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return sliceAlongFirstAxis(array, start, size);
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case 2:
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return tfc.slice3d(array, [0, start, 0], [array.shape[0], size, array.shape[2]]);
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case 3:
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return sliceAlongLastAxis(array, start, size);
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default:
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throw new ValueError(`The axis is not within the rank of the tensor ` +
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`${axis}`);
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}
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case 4:
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switch (axis) {
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case 1:
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return sliceAlongFirstAxis(array, start, size);
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case 2:
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return tfc.slice4d(array, [0, start, 0, 0], [array.shape[0], size, array.shape[2], array.shape[3]]);
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case 3:
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return tfc.slice4d(array, [0, 0, start, 0], [array.shape[0], array.shape[1], size, array.shape[3]]);
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case 4:
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return sliceAlongLastAxis(array, start, size);
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default:
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throw new ValueError(`The axis is not within the rank of the tensor ` +
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`${axis}`);
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}
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default:
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throw new ValueError(`sliceAlongLastAxis() received an unsupported tensor rank: ` +
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`${array.rank}`);
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}
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});
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}
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/**
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* Concatenates a list of tensors alongside the specified axis.
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* @param tensors `Array` of tensors to concatenate.
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* @param axis Concatenation axis.
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* @returns The result of the concatenation.
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*/
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export function concatenate(tensors, axis = -1) {
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let rank;
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if (axis < 0) {
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rank = tensors[0].rank;
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if (rank !== 0) {
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axis = rank;
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}
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else {
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axis = 0;
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}
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}
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if (axis === tensors[0].rank) {
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// Porting Note: This is necessary because tfc.concat() requires axis to be
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// in the interval [-rank, rank).
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axis = -1;
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}
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// Porting Note: Sparse concat is not supported yet.
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return tfc.concat(tensors, axis);
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}
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/**
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* Concatenate two arrays along the first dimension.
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* @param a The 1st `tf.Tensor` to concatenate.
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* @param b The 2nd `tf.Tensor` to concatenate.
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* @returns Result of the concatenation.
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* @throws ValueError: If `a` is of an unsupported subtype of `tf.Tensor`.
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*/
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export function concatAlongFirstAxis(a, b) {
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switch (a.rank) {
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case 1:
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return tfc.concat1d([a, b]);
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case 2:
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return tfc.concat2d([a, b], 0);
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case 3:
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return tfc.concat3d([a, b], 0);
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case 4:
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return tfc.concat4d([a, b], 0);
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default:
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throw new ValueError(`concatAlongFirstAxis() received an unsupported ` +
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`tensor rank: ${a.rank}`);
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}
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}
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/**
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* Creates a tensor by tiling `x` by `n`.
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* @param x A tensor.
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* @param n An Array of integers or a single integer. If an Array, the length
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* must be the same as the number of dimensions in `x`. If a single integer,
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* it will be treated as an Array of length 1.
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*/
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export function tile(x, n) {
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if (!Array.isArray(n)) {
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n = [n];
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}
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if (x.rank !== n.length) {
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throw new ValueError(`The length of input n (${n.length}) does not match ` +
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`the number of dimensions in input x (${x.rank})`);
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}
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return tfc.tile(x, n);
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}
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/* Creation of random tensors. */
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/**
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* Get a tensor with normal distribution of values.
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*
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* @param shape Shape of the tensor.
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* @param mean mean value of the normal distribution.
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* @param stddev standard deviation of the normal distribution.
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* @param dtype
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* @param seed
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* @return The normal tensor.
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*/
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export function randomNormal(shape, mean = 0.0, stddev = 1.0, dtype, seed) {
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return tfc.randomNormal(shape, mean, stddev, dtype, seed);
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}
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/* Linear Algebra */
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/**
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* Multiply two tensors and returns the result as a tensor.
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*
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* For 2D tensors, this is equivalent to matrix multiplication (matMul).
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* For tensors of higher ranks, it follows the Theano behavior,
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* (e.g. `(2, 3) * (4, 3, 5) -> (2, 4, 5)`). From the Theano documentation:
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*
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* For N dimensions it is a sum product over the last axis of x and the
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* second-to-last of y:
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*
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* @param a A tensor of at least rank 2.
