/**
 * @license
 * Copyright 2018 Google LLC
 *
 * Use of this source code is governed by an MIT-style
 * license that can be found in the LICENSE file or at
 * https://opensource.org/licenses/MIT.
 * =============================================================================
 */
/// <amd-module name="@tensorflow/tfjs-layers/dist/exports" />
/**
 * Exported functions.
 */
import { io } from '@tensorflow/tfjs-core';
import { BaseCallbackConstructor } from './base_callbacks';
import { ContainerArgs } from './engine/container';
import { InputConfig } from './engine/input_layer';
import { SymbolicTensor } from './engine/topology';
import { LayersModel } from './engine/training';
import { Sequential, SequentialArgs } from './models';
/**
 * A model is a data structure that consists of `Layers` and defines inputs
 * and outputs.
 *
 * The key difference between `tf.model` and `tf.sequential` is that
 * `tf.model` is more generic, supporting an arbitrary graph (without
 * cycles) of layers. `tf.sequential` is less generic and supports only a linear
 * stack of layers.
 *
 * When creating a `tf.LayersModel`, specify its input(s) and output(s). Layers
 * are used to wire input(s) to output(s).
 *
 * For example, the following code snippet defines a model consisting of
 * two `dense` layers, with 10 and 4 units, respectively.
 *
 * ```js
 * // Define input, which has a size of 5 (not including batch dimension).
 * const input = tf.input({shape: [5]});
 *
 * // First dense layer uses relu activation.
 * const denseLayer1 = tf.layers.dense({units: 10, activation: 'relu'});
 * // Second dense layer uses softmax activation.
 * const denseLayer2 = tf.layers.dense({units: 4, activation: 'softmax'});
 *
 * // Obtain the output symbolic tensor by applying the layers on the input.
 * const output = denseLayer2.apply(denseLayer1.apply(input));
 *
 * // Create the model based on the inputs.
 * const model = tf.model({inputs: input, outputs: output});
 *
 * // The model can be used for training, evaluation and prediction.
 * // For example, the following line runs prediction with the model on
 * // some fake data.
 * model.predict(tf.ones([2, 5])).print();
 * ```
 * See also:
 *   `tf.sequential`, `tf.loadLayersModel`.
 *
 * @doc {heading: 'Models', subheading: 'Creation'}
 */
export declare function model(args: ContainerArgs): LayersModel;
/**
 * Creates a `tf.Sequential` model.  A sequential model is any model where the
 * outputs of one layer are the inputs to the next layer, i.e. the model
 * topology is a simple 'stack' of layers, with no branching or skipping.
 *
 * This means that the first layer passed to a `tf.Sequential` model should have
 * a defined input shape. What that means is that it should have received an
 * `inputShape` or `batchInputShape` argument, or for some type of layers
 * (recurrent, Dense...) an `inputDim` argument.
 *
 * The key difference between `tf.model` and `tf.sequential` is that
 * `tf.sequential` is less generic, supporting only a linear stack of layers.
 * `tf.model` is more generic and supports an arbitrary graph (without
 * cycles) of layers.
 *
 * Examples:
 *
 * ```js
 * const model = tf.sequential();
 *
 * // First layer must have an input shape defined.
 * model.add(tf.layers.dense({units: 32, inputShape: [50]}));
 * // Afterwards, TF.js does automatic shape inference.
 * model.add(tf.layers.dense({units: 4}));
 *
 * // Inspect the inferred shape of the model's output, which equals
 * // `[null, 4]`. The 1st dimension is the undetermined batch dimension; the
 * // 2nd is the output size of the model's last layer.
 * console.log(JSON.stringify(model.outputs[0].shape));
 * ```
 *
 * It is also possible to specify a batch size (with potentially undetermined
 * batch dimension, denoted by "null") for the first layer using the
 * `batchInputShape` key. The following example is equivalent to the above:
 *
 * ```js
 * const model = tf.sequential();
 *
 * // First layer must have a defined input shape
 * model.add(tf.layers.dense({units: 32, batchInputShape: [null, 50]}));
 * // Afterwards, TF.js does automatic shape inference.
 * model.add(tf.layers.dense({units: 4}));
 *
 * // Inspect the inferred shape of the model's output.
 * console.log(JSON.stringify(model.outputs[0].shape));
 * ```
 *
 * You can also use an `Array` of already-constructed `Layer`s to create
 * a `tf.Sequential` model:
 *
 * ```js
 * const model = tf.sequential({
 *   layers: [tf.layers.dense({units: 32, inputShape: [50]}),
 *            tf.layers.dense({units: 4})]
 * });
 * console.log(JSON.stringify(model.outputs[0].shape));
 * ```
 *
 * @doc {heading: 'Models', subheading: 'Creation'}
 */
export declare function sequential(config?: SequentialArgs): Sequential;
/**
 * Load a model composed of Layer objects, including its topology and optionally
 * weights. See the Tutorial named "How to import a Keras Model" for usage
 * examples.
