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array_ops.ts
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array_ops.ts
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/**
* @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 {ENV} from '../environment';
import {Tensor, Tensor1D, Tensor2D, Tensor3D, Tensor4D, TensorBuffer} from '../tensor';
import {convertToTensor, convertToTensorArray} from '../tensor_util_env';
import {DataType, DataTypeMap, Rank, ShapeMap, TensorLike, TensorLike4D} from '../types';
import * as util from '../util';
import {getAxesPermutation, getInnerMostAxes} from './axis_util';
import {concat} from './concat_split';
import {op} from './operation';
import {MPRandGauss} from './rand';
import {zeros, zerosLike} from './tensor_ops';
/**
* Creates a new tensor with the same values and shape as the specified
* tensor.
*
* ```js
* const x = tf.tensor([1, 2]);
*
* x.clone().print();
* ```
*
* @param x The tensor to clone.
*/
/** @doc {heading: 'Tensors', subheading: 'Creation'} */
function clone_<T extends Tensor>(x: T|TensorLike): T {
const $x = convertToTensor(x, 'x', 'clone', null);
const der = (dy: T) => {
return {$x: () => dy.toFloat()};
};
return ENV.engine.runKernel(
backend =>
Tensor.make($x.shape, {dataId: $x.dataId}, $x.dtype) as T,
{$x}, der) as T;
}
/**
* Create an identity matrix.
*
* @param numRows Number of rows.
* @param numColumns Number of columns. Defaults to `numRows`.
* @param batchShape If provided, will add the batch shape to the beginning
* of the shape of the returned `tf.Tensor` by repeating the identity
* matrix.
* @param dtype Data type.
* @returns Identity matrix of the specified size and data type, possibly
* with batch repetition if `batchShape` is specified.
*/
/** @doc {heading: 'Tensors', subheading: 'Creation'} */
function eye_(
numRows: number, numColumns?: number,
batchShape?:
[
number
]|[number,
number]|[number, number, number]|[number, number, number, number],
dtype: DataType = 'float32'): Tensor2D {
if (numColumns == null) {
numColumns = numRows;
}
const buff = buffer([numRows, numColumns], dtype);
const n = numRows <= numColumns ? numRows : numColumns;
for (let i = 0; i < n; ++i) {
buff.set(1, i, i);
}
const out = buff.toTensor().as2D(numRows, numColumns);
if (batchShape == null) {
return out;
} else {
if (batchShape.length === 1) {
return tile(expandDims(out, 0), [batchShape[0], 1, 1]);
} else if (batchShape.length === 2) {
return tile(
expandDims(expandDims(out, 0), 0),
[batchShape[0], batchShape[1], 1, 1]);
} else if (batchShape.length === 3) {
return tile(
expandDims(expandDims(expandDims(out, 0), 0), 0),
[batchShape[0], batchShape[1], batchShape[2], 1, 1]);
} else {
throw new Error(
`eye() currently supports only 1D and 2D ` +
// tslint:disable-next-line:no-any
`batchShapes, but received ${(batchShape as any).length}D.`);
}
}
}
/**
* Creates a `tf.Tensor` with values sampled from a normal distribution.
*
* ```js
* tf.randomNormal([2, 2]).print();
* ```
*
* @param shape An array of integers defining the output tensor shape.
* @param mean The mean of the normal distribution.
* @param stdDev The standard deviation of the normal distribution.
* @param dtype The data type of the output.
* @param seed The seed for the random number generator.
*/
/** @doc {heading: 'Tensors', subheading: 'Random'} */
function randomNormal_<R extends Rank>(
shape: ShapeMap[R], mean = 0, stdDev = 1, dtype?: 'float32'|'int32',
seed?: number): Tensor<R> {
if (dtype != null && (dtype as DataType) === 'bool') {
throw new Error(`Unsupported data type ${dtype}`);
}
const randGauss =
new MPRandGauss(mean, stdDev, dtype, false /* truncated */, seed);
const res = buffer(shape, dtype);
for (let i = 0; i < res.values.length; i++) {
res.values[i] = randGauss.nextValue();
}
return res.toTensor();
}
/**
* Creates a `tf.Tensor` with values sampled from a truncated normal
* distribution.
*
* ```js
* tf.truncatedNormal([2, 2]).print();
* ```
*
* The generated values follow a normal distribution with specified mean and
* standard deviation, except that values whose magnitude is more than 2
* standard deviations from the mean are dropped and re-picked.
*
* @param shape An array of integers defining the output tensor shape.
* @param mean The mean of the normal distribution.
* @param stdDev The standard deviation of the normal distribution.
* @param dtype The data type of the output tensor.