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* @param b A tensor of at least rank 2.
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* @param activation (optional) A string identifying the activation
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* function.
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* @return Result of the dot operation.
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*/
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export function dot(a, b, activation, bias) {
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if ((a.rank < 2) || (b.rank < 2)) {
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throw new NotImplementedError(`dot requires both inputs to be rank >= 2` +
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` but got x shape = ${a.shape} and y shape = ${b.shape}`);
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}
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if (b.rank >= 3) {
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const xLastDim = a.shape.slice(-1)[0];
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const ySecondLastDim = b.shape.slice(-2)[0];
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if (xLastDim !== ySecondLastDim) {
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throw new NotImplementedError(`If rank y >= 3, then the second last dim` +
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` of y must equal the last dim of x but got x shape = ${a.shape} and ` +
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` y shape = ${b.shape}`);
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}
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}
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// Handle basic 2D x 2D case.
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if ((a.rank === 2) && (b.rank === 2)) {
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const transposeA = false;
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const transposeB = false;
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// tfc.fused.matMul only fuses certain activation functions. Unsupported
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// activation functions are treated as 'linear' activations, which is
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// equivalent to a no-op.
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return tfc.fused.matMul({
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a,
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b: b,
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transposeA,
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transposeB,
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bias: bias ? reshapeBias(a.rank, bias, imageDataFormat()) : null,
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activation
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});
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}
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else {
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// Reshape x into the analogous 2D Tensor.
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const aFirstDims = a.shape.slice(); // Holds all but the last dim of x.
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const aLastDim = aFirstDims.pop();
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a = tfc.reshape(a, [-1, aLastDim]);
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// Reshape y into the analogous 2D Tensor, and keep track of the
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// required dimensions to reproduce the output shape.
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const bShape = b.shape.slice();
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const bLastDim = bShape.pop();
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const ySecondLastDim = bShape.pop();
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const yOtherDims = [...bShape, bLastDim];
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// permutation should be like [r-2, 0, 1, 2, ... r-4, r-3, r-1]
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// where r is the rank of y.
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const perm = Array.from({ length: b.rank }, (_, i) => {
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if (i === 0) {
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return b.rank - 2;
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}
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else if (i <= b.rank - 2) {
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return i - 1;
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}
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return i;
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});
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b = tfc.reshape(tfc.transpose(b, perm), [ySecondLastDim, -1]);
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// Multiply x and y as 2D Tensors, and then reshape back to original.
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const outputShape = [...aFirstDims, ...yOtherDims];
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const transposeA = false;
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const transposeB = false;
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return tfc.reshape(tfc.fused.matMul({
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a,
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b,
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transposeA,
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transposeB,
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bias: bias ? reshapeBias(a.rank, bias, imageDataFormat()) : null,
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activation
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}), outputShape);
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}
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}
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/**
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* Compute the sign Tensor of an input Tensor.
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*
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* Elements of the input `tf.Tensor` that are === 0 are mapped to 0.
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* Elements of the input `tf.Tensor` that are > 0 are mapped to 1.
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* Elements of the input `tf.Tensor` that are < 0 are mapped to -1.
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*
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* @param x Input `tf.Tensor`.
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* @return The sign `tf.Tensor`.
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*/
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export function sign(x) {
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// TODO(cais): Move to the core.
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return tidy(() => {
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const zerosLikeX = coreZerosLike(x);
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const onesLikeX = coreOnesLike(x);
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return where(tfc.equal(x, zerosLikeX), zerosLikeX, where(tfc.greater(x, coreZerosLike(x)), onesLikeX, tfc.mul(-1, onesLikeX)));
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});
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}
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/**
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* Computes the one-hot representation of an integer tensor.
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* @param indices nD integer tensor of shape
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* `(batch_size, dim1, dim2, ... dim(n-1))`
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* @param numClasses Integer, number of classes to consider.