 *
 * This method is applicable to:
 *
 * 1. Models created with the `tf.layers.*`, `tf.sequential`, and
 * `tf.model` APIs of TensorFlow.js and later saved with the
 * `tf.LayersModel.save` method.
 * 2. Models converted from Keras or TensorFlow tf.keras using the
 * [tensorflowjs_converter](https://github.com/tensorflow/tfjs/tree/master/tfjs-converter).
 *
 * This mode is *not* applicable to TensorFlow `SavedModel`s or their converted
 * forms. For those models, use `tf.loadGraphModel`.
 *
 * Example 1. Load a model from an HTTP server.
 *
 * ```js
 * const model = await tf.loadLayersModel(
 *     'https://storage.googleapis.com/tfjs-models/tfjs/iris_v1/model.json');
 * model.summary();
 * ```
 *
 * Example 2: Save `model`'s topology and weights to browser [local
 * storage](https://developer.mozilla.org/en-US/docs/Web/API/Window/localStorage);
 * then load it back.
 *
 * ```js
 * const model = tf.sequential(
 *     {layers: [tf.layers.dense({units: 1, inputShape: [3]})]});
 * console.log('Prediction from original model:');
 * model.predict(tf.ones([1, 3])).print();
 *
 * const saveResults = await model.save('localstorage://my-model-1');
 *
 * const loadedModel = await tf.loadLayersModel('localstorage://my-model-1');
 * console.log('Prediction from loaded model:');
 * loadedModel.predict(tf.ones([1, 3])).print();
 * ```
 *
 * Example 3. Saving `model`'s topology and weights to browser
 * [IndexedDB](https://developer.mozilla.org/en-US/docs/Web/API/IndexedDB_API);
 * then load it back.
 *
 * ```js
 * const model = tf.sequential(
 *     {layers: [tf.layers.dense({units: 1, inputShape: [3]})]});
 * console.log('Prediction from original model:');
 * model.predict(tf.ones([1, 3])).print();
 *
 * const saveResults = await model.save('indexeddb://my-model-1');
 *
 * const loadedModel = await tf.loadLayersModel('indexeddb://my-model-1');
 * console.log('Prediction from loaded model:');
 * loadedModel.predict(tf.ones([1, 3])).print();
 * ```
 *
 * Example 4. Load a model from user-selected files from HTML
 * [file input
 * elements](https://developer.mozilla.org/en-US/docs/Web/HTML/Element/input/file).
 *
 * ```js
 * // Note: this code snippet will not work without the HTML elements in the
 * //   page
 * const jsonUpload = document.getElementById('json-upload');
 * const weightsUpload = document.getElementById('weights-upload');
 *
 * const model = await tf.loadLayersModel(
 *     tf.io.browserFiles([jsonUpload.files[0], weightsUpload.files[0]]));
 * ```
 *
 * @param pathOrIOHandler Can be either of the two formats
 *   1. A string path to the `ModelAndWeightsConfig` JSON describing
 *      the model in the canonical TensorFlow.js format. For file://
 *      (tfjs-node-only), http:// and https:// schemas, the path can be
 *      either absolute or relative.
 *   2. An `tf.io.IOHandler` object that loads model artifacts with its `load`
 *      method.
 * @param options Optional configuration arguments for the model loading,
 *   including:
 *   - `strict`: Require that the provided weights exactly match those required
 *     by the layers.  Default true.  Passing false means that both extra
 *     weights and missing weights will be silently ignored.
 *   - `onProgress`: A function of the signature `(fraction: number) => void',
 *     that can be used as the progress callback for the model loading.
 * @returns A `Promise` of `tf.LayersModel`, with the topology and weights
 *     loaded.
 *
 * @doc {heading: 'Models', subheading: 'Loading'}
 */
export declare function loadLayersModel(pathOrIOHandler: string | io.IOHandler, options?: io.LoadOptions): Promise<LayersModel>;
/**
 * Used to instantiate an input to a model as a `tf.SymbolicTensor`.
 *
 * Users should call the `input` factory function for
 * consistency with other generator functions.
 *
 * Example:
 *
 * ```js
 * // Defines a simple logistic regression model with 32 dimensional input
 * // and 3 dimensional output.
 * const x = tf.input({shape: [32]});
 * const y = tf.layers.dense({units: 3, activation: 'softmax'}).apply(x);
 * const model = tf.model({inputs: x, outputs: y});
 * model.predict(tf.ones([2, 32])).print();
 * ```
 *
 * Note: `input` is only necessary when using `model`. When using
 * `sequential`, specify `inputShape` for the first layer or use `inputLayer`
 * as the first layer.
 *
 * @doc {heading: 'Models', subheading: 'Inputs'}
 */
export declare function input(config: InputConfig): SymbolicTensor;
export declare function registerCallbackConstructor(verbosityLevel: number, callbackConstructor: BaseCallbackConstructor): void;