@tensorflow/tfjs-layers/dist/models.d.ts

/**
 * @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/models" />
import { io, Optimizer, Scalar, serialization, Tensor } from '@tensorflow/tfjs-core';
import { History } from './base_callbacks';
import { Dataset } from './engine/dataset_stub';
import { Layer } from './engine/topology';
import { LayersModel, ModelCompileArgs, ModelEvaluateArgs } from './engine/training';
import { ModelEvaluateDatasetArgs, ModelFitDatasetArgs } from './engine/training_dataset';
import { ModelFitArgs } from './engine/training_tensors';
import { Shape } from './keras_format/common';
import { PyJsonDict } from './keras_format/types';
import { Kwargs } from './types';
/**
 * Parses a JSON model configuration file and returns a model instance.
 *
 * ```js
 * // This example shows how to serialize a model using `toJSON()` and
 * // deserialize it as another model using `tf.models.modelFromJSON()`.
 * // Note: this example serializes and deserializes only the topology
 * // of the model; the weights of the loaded model will be different
 * // from those of the the original model, due to random weight
 * // initialization.
 * // To load the topology and weights of a model, use `tf.loadLayersModel()`.
 * const model1 = tf.sequential();
 * model1.add(tf.layers.repeatVector({inputShape: [2], n: 4}));
 * // Serialize `model1` as a JSON object.
 * const model1JSON = model1.toJSON(null, false);
 * model1.summary();
 *
 * const model2 = await tf.models.modelFromJSON(model1JSON);
 * model2.summary();
 * ```
 *
 *  @param modelAndWeightsConfig JSON object or string encoding a model and
 *       weights configuration. It can also be only the topology JSON of the
 *       model, in which case the weights will not be loaded.
 *  @param custom_objects Optional dictionary mapping names
 *       (strings) to custom classes or functions to be
 *       considered during deserialization.
 * @returns A TensorFlow.js Layers `tf.LayersModel` instance (uncompiled).
 */
export declare function modelFromJSON(modelAndWeightsConfig: ModelAndWeightsConfig | PyJsonDict, customObjects?: serialization.ConfigDict): Promise<LayersModel>;
/**
 * Options for loading a saved mode in TensorFlow.js format.
 */
export interface ModelAndWeightsConfig {
    /**
     * A JSON object or JSON string containing the model config.
     *
     * This can be either of the following two formats:
     *   - A model archiecture-only config,  i.e., a format consistent with the
     *     return value of`keras.Model.to_json()`.
     *   - A full model config, containing not only model architecture, but also
     *     training options and state, i.e., a format consistent with the return
     *     value of `keras.models.save_model()`.
     */
    modelTopology: PyJsonDict;
    /**
     * A weights manifest in TensorFlow.js format.
     */
    weightsManifest?: io.WeightsManifestConfig;
    /**
     * Path to prepend to the paths in `weightManifest` before fetching.
     *
     * The path may optionally end in a slash ('/').
     */
    pathPrefix?: string;
}
export interface ModelPredictArgs {
    /**
     * Optional. Batch size (Integer). If unspecified, it will default to 32.
     */
    batchSize?: number;
    /**
     * Optional. Verbosity mode. Defaults to false.
     */
    verbose?: boolean;
}
/**
 * Load a model, including its topology and optionally weights.  See the
 * Tutorial named "How to import a Keras Model" for usage examples.
 *
 * Example 1: 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 2. 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 3. 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]]));
 * ```
 *
 * Example 4. 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();
 * ```
 *
 * @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. This path will be
 *      interpreted as a relative HTTP path, to which `fetch` will be used to
 *      request the model topology and weight manifest JSON.
 *      The content of the JSON file is assumed to be a JSON object with the
 *      following fields and values:
 *      - 'modelTopology': A JSON object that can be either of:
 *        1. a model architecture JSON consistent with the format of the return
 *            value of `keras.Model.to_json()`
 *        2. a full model JSON in the format of `keras.models.save_model()`.
 *      - 'weightsManifest': A TensorFlow.js weights manifest.
 *      See the Python converter function `save_model()` for more details.