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* @returns (n + 1)D one hot representation of the input
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* with shape `(batch_size, dim1, dim2, ... dim(n-1), num_classes)`
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*/
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export function oneHot(indices, numClasses) {
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return tidy(() => {
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if (indices.rank !== 1) {
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throw new Error('Only 1D one-hot tensors are supported in the ' +
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'deeplearn backend, at present.');
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}
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indices = tfc.cast(indices, 'int32');
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return tfc.cast(tfc.oneHot(indices, numClasses), 'float32');
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});
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}
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/* Elementary math functions. */
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/**
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* Retrieves the elements of indices `indices` in the tensor `reference`.
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* @param reference A tensor.
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* @param indices An integer tensor of indices or an `Array` of integers.
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* @param axis Axis along which to perform the gather operation.
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* @returns The result of the gathering as a tensor.
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*/
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export function gather(reference, indices, axis) {
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return tidy(() => {
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if (Array.isArray(indices)) {
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indices = tensor1d(indices, 'int32');
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}
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else {
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indices = tfc.cast(indices, 'int32');
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}
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return tfc.gather(reference, indices, axis);
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});
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}
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/**
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* Element-wise square.
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* @param x Input tensor.
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* @return element-wise x^2
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*/
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export function square(x) {
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return tfc.mul(x, x);
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}
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/**
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* Element-wise exponentiation.
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*
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* Porting Note: In PyKeras, `a` (the exponent) is a Python integer, which
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* takes advatnage of the backend's (e.g., TensorFlow's) automatic
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* conversion to tensor. Here we allow `a` to be either a number or a tensor.
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*
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* @param x The base tensor.
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* @param a The exponent, tensor or number. If a number, it is rounded to the
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* nearest integer and converted to a tensor.
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* @returns A tensor of the same shape as `x`.
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*/
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export function pow(x, a) {
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return tidy(() => {
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if (typeof (a) === 'number') {
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a = scalar(Math.round(a), 'int32');
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}
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if (a.dtype !== 'int32') {
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throw new NotImplementedError(`Non-int32 dtype (${a.dtype}) is not supported by pow() yet`);
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}
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return tfc.pow(x, a);
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});
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}
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/**
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* Reshapes bias tensor according to rank of x.
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*/
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function reshapeBias(xRank, bias, dataFormat) {
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const biasShape = bias.shape;
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if (bias.rank !== 1 && bias.rank !== xRank) {
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throw new ValueError(`Unexpected bias dimensions: ${bias.rank}` +
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`; expected it to be 1 or ${xRank}`);
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}
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if (xRank === 5) {
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if (dataFormat === 'channelsFirst') {
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if (biasShape.length === 1) {
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return tfc.reshape(bias, [1, biasShape[0], 1, 1, 1]);
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}
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else {
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return tfc.reshape(bias, [1, biasShape[3], biasShape[0], biasShape[1], biasShape[2]]);
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}
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}
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else if (dataFormat === 'channelsLast') {
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if (biasShape.length === 1) {
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return tfc.reshape(bias, [1, 1, 1, 1, biasShape[0]]);
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}
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else {
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return tfc.reshape(bias, [1].concat(biasShape));
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}
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}
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}
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else if (xRank === 4) {
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if (dataFormat === 'channelsFirst') {
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if (biasShape.length === 1) {
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return tfc.reshape(bias, [1, biasShape[0], 1, 1]);
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}
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else {
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return tfc.reshape(bias, [1, biasShape[2], biasShape[0], biasShape[1]]);
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}
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}
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else if (dataFormat === 'channelsLast') {
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if (biasShape.length === 1) {
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return tfc.reshape(bias, [1, 1, 1, biasShape[0]]);
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}
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else {
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return tfc.reshape(bias, [1].concat(biasShape));
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}
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}
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}
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else if (xRank === 3) {
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if (dataFormat === 'channelsFirst') {
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if (biasShape.length === 1) {
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return tfc.reshape(bias, [1, biasShape[0], 1]);
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}
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else {
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return tfc.reshape(bias, [1, biasShape[1], biasShape[0]]);
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}
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}
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else if (dataFormat === 'channelsLast') {
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if (biasShape.length === 1) {
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return tfc.reshape(bias, [1, 1, biasShape[0]]);
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}
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else {
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return tfc.reshape(bias, [1].concat(biasShape));
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}
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}
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}
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else if (xRank < 3) {
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return bias;
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}
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throw new ValueError(`Unsupported input rank by biasAdd: ${bias.rank}`);
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}
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/* Neural-network operations. */
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/**
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* Add a bias to a tensor.