* @param seed The seed for the random number generator.
*/
/** @doc {heading: 'Tensors', subheading: 'Creation'} */
function truncatedNormal_<R extends Rank>(
shape: ShapeMap[R], mean = 0, stdDev = 1, dtype?: 'float32'|'int32',
seed?: number): Tensor<R> {
if (dtype != null && (dtype as DataType) === 'bool') {
throw new Error(`Unsupported data type ${dtype}`);
}
const randGauss =
new MPRandGauss(mean, stdDev, dtype, true /* truncated */, seed);
const res = buffer(shape, dtype);
for (let i = 0; i < res.values.length; i++) {
res.values[i] = randGauss.nextValue();
}
return res.toTensor();
}
/**
* Creates a `tf.Tensor` with values sampled from a uniform distribution.
*
* The generated values follow a uniform distribution in the range [minval,
* maxval). The lower bound minval is included in the range, while the upper
* bound maxval is excluded.
*
* ```js
* tf.randomUniform([2, 2]).print();
* ```
*
* @param shape An array of integers defining the output tensor shape.
* @param minval The lower bound on the range of random values to generate.
* Defaults to 0.
* @param maxval The upper bound on the range of random values to generate.
* Defaults to 1.
* @param dtype The data type of the output tensor. Defaults to 'float32'.
*/
/** @doc {heading: 'Tensors', subheading: 'Random'} */
function randomUniform_<R extends Rank>(
shape: ShapeMap[R], minval = 0, maxval = 1,
dtype: DataType = 'float32'): Tensor<R> {
const res = buffer(shape, dtype);
for (let i = 0; i < res.values.length; i++) {
res.values[i] = util.randUniform(minval, maxval);
}
return res.toTensor();
}
/**
* Creates a `tf.Tensor` with values sampled from a random number generator
* function defined by the user.
*
* @param shape An array of integers defining the output tensor shape.
* @param randFunction A random number generator function which is called
* for each element in the output tensor.
* @param dtype The data type of the output tensor. Defaults to 'float32'.
*/
function rand_<R extends Rank>(
shape: ShapeMap[R], randFunction: () => number,
dtype?: DataType): Tensor<R> {
const size = util.sizeFromShape(shape);
let values = null;
if (dtype == null || dtype === 'float32') {
values = new Float32Array(size);
} else if (dtype === 'int32') {
values = new Int32Array(size);
} else if (dtype === 'bool') {
values = new Uint8Array(size);
} else {
throw new Error(`Unknown data type ${dtype}`);
}
for (let i = 0; i < size; i++) {
values[i] = randFunction();
}
return Tensor.make(shape, {values}, dtype);
}
/**
* Creates a `tf.Tensor` with values drawn from a multinomial distribution.
*
* ```js
* const probs = tf.tensor([.75, .25]);
* tf.multinomial(probs, 3).print();
* ```
*
* @param logits 1D array with unnormalized log-probabilities, or
* 2D array of shape `[batchSize, numOutcomes]`. See the `normalized`
* parameter.
* @param numSamples Number of samples to draw for each row slice.
* @param seed The seed number.
* @param normalized Whether the provided `logits` are normalized true
* probabilities (sum to 1). Defaults to false.
* @return 1D array of shape `[numSamples]`, or 2D array of shape
* `[batchSize, numSamples]`, depending on the rank of the input.
*/
/** @doc {heading: 'Tensors', subheading: 'Random'} */
function multinomial_(
logits: Tensor1D|Tensor2D|TensorLike, numSamples: number, seed?: number,
normalized = false): Tensor1D|Tensor2D {
const $logits = convertToTensor(logits, 'logits', 'multinomial');
const numOutcomes = $logits.size;
const origRank = $logits.rank;
if (numOutcomes < 2) {
throw new Error(
`Error in multinomial: you need at least 2 outcomes, but got ` +
`${numOutcomes}.`);
}
if (origRank > 2) {
throw new Error(`Rank of probabilities must be 1 or 2, but is ${origRank}`);
}
seed = seed || Math.random();
const logits2D = origRank === 1 ? $logits.as2D(1, -1) : $logits as Tensor2D;
const res = ENV.engine.runKernel(
backend => backend.multinomial(logits2D, normalized, numSamples, seed),
{logits2D});
return origRank === 1 ? res.as1D() : res;
}
/**
* Creates a one-hot `tf.Tensor`. The locations represented by `indices` take
* value `onValue` (defaults to 1), while all other locations take value
* `offValue` (defaults to 0). If `indices` is rank `R`, the output has rank
* `R+1` with the last axis of size `depth`.
