/// <reference types="node" />
/**
 * @license
 * Copyright 2018 Google LLC
 *
 * 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
 *
 *     https://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 'isomorphic-fetch';
import * as tfc from '@tensorflow/tfjs-core';
import { FrozenModel } from '@tensorflow/tfjs-converter';
import { EmojiId, EmojiName } from './emoji-classes';
declare const MOBILENET_SIZE = 224;
interface PredictItem {
    id: EmojiId;
    name: EmojiName;
    probobility: number;
}
declare class EmojiNet {
    model?: FrozenModel;
    load(): Promise<void>;
    dispose(): void;
    recognize(src: string | Buffer): Promise<PredictItem[]>;
    /**
     * Infer through MobileNet, assumes variables have been loaded. This does
     * standard ImageNet pre-processing before inferring through the model. This
     * method returns named activations as well as softmax logits.
     *
     * @param input un-preprocessed input Array.
     * @return The softmax logits.
     */
    protected predict(input: tfc.Tensor): tfc.Tensor1D;
    protected getTopKClasses(predictions: tfc.Tensor1D, topK: number): PredictItem[];
}
export { MOBILENET_SIZE, EmojiNet, };
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