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*
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* @param x The tensor to add the bias to.
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* @param bias The bias to add to `x`. Must be 1D or the same rank as `x`.
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* @return Result of the bias adding.
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* @throws ValueError: If the rank of `bias` is incorrect.
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*/
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export function biasAdd(x, bias, dataFormat) {
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return tidy(() => {
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if (dataFormat == null) {
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dataFormat = imageDataFormat();
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}
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checkDataFormat(dataFormat);
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return tfc.add(x, reshapeBias(x.rank, bias, dataFormat));
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});
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}
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/**
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* Exponential linear unit (ELU).
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* @param x A tensor or variable to compute the activation function for.
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* @param alpha: A scalar, a scaling factor for the negative section.
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* @return Output of the ELU operation.
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*/
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export function elu(x, alpha = 1) {
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// TODO(cais): Add support for alpha values other than 1.
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if (alpha !== 1) {
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throw new NotImplementedError(`Support for alpha values other than 1 (${alpha}) is not implemented ` +
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`yet.`);
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}
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return tfc.elu(x);
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}
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/**
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* Softsign of a tensor.
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*
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* Defined as x / (abs(x) + 1), element-wise.
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*
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* @param x: Input.
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* @returns Output.
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*/
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export function softsign(x) {
|
return tidy(() => tfc.div(x, tfc.add(tfc.abs(x), 1)));
|
}
|
/**
|
* Sets entries in `x` to zero at random, while scaling the entire tensor.
|
*
|
* @param x input tensor.
|
* @param level fraction of the entries in the tensor that will be set to 0.
|
* @param noiseShape shape of randomly generated keep/drop flags, must be
|
* broadcastable to the shape of `x`. Optional.
|
* @param seed random seed to ensure determinism. Optional.
|
* @returns Result of the dropout operation.
|
*/
|
export function dropout(x, level, noiseShape, seed) {
|
return tidy(() => tfc.dropout(x, level, noiseShape, seed));
|
}
|
/**
|
* Element-wise, segment-wise linear approximation of sigmoid.
|
*
|
* Returns `0.` if `x < -2.5`, `1.` if `x > 2.5`.
|
* In `-2.5 <= x <= 2.5`, returns `0.2 * x + 0.5`.
|
*
|
* @param x Input tensor.
|
* @returns Output tensor.
|
*/
|
export function hardSigmoid(x) {
|
return tidy(() => {
|
const y = tfc.add(.5, tfc.mul(.2, x));
|
return tfc.clipByValue(y, 0, 1);
|
});
|
}
|
/**
|
* Invoke `x` in the training phase, and `alt` otherwise.
|
*
|
* Porting Note: We do not create placeholder tensors for the `training`
|
* boolean flag here, because there is no such thing in the TF.js imperative
|
* backend.
|
*
|
* @param x The function to invoke iff `training` is `true`.
|
* @param alt The function to invoke iff `training` is `false`.
|
* @param training Boolean flag for whether training phase is active.
|
* @returns The return value of `x()` if `training` is `true`, or the return
|
* value of `alt()` if `training` is `false`.