*
* ```js
* tf.oneHot(tf.tensor1d([0, 1], 'int32'), 3).print();
* ```
*
* @param indices `tf.Tensor` of indices with dtype `int32`.
* @param depth The depth of the one hot dimension.
* @param onValue A number used to fill in the output when the index matches
* the location.
* @param offValue A number used to fill in the output when the index does
* not match the location.
*/
/** @doc {heading: 'Tensors', subheading: 'Creation'} */
function oneHot_(
indices: Tensor|TensorLike, depth: number, >
offValue = 0): Tensor {
if (depth < 2) {
throw new Error(`Error in oneHot: depth must be >=2, but it is ${depth}`);
}
let $indices = convertToTensor(indices, 'indices', 'oneHot', 'int32');
const outShape = [...$indices.shape, depth];
$indices = $indices.flatten();
const grad = (dy: Tensor2D) => {
return {$indices: () => zeros($indices.shape, 'float32')};
};
const result = ENV.engine.runKernel(
backend => backend.oneHot($indices as Tensor1D, depth, onValue, offValue),
{$indices}, grad);
return result.reshape(outShape);
}
/**
* Reshapes a `tf.Tensor` to a given shape.
*
* Given an input tensor, returns a new tensor with the same values as the
* input tensor with shape `shape`.
*
* If one component of shape is the special value -1, the size of that
* dimension is computed so that the total size remains constant. In
* particular, a shape of [-1] flattens into 1-D. At most one component of
* shape can be -1.
*
* If shape is 1-D or higher, then the operation returns a tensor with shape
* shape filled with the values of tensor. In this case, the number of
* elements implied by shape must be the same as the number of elements in
* tensor.
*
* ```js
* const x = tf.tensor1d([1, 2, 3, 4]);
* x.reshape([2, 2]).print();
* ```
*
* @param x The input tensor to be reshaped.
* @param shape An array of integers defining the output tensor shape.
*/
/** @doc {heading: 'Tensors', subheading: 'Transformations'} */
function reshape_<R2 extends Rank>(
x: Tensor|TensorLike, shape: ShapeMap[R2]): Tensor<R2> {
const $x = convertToTensor(x, 'x', 'reshape', null);
shape = util.inferFromImplicitShape(shape, $x.size);
util.assert(
$x.size === util.sizeFromShape(shape),
() => 'new shape and old shape must have the same number of elements.');
const grad = (dy: Tensor<R2>) => {
return {$x: () => dy.reshape($x.shape)};
};
return ENV.engine.runKernel(
backend => backend.reshape($x, shape), {$x}, grad);
}
/**
* Removes dimensions of size 1 from the shape of a `tf.Tensor`.
*
* ```js
* const x = tf.tensor([1, 2, 3, 4], [1, 1, 4]);
* x.squeeze().print();
* ```
*
* @param x The input tensor to be squeezed.
* @param axis An optional list of numbers. If specified, only
* squeezes the dimensions listed. The dimension index starts at 0. It
* is an error to squeeze a dimension that is not 1.
*/
/** @doc {heading: 'Tensors', subheading: 'Transformations'} */
function squeeze_<T extends Tensor>(x: Tensor|TensorLike, axis?: number[]): T {
const $x = convertToTensor(x, 'x', 'squeeze');
return reshape($x, util.squeezeShape($x.shape, axis).newShape) as T;
}
/**
* Casts a `tf.Tensor` to a new dtype.
*
* ```js
* const x = tf.tensor1d([1.5, 2.5, 3]);
* tf.cast(x, 'int32').print();
* ```
* @param x The input tensor to be casted.
* @param dtype The dtype to cast the input tensor to.
*/
/** @doc {heading: 'Tensors', subheading: 'Transformations'} */
function cast_<T extends Tensor>(x: T|TensorLike, dtype: DataType): T {
const $x = convertToTensor(x, 'x', 'cast');
const grad = (dy: T) => {
return {$x: () => dy.clone()};
};
return ENV.engine.runKernel(backend => backend.cast($x, dtype), {$x}, grad) as
T;
}
/**
* Construct a tensor by repeating it the number of times given by reps.
*
* This operation creates a new tensor by replicating `input` `reps`
* times. The output tensor's i'th dimension has `input.shape[i] *
* reps[i]` elements, and the values of `input` are replicated
* `reps[i]` times along the i'th dimension. For example, tiling
* `[a, b, c, d]` by `[2]` produces `[a, b, c, d, a, b, c, d]`.
*
* ```js
* const a = tf.tensor1d([1, 2]);
*
* a.tile([2]).print(); // or a.tile([2])
* ```
*
* ```js
* const a = tf.tensor2d([1, 2, 3, 4], [2, 2]);
*
* a.tile([1, 2]).print(); // or a.tile([1, 2])
* ```
* @param x The tensor to tile.