 *      It is also assumed that model weights can be accessed from relative
 *      paths described by the `paths` fields in weights manifest.
 *   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 progress callback of the form:
 *     `(fraction: number) => void`. This callback can be used to monitor the
 *     model-loading process.
 * @returns A `Promise` of `tf.LayersModel`, with the topology and weights
 *     loaded.
 */
export declare function loadLayersModelInternal(pathOrIOHandler: string | io.IOHandler, options?: io.LoadOptions): Promise<LayersModel>;
/**
 * Load a model and optionally its weights, using an IOHandler object.
 *
 * @param handler The instance of `IOHandler` to be used during the model
 *   loading.
 * @param customObjects Any optional custom objects to be used during model
 *   loading.
 * @param strict Whether the weight loading will be done in strict mode.
 *   Default: `true`.
 */
export declare function loadLayersModelFromIOHandler(handler: io.IOHandler, customObjects?: serialization.ConfigDict, options?: io.LoadOptions): Promise<LayersModel>;
/**
 * Configuration for a Sequential model.
 */
export interface SequentialArgs {
    /** Stack of layers for the model. */
    layers?: Layer[];
    /** The name of this model. */
    name?: string;
}
/**
 * A model with a stack of layers, feeding linearly from one to the next.
 *
 * `tf.sequential` is a factory function that creates an instance of
 * `tf.Sequential`.
 *
 * ```js
 *  // Define a model for linear regression.
 *  const model = tf.sequential();
 *  model.add(tf.layers.dense({units: 1, inputShape: [1]}));
 *
 *  // Prepare the model for training: Specify the loss and the optimizer.
 *  model.compile({loss: 'meanSquaredError', optimizer: 'sgd'});
 *
 *  // Generate some synthetic data for training.
 *  const xs = tf.tensor2d([1, 2, 3, 4], [4, 1]);
 *  const ys = tf.tensor2d([1, 3, 5, 7], [4, 1]);
 *
 *  // Train the model using the data then do inference on a data point the
 *  // model hasn't seen:
 *  await model.fit(xs, ys);
 *  model.predict(tf.tensor2d([5], [1, 1])).print();
 * ```
 *
 * @doc {heading: 'Models', subheading: 'Classes'}
 */
export declare class Sequential extends LayersModel {
    /** @nocollapse */
    static className: string;
    private model;
    constructor(args?: SequentialArgs);
    private checkShape;
    /**
     * Adds a layer instance on top of the layer stack.
     *
     * ```js
     *  const model = tf.sequential();
     *  model.add(tf.layers.dense({units: 8, inputShape: [1]}));
     *  model.add(tf.layers.dense({units: 4, activation: 'relu6'}));
     *  model.add(tf.layers.dense({units: 1, activation: 'relu6'}));
     *  // Note that the untrained model is random at this point.
     *  model.predict(tf.randomNormal([10, 1])).print();
     * ```
     * @param layer Layer instance.
     *
     * @exception ValueError In case the `layer` argument does not know its
     * input shape.
     * @exception ValueError In case the `layer` argument has multiple output
     *   tensors, or is already connected somewhere else (forbidden in
     *   `Sequential` models).
     *
     * @doc {heading: 'Models', subheading: 'Classes'}
     */
    add(layer: Layer): void;
    /**
     * Removes the last layer in the model.
     *
     * @exception TypeError if there are no layers in the model.
     */
    pop(): void;
    call(inputs: Tensor | Tensor[], kwargs: Kwargs): Tensor | Tensor[];
    build(inputShape?: Shape | Shape[]): void;
    countParams(): number;
    /**
     * Print a text summary of the Sequential model's layers.
     *
     * The summary includes
     * - Name and type of all layers that comprise the model.
     * - Output shape(s) of the layers
     * - Number of weight parameters of each layer
     * - The total number of trainable and non-trainable parameters of the
     * model.
     *
     * ```js
     * const model = tf.sequential();
     * model.add(
     *     tf.layers.dense({units: 100, inputShape: [10], activation: 'relu'}));
     * model.add(tf.layers.dense({units: 1, activation: 'sigmoid'}));
     *
     * model.summary();
     * ```
     *
     * @param lineLength Custom line length, in number of characters.