|
*/
|
export function inTrainPhase(x, alt, training = false) {
|
return training ? x() : alt();
|
}
|
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* @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n\n/**\n * deeplearn.js backend.\n */\n\nimport * as tfc from '@tensorflow/tfjs-core';\nimport {onesLike as coreOnesLike, scalar, Tensor, Tensor1D, tensor1d, Tensor2D, Tensor3D, Tensor4D, Tensor5D, tidy, where, zerosLike as coreZerosLike} from '@tensorflow/tfjs-core';\nimport {checkDataFormat} from '../common';\nimport {NotImplementedError, ValueError} from '../errors';\nimport {DataFormat, Shape} from '../keras_format/common';\nimport {HasShape} from '../types';\nimport * as math_utils from '../utils/math_utils';\n\nimport {imageDataFormat} from './common';\n\n// tslint:enable\n\n/* Setting and getting backend from deeplearn.js. */\n\n// Default deeplearn.js backend is WebGL (GPU).\nlet backend: 'cpu'|'webgl' = 'webgl';\n\nexport function setBackend(requestedBackend: 'cpu'|'webgl') {\n  tfc.setBackend(requestedBackend);\n  backend = requestedBackend;\n}\n\nexport function getBackend(): 'cpu'|'webgl' {\n  return backend;\n}\n\n/**\n * Indicates whether the backend is operating symbolically.\n *\n * This function will be used to determine how to interpret user code. 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sliceAlongLastAxis(array, start, size);\n          default:\n            throw new ValueError(\n                `The axis is not within the rank of the tensor ` +\n                `${axis}`);\n        }\n      case 4:\n        switch (axis) {\n          case 1:\n            return sliceAlongFirstAxis(array, start, size);\n          case 2:\n            return tfc.slice4d(\n                array as Tensor4D, [0, start, 0, 0],\n                [array.shape[0], size, array.shape[2], array.shape[3]]);\n          case 3:\n            return tfc.slice4d(\n                array as Tensor4D, [0, 0, start, 0],\n                [array.shape[0], array.shape[1], size, array.shape[3]]);\n          case 4:\n            return sliceAlongLastAxis(array, start, size);\n          default:\n            throw new ValueError(\n                `The axis is not within the rank of the tensor ` +\n                `${axis}`);\n        }\n      default:\n        throw new ValueError(\n            `sliceAlongLastAxis() received an unsupported tensor rank: ` +\n            `${array.rank}`);\n    }\n  });\n}\n\n/**\n * Concatenates a list of tensors alongside the specified axis.\n * @param tensors `Array` of tensors to concatenate.\n * @param axis Concatenation axis.\n * @returns The result of the concatenation.\n */\nexport function concatenate(tensors: Tensor[], axis = -1): Tensor {\n  let rank: number;\n  if (axis < 0) {\n    rank = tensors[0].rank;\n    if (rank !== 0) {\n      axis = rank;\n    } else {\n      axis = 0;\n    }\n  }\n  if (axis === tensors[0].rank) {\n    // Porting Note: This is necessary because tfc.concat() requires axis to be\n    //   in the interval [-rank, rank).\n    axis = -1;\n  }\n  // Porting Note: Sparse concat is not supported yet.\n  return tfc.concat(tensors, axis);\n}\n\n/**\n * Concatenate two arrays along the first dimension.\n * @param a The 1st `tf.Tensor` to concatenate.\n * @param b The 2nd `tf.Tensor` to concatenate.\n * @returns Result 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If an Array, the length\n *   must be the same as the number of dimensions in `x`. If a single integer,\n *   it will be treated as an Array of length 1.\n */\nexport function tile(x: Tensor, n: number|number[]): Tensor {\n  if (!Array.isArray(n)) {\n    n = [n];\n  }\n  if (x.rank !== n.length) {\n    throw new ValueError(\n        `The length of input n (${n.length}) does not match ` +\n        `the number of dimensions in input x (${x.rank})`);\n  }\n  return tfc.tile(x, n);\n}\n\n/* Creation of random tensors. */\n\n/**\n * Get a tensor with normal distribution of values.