* @param reps Determines the number of replications per dimension.
*/
/** @doc {heading: 'Tensors', subheading: 'Slicing and Joining'} */
function tile_<T extends Tensor>(x: T|TensorLike, reps: number[]): T {
const $x = convertToTensor(x, 'x', 'tile');
util.assert(
$x.rank === reps.length,
() => `Error in transpose: rank of input ${$x.rank} ` +
`must match length of reps ${reps}.`);
const grad = (dy: T) => {
const derX = () => {
let xGrad = zerosLike($x);
// TODO(cais): Maybe reduce memory footprint by avoiding repeated
// slicing.
if ($x.rank === 1) {
for (let i = 0; i < reps[0]; ++i) {
xGrad = xGrad.add(dy.slice([i * $x.shape[0]], [$x.shape[0]]));
}
} else if ($x.rank === 2) {
for (let i = 0; i < reps[0]; ++i) {
for (let j = 0; j < reps[1]; ++j) {
xGrad = xGrad.add(dy.slice(
[i * $x.shape[0], j * $x.shape[1]],
[$x.shape[0], $x.shape[1]]));
}
}
} else if ($x.rank === 3) {
for (let i = 0; i < reps[0]; ++i) {
for (let j = 0; j < reps[1]; ++j) {
for (let k = 0; k < reps[2]; ++k) {
xGrad = xGrad.add(dy.slice(
[i * $x.shape[0], j * $x.shape[1], k * $x.shape[2]],
[$x.shape[0], $x.shape[1], $x.shape[2]]));
}
}
}
} else if ($x.rank === 4) {
for (let i = 0; i < reps[0]; ++i) {
for (let j = 0; j < reps[1]; ++j) {
for (let k = 0; k < reps[2]; ++k) {
for (let l = 0; l < reps[3]; ++l) {
xGrad = xGrad.add(dy.slice(
[
i * $x.shape[0], j * $x.shape[1], k * $x.shape[2],
l * $x.shape[3]
],
[$x.shape[0], $x.shape[1], $x.shape[2], $x.shape[3]]));
}
}
}
}
} else {
throw new Error(
`Gradient for tile operation is not implemented for rank-` +
`${$x.rank} tensors yet.`);
}
return xGrad;
};
return {$x: derX};
};
return ENV.engine.runKernel(backend => backend.tile($x, reps), {$x}, grad);
}
/**
* Pads a `tf.Tensor1D` with a given value and paddings. See `pad` for details.
*/
function pad1d_(
x: Tensor1D|TensorLike, paddings: [number, number],
constantValue = 0): Tensor1D {
util.assert(
paddings.length === 2,
() => 'Invalid number of paddings. Must be length of 2.');
return pad(x, [paddings], constantValue);
}
/**
* Pads a `tf.Tensor2D` with a given value and paddings. See `pad` for details.
*/
function pad2d_(
x: Tensor2D|TensorLike, paddings: [[number, number], [number, number]],
constantValue = 0): Tensor2D {
util.assert(
paddings.length === 2 && paddings[0].length === 2 &&
paddings[1].length === 2,
() => 'Invalid number of paddings. Must be length of 2 each.');
return pad(x, paddings, constantValue);
}
/**
* Pads a `tf.Tensor3D` with a given value and paddings. See `pad` for details.
*/
function pad3d_(
x: Tensor3D|TensorLike,
paddings: [[number, number], [number, number], [number, number]],
constantValue = 0): Tensor3D {
util.assert(
paddings.length === 3 && paddings[0].length === 2 &&
paddings[1].length === 2 && paddings[2].length === 2,
() => 'Invalid number of paddings. Must be length of 2 each.');
return pad(x, paddings, constantValue);
}
/**
* Pads a `tf.Tensor4D` with a given value and paddings. See `pad` for details.
*/
function pad4d_(
x: Tensor4D|TensorLike,
paddings:
[
[number, number], [number, number], [number, number], [number, number]
],
constantValue = 0): Tensor4D {
util.assert(
paddings.length === 4 && paddings[0].length === 2 &&
paddings[1].length === 2 && paddings[2].length === 2 &&
paddings[3].length === 2,
() => 'Invalid number of paddings. Must be length of 2 each.');
return pad(x, paddings, constantValue);
}
/**
* Pads a `tf.Tensor` with a given value and paddings.
*
* This operation currently only implements the `CONSTANT` mode.
*
* Also available are stricter rank-specific methods with the same signature
* as this method that assert that `paddings` is of given length.
