gx

chenyc

2025-02-12 ea42ff3ebee1eeb3fb29423aa848a249441db81c

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/**
 * @license
 * Copyright 2020 Google LLC. 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 { ENGINE } from '../engine';
import { OneHot } from '../kernel_names';
import { convertToTensor } from '../tensor_util_env';
import { op } from './operation';
/**
 * 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`.
 * `indices` used to encode prediction class must start from 0. For example,
 *  if you have 3 classes of data, class 1 should be encoded as 0, class 2
 *  should be 1, and class 3 should be 2.
 *
 * ```js
 * tf.oneHot(tf.tensor1d([0, 1], 'int32'), 3).print();
 * ```
 *
 * @param indices `tf.Tensor` of indices with dtype `int32`. Indices must
 * start from 0.
 * @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.
 * @param dtype The dtype of the output tensor, default to 'int32'.
 *
 * @doc {heading: 'Tensors', subheading: 'Creation'}
 */
function oneHot_(indices, depth, onValue = 1, offValue = 0, dtype = 'int32') {
    if (depth < 2) {
        throw new Error(`Error in oneHot: depth must be >=2, but it is ${depth}`);
    }
    const $indices = convertToTensor(indices, 'indices', 'oneHot', 'int32');
    const inputs = { indices: $indices };
    const attrs = { dtype, depth, onValue, offValue };
    return ENGINE.runKernel(OneHot, inputs, attrs);
}
export const oneHot = /* @__PURE__ */ op({ oneHot_ });
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