\n *\n * @param shape Shape of the tensor.\n * @param mean mean value of the normal distribution.\n * @param stddev standard deviation of the normal distribution.\n * @param dtype\n * @param seed\n * @return The normal tensor.\n */\nexport function randomNormal(\n    shape: Shape, mean = 0.0, stddev = 1.0, dtype?: 'float32'|'int32',\n    seed?: number): Tensor {\n  return tfc.randomNormal(shape, mean, stddev, dtype, seed);\n}\n\n/* Linear Algebra */\n\n/**\n * Multiply two tensors and returns the result as a tensor.\n *\n * For 2D tensors, this is equivalent to matrix multiplication (matMul).\n * For tensors of higher ranks, it follows the Theano behavior,\n * (e.g. `(2, 3) * (4, 3, 5) -> (2, 4, 5)`).  From the Theano documentation:\n *\n * For N dimensions it is a sum product over the last axis of x and the\n * second-to-last of y:\n *\n * @param a A tensor of at least rank 2.\n * @param b A tensor of at least rank 2.\n * @param activation (optional) A string identifying the activation\n *   function.\n * @return Result of the dot operation.\n */\nexport function dot(\n    a: Tensor, b: Tensor, activation?: tfc.fused.Activation,\n    bias?: Tensor): Tensor {\n  if ((a.rank < 2) || (b.rank < 2)) {\n    throw new NotImplementedError(\n        `dot requires both inputs to be rank >= 2` +\n        ` but got x shape = ${a.shape} and y shape = ${b.shape}`);\n  }\n  if (b.rank >= 3) {\n    const xLastDim = a.shape.slice(-1)[0];\n    const ySecondLastDim = b.shape.slice(-2)[0];\n    if (xLastDim !== ySecondLastDim) {\n      throw new NotImplementedError(\n          `If rank y >= 3, then the second last dim` +\n          ` of y must equal the last dim of x but got x shape = ${\n              a.shape} and ` +\n          ` y shape = ${b.shape}`);\n    }\n  }\n  // Handle basic 2D x 2D case.\n  if ((a.rank === 2) && (b.rank === 2)) {\n    const transposeA = false;\n    const transposeB = false;\n    // tfc.fused.matMul only fuses certain activation functions. Unsupported\n    // activation functions are treated as 'linear' activations, which is\n    // equivalent to a no-op.\n    return tfc.fused.matMul({\n      a,\n      b: b as Tensor2D,\n      transposeA,\n      transposeB,\n      bias: bias ? reshapeBias(a.rank, bias, imageDataFormat()) : null,\n      activation\n    });\n  } else {\n    // Reshape x into the analogous 2D Tensor.\n    const aFirstDims = a.shape.slice();  // Holds all but the last dim of x.\n    const aLastDim = aFirstDims.pop();\n    a = tfc.reshape(a, [-1, aLastDim]);\n\n    // Reshape y into the analogous 2D Tensor, and keep track of the\n    // required dimensions to reproduce the output shape.\n    const bShape = b.shape.slice();\n    const bLastDim = bShape.pop();\n    const ySecondLastDim = bShape.pop();\n    const yOtherDims = [...bShape, bLastDim];\n    // permutation should be like [r-2, 0, 1, 2, ... r-4, r-3, r-1]\n    // where r is the rank of y.\n    const perm = Array.from({length: b.rank}, (_, i) => {\n      if (i === 0) {\n        return b.rank - 2;\n      } else if (i <= b.rank - 2) {\n        return i - 1;\n      }\n      return i;\n    });\n    b = tfc.reshape(tfc.transpose(b, perm), [ySecondLastDim, -1]);\n\n    // Multiply x and y as 2D Tensors, and then reshape back to original.\n    const outputShape = [...aFirstDims, ...yOtherDims];\n    const transposeA = false;\n    const transposeB = false;\n    return tfc.reshape(\n        tfc.fused.matMul({\n          a,\n          b,\n          transposeA,\n          transposeB,\n          bias: bias ? reshapeBias(a.rank, bias, imageDataFormat()) : null,\n          activation\n        }),\n        outputShape);\n  }\n}\n\n/**\n * Compute the sign Tensor of an input Tensor.\n *\n * Elements of the input `tf.Tensor` that are === 0 are mapped to 0.\n * Elements of the input `tf.Tensor` that are > 0 are mapped to 1.\n * Elements of the input `tf.Tensor` that are < 0 are mapped to -1.\n *\n * @param x Input `tf.Tensor`.\n * @return The sign `tf.Tensor`.