* - `tf.pad1d`
* - `tf.pad2d`
* - `tf.pad3d`
* - `tf.pad4d`
*
* ```js
* const x = tf.tensor1d([1, 2, 3, 4]);
* x.pad([[1, 2]]).print();
* ```
* @param x The tensor to pad.
* @param paddings An array of length `R` (the rank of the tensor), where
* each element is a length-2 tuple of ints `[padBefore, padAfter]`,
* specifying how much to pad along each dimension of the tensor.
* @param constantValue The pad value to use. Defaults to 0.
*/
/** @doc {heading: 'Tensors', subheading: 'Transformations'} */
function pad_<T extends Tensor>(
x: T|TensorLike, paddings: Array<[number, number]>, constantValue = 0): T {
const $x = convertToTensor(x, 'x', 'pad');
if ($x.rank === 0) {
throw new Error('pad(scalar) is not defined. Pass non-scalar to pad');
}
// Pad introduces values around the original tensor, so the gradient
// slices the original shape out of the gradient.
const begin = paddings.map(p => p[0]);
const grad = (dy: T) => {
return {$x: () => dy.slice(begin, $x.shape)};
};
return ENV.engine.runKernel(
backend => backend.pad($x, paddings, constantValue), {$x}, grad) as
T;
}
/**
* Stacks a list of rank-`R` `tf.Tensor`s into one rank-`(R+1)` `tf.Tensor`.
*
* ```js
* const a = tf.tensor1d([1, 2]);
* const b = tf.tensor1d([3, 4]);
* const c = tf.tensor1d([5, 6]);
* tf.stack([a, b, c]).print();
* ```
*
* @param tensors A list of tensor objects with the same shape and dtype.
* @param axis The axis to stack along. Defaults to 0 (the first dim).
*/
/** @doc {heading: 'Tensors', subheading: 'Slicing and Joining'} */
function stack_<T extends Tensor>(
tensors: Array<T|TensorLike>, axis = 0): Tensor {
const $tensors = convertToTensorArray(tensors, 'tensors', 'stack');
util.assert(
$tensors.length >= 1, () => 'Pass at least one tensor to tf.stack');
if ($tensors.length === 1) {
return $tensors[0].expandDims(axis);
}
const rank = $tensors[0].rank;
const shape = $tensors[0].shape;
const dtype = $tensors[0].dtype;
util.assert(axis <= rank, () => 'Axis must be <= rank of the tensor');
$tensors.forEach(t => {
util.assertShapesMatch(
shape, t.shape,
'All tensors passed to stack must have matching shapes');
});
$tensors.forEach(t => {
util.assert(
dtype === t.dtype,
() => 'All tensors passed to stack must have matching dtypes');
});
const expandedTensors = $tensors.map(t => t.expandDims(axis));
return concat(expandedTensors, axis);
}
/**
* This operation reshapes the "batch" dimension 0 into `M + 1` dimensions of
* shape `blockShape + [batch]`, interleaves these blocks back into the grid
* defined by the spatial dimensions `[1, ..., M]`, to obtain a result with
* the same rank as the input. The spatial dimensions of this intermediate
* result are then optionally cropped according to `crops` to produce the
* output. This is the reverse of `tf.spaceToBatchND`. See below for a precise
* description.
*
* ```js
* const x = tf.tensor4d([1, 2, 3, 4], [4, 1, 1, 1]);
* const blockShape = [2, 2];
* const crops = [[0, 0], [0, 0]];
*
* x.batchToSpaceND(blockShape, crops).print();
* ```
*
* @param x A `tf.Tensor`. N-D with `x.shape` = `[batch] + spatialShape +
* remainingShape`, where spatialShape has `M` dimensions.
* @param blockShape A 1-D array. Must have shape `[M]`, all values must
* be >= 1.
* @param crops A 2-D array. Must have shape `[M, 2]`, all values must be >= 0.
