@tensorflow/tfjs-core
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Hardware-accelerated JavaScript library for machine intelligence
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JavaScript
;
/**
* @license
* Copyright 2020 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.
* =============================================================================
*/
Object.defineProperty(exports, "__esModule", { value: true });
var array_ops_1 = require("./array_ops");
var operation_1 = require("./operation");
var tile_1 = require("./tile");
/**
* 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, numColumns, batchShape, dtype) {
if (dtype === void 0) { dtype = 'float32'; }
if (numColumns == null) {
numColumns = numRows;
}
var buff = array_ops_1.buffer([numRows, numColumns], dtype);
var n = numRows <= numColumns ? numRows : numColumns;
for (var i = 0; i < n; ++i) {
buff.set(1, i, i);
}
var out = buff.toTensor().as2D(numRows, numColumns);
if (batchShape == null) {
return out;
}
else {
if (batchShape.length === 1) {
return tile_1.tile(array_ops_1.expandDims(out, 0), [batchShape[0], 1, 1]);
}
else if (batchShape.length === 2) {
return tile_1.tile(array_ops_1.expandDims(array_ops_1.expandDims(out, 0), 0), [batchShape[0], batchShape[1], 1, 1]);
}
else if (batchShape.length === 3) {
return tile_1.tile(array_ops_1.expandDims(array_ops_1.expandDims(array_ops_1.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.length + "D."));
}
}
}
exports.eye = operation_1.op({ eye_: eye_ });
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