\n */\nexport function sign(x: Tensor): Tensor {\n  // TODO(cais): Move to the core.\n  return tidy(() => {\n    const zerosLikeX = coreZerosLike(x);\n    const onesLikeX = coreOnesLike(x);\n    return where(\n        tfc.equal(x, zerosLikeX), zerosLikeX,\n        where(\n            tfc.greater(x, coreZerosLike(x)), onesLikeX,\n            tfc.mul(-1, onesLikeX)));\n  });\n}\n\n/**\n * Computes the one-hot representation of an integer tensor.\n * @param indices nD integer tensor of shape\n *   `(batch_size, dim1, dim2, ... dim(n-1))`\n * @param numClasses Integer, number of classes to consider.\n * @returns (n + 1)D one hot representation of the input\n *   with shape `(batch_size, dim1, dim2, ... dim(n-1), num_classes)`\n */\nexport function oneHot(indices: Tensor, numClasses: number): Tensor {\n  return tidy(() => {\n    if (indices.rank !== 1) {\n      throw new Error(\n          'Only 1D one-hot tensors are supported in the ' +\n          'deeplearn backend, at present.');\n    }\n    indices = tfc.cast(indices, 'int32');\n    return tfc.cast(tfc.oneHot(indices as Tensor1D, numClasses), 'float32');\n  });\n}\n\n/* Elementary math functions. */\n\n/**\n * Retrieves the elements of indices `indices` in the tensor `reference`.\n * @param reference A tensor.\n * @param indices An integer tensor of indices or an `Array` of integers.\n * @param axis Axis along which to perform the gather operation.\n * @returns The result of the gathering as a tensor.\n */\nexport function gather(\n    reference: Tensor, indices: number[]|Tensor1D, axis?: number): Tensor {\n  return tidy(() => {\n    if (Array.isArray(indices)) {\n      indices = tensor1d(indices, 'int32');\n    } else {\n      indices = tfc.cast(indices, 'int32');\n    }\n    return tfc.gather(reference, indices, axis);\n  });\n}\n\n/**\n * Element-wise square.\n * @param x Input tensor.\n * @return element-wise x^2\n */\nexport function square(x: Tensor): Tensor {\n  return tfc.mul(x, x);\n}\n\n/**\n * Element-wise exponentiation.\n *\n * Porting Note: In PyKeras, `a` (the exponent) is a Python integer, which\n *   takes advatnage of the backend's (e.g., TensorFlow's) automatic\n * conversion to tensor. Here we allow `a` to be either a number or a tensor.\n *\n * @param x The base tensor.\n * @param a The exponent, tensor or number. If a number, it is rounded to the\n *   nearest integer and converted to a tensor.\n * @returns A tensor of the same shape as `x`.\n */\nexport function pow(x: Tensor, a: Tensor|number): Tensor {\n  return tidy(() => {\n    if (typeof (a) === 'number') {\n      a = scalar(Math.round(a), 'int32');\n    }\n    if (a.dtype !== 'int32') {\n      throw new NotImplementedError(\n          `Non-int32 dtype (${a.dtype}) is not supported by pow() yet`);\n    }\n    return tfc.pow(x, a);\n  });\n}\n\n/**\n * Reshapes bias tensor according to rank of x.\n */\nfunction reshapeBias(xRank: number, bias: Tensor, dataFormat: string) {\n  const biasShape = bias.shape;\n\n  if (bias.rank !== 1 && bias.rank !== xRank) {\n    throw new ValueError(\n        `Unexpected bias dimensions: ${bias.rank}` +\n        `; expected it to be 1 or ${xRank}`);\n  }\n\n  if (xRank === 5) {\n    if (dataFormat === 'channelsFirst') {\n      if (biasShape.length === 1) {\n        return tfc.reshape(bias, [1, biasShape[0], 1, 1, 1]);\n      } else {\n        return tfc.reshape(\n            bias, [1, biasShape[3], biasShape[0], biasShape[1], biasShape[2]]);\n      }\n    } else if (dataFormat === 'channelsLast') {\n      if (biasShape.length === 1) {\n        return tfc.reshape(bias, [1, 1, 1, 1, biasShape[0]]);\n      } else {\n        return tfc.reshape(bias, [1].concat(biasShape));\n      }\n    }\n  } else if (xRank === 4) {\n    if (dataFormat === 'channelsFirst') {\n      if (biasShape.length === 1) {\n        return tfc.reshape(bias, [1, biasShape[0], 1, 1]);\n      } else {\n        return tfc.reshape(bias, [1, biasShape[2], biasShape[0], biasShape[1]]);\n      }\n    } else if (dataFormat === 'channelsLast') {\n      if (biasShape.length === 1) {\n        return tfc.reshape(bias, [1, 1, 1, biasShape[0]]);\n      } else {\n        return tfc.reshape(bias, [1].concat(biasShape));\n      }\n    }\n  } else if (xRank === 3) {\n    if (dataFormat === 'channelsFirst') {\n      if (biasShape.length === 1) {\n        return tfc.reshape(bias, [1, biasShape[0], 1]);\n      } else {\n        return tfc.reshape(bias, [1, biasShape[1], biasShape[0]]);\n      }\n    } else if (dataFormat === 'channelsLast') {\n      if (biasShape.length === 1) {\n        return tfc.reshape(bias, [1, 1, biasShape[0]]);\n      } else {\n        return tfc.reshape(bias, [1].concat(biasShape));\n      }\n    }\n  } else if (xRank < 3) {\n    return bias;\n  }\n  throw new ValueError(`Unsupported input rank by biasAdd: ${bias.rank}`);\n}\n\n/* Neural-network operations. */\n\n/**\n * Add a bias to a tensor.\n *\n * @param x The tensor to add the bias to.\n * @param bias The bias to add to `x`. Must be 1D or the same rank as `x`.\n * @return Result of the bias adding.\n * @throws ValueError: If the rank of `bias` is incorrect.\n */\nexport function biasAdd(\n    x: Tensor, bias: Tensor, dataFormat?: DataFormat): Tensor {\n  return tidy(() => {\n    if (dataFormat == null) {\n      dataFormat = imageDataFormat();\n    }\n    checkDataFormat(dataFormat);\n\n    return tfc.add(x, reshapeBias(x.rank, bias, dataFormat));\n  });\n}\n\n/**\n * Exponential linear unit (ELU).\n * @param x A tensor or variable to compute the activation function for.\n * @param alpha: A scalar, a scaling factor for the negative section.\n * @return Output of the ELU operation.\n */\nexport function elu(x: Tensor, alpha = 1): Tensor {\n  // TODO(cais): Add support for alpha values other than 1.\n  if (alpha !== 1) {\n    throw new NotImplementedError(\n        `Support for alpha values other than 1 (${alpha}) is not implemented ` +\n        `yet.`);\n  }\n  return tfc.elu(x);\n}\n\n/**\n * Softsign of a tensor.\n *\n * Defined as x / (abs(x) + 1), element-wise.\n *\n * @param x: Input.\n * @returns Output.\n */\nexport function softsign(x: Tensor): Tensor {\n  return tidy(() => tfc.div(x, tfc.add(tfc.abs(x), 1)));\n}\n\n/**\n * Sets entries in `x` to zero at random, while scaling the entire tensor.\n *\n * @param x input tensor.\n * @param level fraction of the entries in the tensor that will be set to 0.\n * @param noiseShape shape of randomly generated keep/drop flags, must be\n *   broadcastable to the shape of `x`. Optional.\n * @param seed random seed to ensure determinism. Optional.\n * @returns Result of the dropout operation.\n */\nexport function dropout(\n    x: Tensor, level: number, noiseShape?: number[], seed?: number): Tensor {\n  return tidy(() => tfc.dropout(x, level, noiseShape, seed));\n}\n\n/**\n * Element-wise, segment-wise linear approximation of sigmoid.\n *\n * Returns `0.` if `x < -2.5`, `1.` if `x > 2.5`.\n * In `-2.5 <= x <= 2.5`, returns `0.2 * x + 0.5`.\n *\n * @param x Input tensor.\n * @returns Output tensor.\n */\nexport function hardSigmoid(x: Tensor): Tensor {\n  return tidy(() => {\n    const y = tfc.add(.5, tfc.mul(.2, x));\n    return tfc.clipByValue(y, 0, 1);\n  });\n}\n\n/**\n * Invoke `x` in the training phase, and `alt` otherwise.\n *\n * Porting Note: We do not create placeholder tensors for the `training`\n * boolean flag here, because there is no such thing in the TF.js imperative\n * backend.\n *\n * @param x The function to invoke iff `training` is `true`.\n * @param alt The function to invoke iff `training` is `false`.\n * @param training Boolean flag for whether training phase is active.\n * @returns The return value of `x()` if `training` is `true`, or the return\n *   value of `alt()` if `training` is `false`.\n */\nexport function inTrainPhase<T>(x: () => T, alt: () => T, training = false): T {\n  return training ? x() : alt();\n}\n"]}
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