* `crops[i] = [cropStart, cropEnd]` specifies the amount to crop from input
* dimension `i + 1`, which corresponds to spatial dimension `i`. It is required
* that `cropStart[i] + cropEnd[i] <= blockShape[i] * inputShape[i + 1]`
*
* This operation is equivalent to the following steps:
*
* 1. Reshape `x` to `reshaped` of shape: `[blockShape[0], ...,
* blockShape[M-1], batch / prod(blockShape), x.shape[1], ...,
* x.shape[N-1]]`
*
* 2. Permute dimensions of `reshaped`to produce `permuted` of shape `[batch /
* prod(blockShape),x.shape[1], blockShape[0], ..., x.shape[M],
* blockShape[M-1],x.shape[M+1], ..., x.shape[N-1]]`
*
* 3. Reshape `permuted` to produce `reshapedPermuted` of shape `[batch /
* prod(blockShape),x.shape[1] * blockShape[0], ..., x.shape[M] *
* blockShape[M-1],x.shape[M+1], ..., x.shape[N-1]]`
*
* 4. Crop the start and end of dimensions `[1, ..., M]` of `reshapedPermuted`
* according to `crops` to produce the output of shape: `[batch /
* prod(blockShape),x.shape[1] * blockShape[0] - crops[0,0] - crops[0,1],
* ..., x.shape[M] * blockShape[M-1] - crops[M-1,0] -
* crops[M-1,1],x.shape[M+1], ..., x.shape[N-1]]`
*/
/** @doc {heading: 'Tensors', subheading: 'Transformations'} */
function batchToSpaceND_<T extends Tensor>(
x: T|TensorLike, blockShape: number[], crops: number[][]): T {
const $x = convertToTensor(x, 'x', 'batchToSpaceND');
const prod = blockShape.reduce((a, b) => a * b);
util.assert(
$x.rank >= 1 + blockShape.length,
() => `input rank is ${$x.rank} but should be > than blockShape.length ${
blockShape.length}`);
util.assert(
crops.length === blockShape.length,
() => `crops.length is ${
crops.length} but should be equal to blockShape.length ${
blockShape.length}`);
util.assert(
$x.shape[0] % prod === 0,
() => `input tensor batch is ${
$x.shape[0]} but is not divisible by the product of ` +
`the elements of blockShape ${blockShape.join(' * ')} === ${prod}`);
const grad = (dy: T) => {
return {$x: () => dy.spaceToBatchND(blockShape, crops)};
};
return ENV.engine.runKernel(
backend => backend.batchToSpaceND($x, blockShape, crops), {$x}, grad);
}
/**
* This operation divides "spatial" dimensions `[1, ..., M]` of the input into
* a grid of blocks of shape `blockShape`, and interleaves these blocks with
* the "batch" dimension (0) such that in the output, the spatial
* dimensions `[1, ..., M]` correspond to the position within the grid,
* and the batch dimension combines both the position within a spatial block
* and the original batch position. Prior to division into blocks,
* the spatial dimensions of the input are optionally zero padded
* according to `paddings`. See below for a precise description.
*
* ```js
* const x = tf.tensor4d([1, 2, 3, 4], [1, 2, 2, 1]);
* const blockShape = [2, 2];
* const paddings = [[0, 0], [0, 0]];
*
* x.spaceToBatchND(blockShape, paddings).print();
* ```
*
* @param x A `tf.Tensor`. N-D with `x.shape` = `[batch] + spatialShape +
* remainingShape`, where spatialShape has `M` dimensions.
* @param blockShape A 1-D array. Must have shape `[M]`, all values must
* be >= 1.
* @param paddings A 2-D array. Must have shape `[M, 2]`, all values must be >=
* 0. `paddings[i] = [padStart, padEnd]` specifies the amount to zero-pad
* from input dimension `i + 1`, which corresponds to spatial dimension `i`. It
* is required that
* `(inputShape[i + 1] + padStart + padEnd) % blockShape[i] === 0`
*
* This operation is equivalent to the following steps:
*
* 1. Zero-pad the start and end of dimensions `[1, ..., M]` of the input
* according to `paddings` to produce `padded` of shape paddedShape.
*
* 2. Reshape `padded` to `reshapedPadded` of shape:
* `[batch] + [paddedShape[1] / blockShape[0], blockShape[0], ...,
* paddedShape[M] / blockShape[M-1], blockShape[M-1]] + remainingShape`
*
* 3. Permute dimensions of `reshapedPadded` to produce `permutedReshapedPadded`
* of shape: `blockShape + [batch] + [paddedShape[1] / blockShape[0], ...,
* paddedShape[M] / blockShape[M-1]] + remainingShape`
*
* 4. Reshape `permutedReshapedPadded` to flatten `blockShape` into the
* batch dimension, producing an output tensor of shape:
* `[batch * prod(blockShape)] + [paddedShape[1] / blockShape[0], ...,
* paddedShape[M] / blockShape[M-1]] + remainingShape`
*/
/** @doc {heading: 'Tensors', subheading: 'Transformations'} */
function spaceToBatchND_<T extends Tensor>(
x: T|TensorLike, blockShape: number[], paddings: number[][]): T {
const $x = convertToTensor(x, 'x', 'spaceToBatchND');
util.assert(
$x.rank >= 1 + blockShape.length,
() => `input rank ${$x.rank} should be > than [blockShape] ${
blockShape.length}`);
util.assert(
paddings.length === blockShape.length,
() => `paddings.shape[0] ${
paddings.length} must be equal to [blockShape] ${blockShape.length}`);
util.assert(
$x.shape.reduce(
(a, b, i) => {
if (i > 0 && i <= blockShape.length) {
return a &&
((b + paddings[i - 1][0] + paddings[i - 1][1]) %
blockShape[i - 1] ===
0);
}
return a;
},
true),
() => `input spatial dimensions ${$x.shape.slice(1)} with paddings ${
paddings.toString()} must be divisible by blockShapes ${
blockShape.toString()}`);
const grad = (dy: T) => {
return {$x: () => dy.batchToSpaceND(blockShape, paddings)};
};
return ENV.engine.runKernel(
backend => backend.spaceToBatchND($x, blockShape, paddings), {$x}, grad);
}
/**
* Unstacks a `tf.Tensor` of rank-`R` into a list of rank-`(R-1)` `tf.Tensor`s.