     * @param positions Custom widths of each of the columns, as either
     *   fractions of `lineLength` (e.g., `[0.5, 0.75, 1]`) or absolute number
     *   of characters (e.g., `[30, 50, 65]`). Each number corresponds to
     *   right-most (i.e., ending) position of a column.
     * @param printFn Custom print function. Can be used to replace the default
     *   `console.log`. For example, you can use `x => {}` to mute the printed
     *   messages in the console.
     *
     * @doc {heading: 'Models', subheading: 'Classes'}
     */
    summary(lineLength?: number, positions?: number[], printFn?: (message?: any, ...optionalParams: any[]) => void): void;
    /**
     * Sets the weights of the model.
     *
     * @param weights Should be a list of Tensors with shapes and types matching
     *   the output of `model.getWeights()`.
     */
    setWeights(weights: Tensor[]): void;
    /**
     * Returns the loss value & metrics values for the model in test mode.
     *
     * Loss and metrics are specified during `compile()`, which needs to happen
     * before calls to `evaluate()`.
     *
     * Computation is done in batches.
     *
     * ```js
     * const model = tf.sequential({
     *   layers: [tf.layers.dense({units: 1, inputShape: [10]})]
     * });
     * model.compile({optimizer: 'sgd', loss: 'meanSquaredError'});
     * const result = model.evaluate(tf.ones([8, 10]), tf.ones([8, 1]), {
     *   batchSize: 4,
     * });
     * result.print();
     * ```
     *
     * @param x `tf.Tensor` of test data, or an `Array` of `tf.Tensor`s if the
     * model has multiple inputs.
     * @param y `tf.Tensor` of target data, or an `Array` of `tf.Tensor`s if the
     * model has multiple outputs.
     * @param args A `ModelEvaluateConfig`, containing optional fields.
     *
     * @return `Scalar` test loss (if the model has a single output and no
     *   metrics) or `Array` of `Scalar`s (if the model has multiple outputs
     *   and/or metrics). The attribute `model.metricsNames`
     *   will give you the display labels for the scalar outputs.
     *
     * @doc {heading: 'Models', subheading: 'Classes'}
     */
    evaluate(x: Tensor | Tensor[], y: Tensor | Tensor[], args?: ModelEvaluateArgs): Scalar | Scalar[];
    /**
     * Evaluate model using a dataset object.
     *
     * Note: Unlike `evaluate()`, this method is asynchronous (`async`);
     *
     * @param dataset A dataset object. Its `iterator()` method is expected
     *   to generate a dataset iterator object, the `next()` method of which
     *   is expected to produce data batches for evaluation. The return value
     *   of the `next()` call ought to contain a boolean `done` field and a
     *   `value` field. The `value` field is expected to be an array of two
     *   `tf.Tensor`s or an array of two nested `tf.Tensor` structures. The former
     *   case is for models with exactly one input and one output (e.g..
     *   a sequential model). The latter case is for models with multiple
     *   inputs and/or multiple outputs. Of the two items in the array, the
     *   first is the input feature(s) and the second is the output target(s).
     * @param args A configuration object for the dataset-based evaluation.
     * @returns Loss and metric values as an Array of `Scalar` objects.
     *
     * @doc {heading: 'Models', subheading: 'Classes'}
     */
    evaluateDataset(dataset: Dataset<{}>, args: ModelEvaluateDatasetArgs): Promise<Scalar | Scalar[]>;
    /**
     * Generates output predictions for the input samples.
     *
     * Computation is done in batches.
     *
     * Note: the "step" mode of predict() is currently not supported.
     *   This is because the TensorFlow.js core backend is imperative only.
     *
     * ```js
     * const model = tf.sequential({
     *   layers: [tf.layers.dense({units: 1, inputShape: [10]})]
     * });
     * model.predict(tf.ones([2, 10])).print();
     * ```
     *
     * @param x The input data, as a Tensor, or an `Array` of `tf.Tensor`s if
     *   the model has multiple inputs.
     * @param conifg A `ModelPredictConfig` object containing optional fields.
