/**
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* @license
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* Copyright 2018 Google LLC. All Rights Reserved.
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* Licensed under the Apache License, Version 2.0 (the "License");
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* you may not use this file except in compliance with the License.
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* You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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* =============================================================================
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*/
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import { CustomCallback, LayersModel } from '@tensorflow/tfjs';
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type LogFunction = (message: string) => void;
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export declare const progressBarHelper: {
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ProgressBar: any;
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log: LogFunction;
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};
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/**
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* Terminal-based progress bar callback for tf.Model.fit().
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*/
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export declare class ProgbarLogger extends CustomCallback {
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private numTrainBatchesPerEpoch;
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private progressBar;
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private currentEpochBegin;
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private epochDurationMillis;
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private usPerStep;
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private batchesInLatestEpoch;
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private terminalWidth;
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private readonly RENDER_THROTTLE_MS;
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/**
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* Construtor of LoggingCallback.
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*/
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constructor();
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private formatLogsAsMetricsContent;
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private isFieldRelevant;
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}
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/**
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* Get a succint string representation of a number.
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*
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* Uses decimal notation if the number isn't too small.
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* Otherwise, use engineering notation.
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*
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* @param x Input number.
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* @return Succinct string representing `x`.
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*/
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export declare function getSuccinctNumberDisplay(x: number): string;
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/**
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* Determine the number of decimal places to display.
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*
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* @param x Number to display.
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* @return Number of decimal places to display for `x`.
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*/
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export declare function getDisplayDecimalPlaces(x: number): number;
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export interface TensorBoardCallbackArgs {
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/**
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* The frequency at which loss and metric values are written to logs.
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*
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* Currently supported options are:
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*
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* - 'batch': Write logs at the end of every batch of training, in addition
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* to the end of every epoch of training.
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* - 'epoch': Write logs at the end of every epoch of training.
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*
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* Note that writing logs too often slows down the training.
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*
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* Default: 'epoch'.
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*/
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updateFreq?: 'batch' | 'epoch';
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/**
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* The frequency (in epochs) at which to compute activation and weight
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* histograms for the layers of the model.
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*
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* If set to 0, histograms won't be computed.
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*
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* Validation data (or split) must be specified for histogram visualizations.
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*
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* Default: 0.
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*/
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histogramFreq?: number;
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}
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/**
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* Callback for logging to TensorBoard during training.
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*
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* Users are expected to access this class through the `tensorBoardCallback()`
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* factory method instead.
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*/
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export declare class TensorBoardCallback extends CustomCallback {
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readonly logdir: string;
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private model;
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private trainWriter;
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private valWriter;
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private batchesSeen;
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private readonly args;
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constructor(logdir?: string, args?: TensorBoardCallbackArgs);
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setModel(model: LayersModel): void;
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private logMetrics;
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private logWeights;
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private ensureTrainWriterCreated;
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private ensureValWriterCreated;
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}
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/**
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* Callback for logging to TensorBoard during training.
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*
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* Writes the loss and metric values (if any) to the specified log directory
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* (`logdir`) which can be ingested and visualized by TensorBoard.
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* This callback is usually passed as a callback to `tf.Model.fit()` or
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* `tf.Model.fitDataset()` calls during model training. The frequency at which
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* the values are logged can be controlled with the `updateFreq` field of the
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* configuration object (2nd argument).
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*
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* Usage example:
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* ```js
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* // Constructor a toy multilayer-perceptron regressor for demo purpose.
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* const model = tf.sequential();
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* model.add(
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* tf.layers.dense({units: 100, activation: 'relu', inputShape: [200]}));
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* model.add(tf.layers.dense({units: 1}));
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* model.compile({
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* loss: 'meanSquaredError',
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* optimizer: 'sgd',
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* metrics: ['MAE']
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* });
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*
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* // Generate some random fake data for demo purpose.
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* const xs = tf.randomUniform([10000, 200]);
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* const ys = tf.randomUniform([10000, 1]);
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* const valXs = tf.randomUniform([1000, 200]);
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* const valYs = tf.randomUniform([1000, 1]);
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*
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* // Start model training process.
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* await model.fit(xs, ys, {
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* epochs: 100,
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* validationData: [valXs, valYs],
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* // Add the tensorBoard callback here.
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* callbacks: tf.node.tensorBoard('/tmp/fit_logs_1')
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* });
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* ```
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*
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* Then you can use the following commands to point tensorboard
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* to the logdir:
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*
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* ```sh
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* pip install tensorboard # Unless you've already installed it.
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* tensorboard --logdir /tmp/fit_logs_1
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* ```
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*
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* @param logdir Directory to which the logs will be written.
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* @param args Optional configuration arguments.
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* @returns An instance of `TensorBoardCallback`, which is a subclass of
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* `tf.CustomCallback`.
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*
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* @doc {heading: 'TensorBoard', namespace: 'node'}
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*/
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export declare function tensorBoard(logdir?: string, args?: TensorBoardCallbackArgs): TensorBoardCallback;
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export {};
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