*
* ```js
* const a = tf.tensor2d([1, 2, 3, 4], [2, 2]);
*
* tf.unstack(a).forEach(tensor => tensor.print());
* ```
*
* @param x A tensor object.
* @param axis The axis to unstack along. Defaults to 0 (the first dim).
*/
/** @doc {heading: 'Tensors', subheading: 'Slicing and Joining'} */
function unstack_(x: Tensor|TensorLike, axis = 0): Tensor[] {
axis = axis || 0;
const $x = convertToTensor(x, 'x', 'unstack');
util.assert(
axis >= -$x.shape.length && axis < $x.shape.length,
() =>
`Axis = ${axis} is not in [-${$x.shape.length}, ${$x.shape.length})`);
if (axis < 0) {
axis += $x.shape.length;
}
const grad = (dy: Tensor[]) => {
return {$x: () => stack(dy, axis)};
};
return ENV.engine.runKernel(backend => backend.unstack($x, axis), {$x}, grad);
}
/**
* Computes the cumulative sum of a `tf.Tensor` along `axis`.
*
* ```js
* const x = tf.tensor([1, 2, 3, 4]);
* x.cumsum().print();
* ```
* ```js
* const x = tf.tensor([[1, 2], [3, 4]]);
* x.cumsum().print();
* ```
*
* @param x The input tensor to be summed.
* @param axis The axis along which to sum. Optional. Defaults to 0.
* @param exclusive Whether to perform exclusive cumulative sum. Optional.
* Defaults to false. If set to true then the sum of each tensor entry
* does not include its own value, but only the values previous to it
* along the specified axis.
* @param reverse Whether to sum in the opposite direction. Optional.
* Defaults to false.
*/
/** @doc {heading: 'Operations', subheading: 'Scan'} */
function cumsum_<T extends Tensor>(
x: Tensor|TensorLike, axis = 0, exclusive = false, reverse = false): T {
const $x = convertToTensor(x, 'x', 'cumsum');
axis = axis | 0;
const permutation = getAxesPermutation([axis], $x.rank);
let permutedX = $x;
if (permutation != null) {
permutedX = $x.transpose(permutation);
}
const permutedAxis = getInnerMostAxes(1, $x.rank)[0];
const grad = (dy: T) => {
return {permutedX: () => dy.cumsum(axis, exclusive, !reverse)};
};
let value = ENV.engine.runKernel(
backend => backend.cumsum(
permutedX, permutedAxis, exclusive, reverse),
{permutedX}, grad) as T;
if (permutation != null) {
value = value.transpose(permutation);
}
return value;
}
/**
* Returns a `tf.Tensor` that has expanded rank, by inserting a dimension
* into the tensor's shape.
*
* ```js
* const x = tf.tensor1d([1, 2, 3, 4]);
* const axis = 1;
* x.expandDims(axis).print();
* ```
*
* @param x The input tensor whose dimensions to be expanded.
* @param axis The dimension index at which to insert shape of `1`. Defaults
* to 0 (the first dimension).
*/
/** @doc {heading: 'Tensors', subheading: 'Transformations'} */
function expandDims_<R2 extends Rank>(
x: Tensor|TensorLike, axis = 0): Tensor<R2> {
const $x = convertToTensor(x, 'x', 'expandDims');
util.assert(axis <= $x.rank, () => 'Axis must be <= rank of the tensor');
const newShape = $x.shape.slice();
if (axis < 0) {
// Negative value is counted from the tail of rank.
util.assert(
-($x.rank + 1) <= axis,
() => `Axis must be in the interval [${- ($x.rank + 1)}, ${$x.rank}]`);
axis = $x.rank + axis + 1;
}
newShape.splice(axis, 0, 1);
return reshape($x, newShape);
}
/**
* Rearranges data from depth into blocks of spatial data. More specifically,
* this op outputs a copy of the input tensor where values from the `depth`
* dimension are moved in spatial blocks to the `height` and `width` dimensions.