     *
     * @return `tf.Tensor`(s) of predictions.
     *
     * @exception ValueError In case of mismatch between the provided input data
     *   and the model's expectations, or in case a stateful model receives a
     *   number of samples that is not a multiple of the batch size.
     *
     * @doc {heading: 'Models', subheading: 'Classes'}
     */
    predict(x: Tensor | Tensor[], args?: ModelPredictArgs): Tensor | Tensor[];
    /**
     * Returns predictions for a single batch of samples.
     *
     * @param x: Input samples, as a Tensor, or list of Tensors (if the model
     *   has multiple inputs).
     * @return Tensor(s) of predictions
     */
    predictOnBatch(x: Tensor): Tensor | Tensor[];
    /**
     * See `LayersModel.compile`.
     *
     * @param args
     */
    compile(args: ModelCompileArgs): void;
    optimizer: Optimizer;
    /**
     * Trains the model for a fixed number of epochs (iterations on a dataset).
     *
     * ```js
     * const model = tf.sequential({
     *   layers: [tf.layers.dense({units: 1, inputShape: [10]})]
     * });
     * model.compile({optimizer: 'sgd', loss: 'meanSquaredError'});
     * const history = await model.fit(tf.ones([8, 10]), tf.ones([8, 1]), {
     *   batchSize: 4,
     *   epochs: 3
     * });
     * console.log(history.history.loss[0]);
     * ```
     *
     * @param x `tf.Tensor` of training data, or an array of `tf.Tensor`s if the
     * model has multiple inputs. If all inputs in the model are named, you can
     * also pass a dictionary mapping input names to `tf.Tensor`s.
     * @param y `tf.Tensor` of target (label) data, or an array of `tf.Tensor`s if
     * the model has multiple outputs. If all outputs in the model are named, you
     *  can also pass a dictionary mapping output names to `tf.Tensor`s.
     * @param args  A `ModelFitConfig`, containing optional fields.
     *
     * @return A `History` instance. Its `history` attribute contains all
     *   information collected during training.
     *
     * @exception ValueError In case of mismatch between the provided input data
     *   and what the model expects.
     *
     * @doc {heading: 'Models', subheading: 'Classes'}
     */
    fit(x: Tensor | Tensor[] | {
        [inputName: string]: Tensor;
    }, y: Tensor | Tensor[] | {
        [inputName: string]: Tensor;
    }, args?: ModelFitArgs): Promise<History>;
    /**
     * Trains the model using a dataset object.
     *
     * ```js
     * const xArray = [
     *   [1, 1, 1, 1, 1, 1, 1, 1, 1],
     *   [1, 1, 1, 1, 1, 1, 1, 1, 1],
     *   [1, 1, 1, 1, 1, 1, 1, 1, 1],
     *   [1, 1, 1, 1, 1, 1, 1, 1, 1],
     * ];
     * const yArray = [1, 1, 1, 1];
     * // Create a dataset from the JavaScript array.
     * const xDataset = tf.data.array(xArray);
     * const yDataset = tf.data.array(yArray);
     * // Zip combines the `x` and `y` Datasets into a single Dataset, the
     * // iterator of which will return an object containing of two tensors,
     * // corresponding to `x` and `y`.  The call to `batch(4)` will bundle
     * // four such samples into a single object, with the same keys now pointing
     * // to tensors that hold 4 examples, organized along the batch dimension.
     * // The call to `shuffle(4)` causes each iteration through the dataset to
     * // happen in a different order.  The size of the shuffle window is 4.
     * const xyDataset = tf.data.zip({xs: xDataset, ys: yDataset})
     *     .batch(4)
     *     .shuffle(4);
     * const model = tf.sequential({
     *   layers: [tf.layers.dense({units: 1, inputShape: [9]})]
     * });
     * model.compile({optimizer: 'sgd', loss: 'meanSquaredError'});
     * const history = await model.fitDataset(xyDataset, {
     *   epochs: 4,
     *   callbacks: {onEpochEnd: (epoch, logs) => console.log(logs.loss)}
     * });
     * ```
     *
     * @param dataset A dataset object. Its `iterator()` method is expected to
     *   generate a dataset iterator object, the `next()` method of which is
     *   expected to produce data batches for evaluation. The return value of the
     *   `next()` call ought to contain a boolean `done` field and a `value`
     *   field.