* The attr `blockSize` indicates the input block size and how the data is
* moved.
*
* - Chunks of data of size `blockSize * blockSize` from depth are rearranged
* into non-overlapping blocks of size `blockSize x blockSize`
*
* - The width the output tensor is `inputWidth * blockSize`, whereas the
* height is `inputHeight * blockSize`
*
* - The Y, X coordinates within each block of the output image are determined
* by the high order component of the input channel index
*
* - The depth of the input tensor must be divisible by `blockSize *
* blockSize`
*
* The `dataFormat` attr specifies the layout of the input and output tensors
* with the following options: "NHWC": [ `batch, height, width, channels` ]
* "NCHW": [ `batch, channels, height, width` ]
*
* ```js
* const x = tf.tensor4d([1, 2, 3, 4], [1, 1, 1, 4]);
* const blockSize = 2;
* const dataFormat = "NHWC";
*
* tf.depthToSpace(x, blockSize, dataFormat).print();
* ```
*
* @param x The input tensor of rank 4
* @param blockSIze An `int` that is `>= 2`. The size of the spatial block
* @param dataFormat An optional string from: "NHWC", "NCHW". Defaults to "NHWC"
*/
/** @doc {heading: 'Tensors', subheading: 'Transformations'} */
function depthToSpace_(
x: Tensor4D|TensorLike4D, blockSize: number,
dataFormat: 'NHWC'|'NCHW' = 'NHWC'): Tensor4D {
const $x = convertToTensor(x, 'x', 'depthToSpace') as Tensor4D;
const inputHeight = (dataFormat === 'NHWC') ? $x.shape[1] : $x.shape[2];
const inputWidth = (dataFormat === 'NHWC') ? $x.shape[2] : $x.shape[3];
const inputDepth = (dataFormat === 'NHWC') ? $x.shape[3] : $x.shape[1];
util.assert(
inputHeight * blockSize >= 0,
() => `Negative dimension size caused by overflow when multiplying
${inputHeight} and ${blockSize} for depthToSpace with input shape
${$x.shape}`);
util.assert(
inputWidth * blockSize >= 0,
() => `Negative dimension size caused by overflow when multiplying
${inputWidth} and ${blockSize} for depthToSpace with input shape
${$x.shape}`);
util.assert(
(inputDepth % (blockSize * blockSize) === 0),
() => `Dimension size must be evenly divisible by ${
blockSize * blockSize} but is ${
inputDepth} for depthToSpace with input shape ${$x.shape}`);
return ENV.engine.runKernel(
backend => backend.depthToSpace($x, blockSize, dataFormat), {$x});
}
/**
* Computes the difference between two lists of numbers.
*
* Given a Tensor `x` and a Tensor `y`, this operation returns a Tensor `out`
* that represents all values that are in `x` but not in `y`. The returned
* Tensor `out` is sorted in the same order that the numbers appear in `x`
* (duplicates are preserved). This operation also returns a Tensor indices that
* represents the position of each out element in `x`. In other words:
*
* `out[i] = x[idx[i]] for i in [0, 1, ..., out.length - 1]`
*
* ```js
* const x = [1, 2, 3, 4, 5, 6];
* const y = [1, 3, 5];
*
* const [out, indices] = await tf.setdiff1dAsync(x, y);
* out.print(); // [2, 4, 6]
* indices.print(); // [1, 3, 5]
* ```
*
* @param x 1-D Tensor. Values to keep.
* @param y 1-D Tensor. Must have the same type as x. Values to exclude in the
* output.
* @returns Promise of Tensor tuple [out, indices].
* out: Tensor with the same type as x.
* indices: A Tensor of type int32.
*/
/** @doc {heading: 'Tensors', subheading: 'Transformations'} */
async function setdiff1dAsync_(
x: Tensor|TensorLike, y: Tensor|TensorLike): Promise<[Tensor, Tensor]> {
const $x = convertToTensor(x, 'x', 'setdiff1d');
const $y = convertToTensor(y, 'y', 'setdiff1d');
util.assert(
$x.dtype === $y.dtype,
() => `x and y should have the same dtype, but got x (${
$x.dtype}) and y (${$y.dtype}).`);
util.assert(
$x.rank === 1, () => `x should be 1D tensor, but got x (${$x.shape}).`);
util.assert(
$y.rank === 1, () => `y should be 1D tensor, but got y (${$y.shape}).`);
const xVals = await $x.data();
const yVals = await $y.data();
const ySet = new Set(yVals);