     *
     *   The `value` field is expected to be an object of with fields
     *   `xs` and `ys`, which point to the feature tensor and the target tensor,
     *   respectively. This case is for models with exactly one input and one
     *   output (e.g.. a sequential model). For example:
     *   ```js
     *   {value: {xs: xsTensor, ys: ysTensor}, done: false}
     *   ```
     *
     *   If the model has multiple inputs, the `xs` field of `value` should
     *   be an object mapping input names to their respective feature tensors.
     *   For example:
     *   ```js
     *   {
     *     value: {
     *       xs: {
     *         input_1: xsTensor1,
     *         input_2: xsTensor2
     *       },
     *       ys: ysTensor
     *     },
     *     done: false
     *   }
     *   ```
     *   If the model has multiple outputs, the `ys` field of `value` should
     *   be an object mapping output names to their respective target tensors.
     *   For example:
     *   ```js
     *   {
     *     value: {
     *       xs: xsTensor,
     *       ys: {
     *         output_1: ysTensor1,
     *         output_2: ysTensor2
     *       },
     *     },
     *     done: false
     *   }
     *   ```
     * @param args A `ModelFitDatasetArgs`, containing optional fields.
     *
     * @return A `History` instance. Its `history` attribute contains all
     *   information collected during training.
     *
     * @doc {heading: 'Models', subheading: 'Classes', ignoreCI: true}
     */
    fitDataset<T>(dataset: Dataset<T>, args: ModelFitDatasetArgs<T>): Promise<History>;
    /**
     * Runs a single gradient update on a single batch of data.
     *
     * This method differs from `fit()` and `fitDataset()` in the following
     * regards:
     *   - It operates on exactly one batch of data.
     *   - It returns only the loss and matric values, instead of
     *     returning the batch-by-batch loss and metric values.
     *   - It doesn't support fine-grained options such as verbosity and
     *     callbacks.
     *
     * @param x Input data. It could be one of the following:
     *   - A `tf.Tensor`, or an Array of `tf.Tensor`s (in case the model has
     *     multiple inputs).
     *   - An Object mapping input names to corresponding `tf.Tensor` (if the
     *     model has named inputs).
     * @param y Target darta. It could be either a `tf.Tensor` a multiple
     *   `tf.Tensor`s. It should be consistent with `x`.
     * @returns Training loss or losses (in case the model has
     *   multiple outputs), along with metrics (if any), as numbers.
     *
     * @doc {heading: 'Models', subheading: 'Classes'}
     */
    trainOnBatch(x: Tensor | Tensor[] | {
        [inputName: string]: Tensor;
    }, y: Tensor | Tensor[] | {
        [inputName: string]: Tensor;
    }): Promise<number | number[]>;
    /** @nocollapse */
    static fromConfig<T extends serialization.Serializable>(cls: serialization.SerializableConstructor<T>, config: serialization.ConfigDict, customObjects?: serialization.ConfigDict, fastWeightInit?: boolean): T;
    /**
     * Setter used for force stopping of LayersModel.fit() (i.e., training).
     *
     * Example:
     *
     * ```js
     * const model = tf.sequential();
     * model.add(tf.layers.dense({units: 1, inputShape: [10]}));
     * model.compile({loss: 'meanSquaredError', optimizer: 'sgd'});
     * const xs = tf.ones([8, 10]);
     * const ys = tf.zeros([8, 1]);
     *
     * const history = await model.fit(xs, ys, {
     *   epochs: 10,
     *   callbacks: {
     *     onEpochEnd: async (epoch, logs) => {
     *       if (epoch === 2) {
     *         model.stopTraining = true;
     *       }
     *     }
     *   }
     * });
     *
     * // There should be only 3 values in the loss array, instead of 10 values,
     * // due to the stopping after 3 epochs.
     * console.log(history.history.loss);
     * ```
     */
    stopTraining: boolean;
    getConfig(): any;
}