/**
 * @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.
 * =============================================================================
 */
/* Original source keras/models.py */
import { dispose, io, serialization, util } from '@tensorflow/tfjs-core';
import { getUid } from './backend/state';
import { Input } from './engine/input_layer';
import { getSourceInputs, Node } from './engine/topology';
import { LayersModel } from './engine/training';
import { NotImplementedError, RuntimeError, ValueError } from './errors';
import { deserialize } from './layers/serialization';
import * as generic_utils from './utils/generic_utils';
import { convertPythonicToTs } from './utils/serialization_utils';
import { getExactlyOneShape } from './utils/types_utils';
/**
 * 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 async function modelFromJSON(modelAndWeightsConfig, customObjects) {
    if (!('modelTopology' in modelAndWeightsConfig)) {
        modelAndWeightsConfig = { modelTopology: modelAndWeightsConfig };
    }
    modelAndWeightsConfig = modelAndWeightsConfig;
    let modelTopology = modelAndWeightsConfig.modelTopology;
    if (modelTopology['model_config'] != null) {
        // If the model-topology JSON contains a 'model_config' field, then it is
        // a full model JSON (e.g., from `keras.Model.save()`), which contains
        // not only the model's architecture in its 'model_config' field, but
        // additional information such as the model's optimizer. We use only the
        // 'model_config' field currently.
        modelTopology = modelTopology['model_config'];
    }
    const tsConfig = convertPythonicToTs(modelTopology);
    const model = deserialize(tsConfig, customObjects);
    if (modelAndWeightsConfig.weightsManifest != null) {
        // Load the weight values keyed by the original tensor names in the model
        // file that was loaded.  These should match the keys of the weight
        // manifest.
        const weightValues = await io.loadWeights(modelAndWeightsConfig.weightsManifest, modelAndWeightsConfig.pathPrefix, model.weights.map(weight => weight.originalName));
        // Map the weights to the unique tensor names generated during model loading
        const uniqueWeightValues = {};
        for (const weight of model.weights) {
            uniqueWeightValues[weight.originalName] =
                weightValues[weight.originalName];
        }
        model.loadWeights(uniqueWeightValues);
        // Dispose temporary weight values.
        dispose(weightValues);
    }
    return model;
}
/**
 * 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 async function loadLayersModelInternal(pathOrIOHandler, options) {
    if (options == null) {
        options = {};
    }
    if (typeof pathOrIOHandler === 'string') {
        const handlers = io.getLoadHandlers(pathOrIOHandler, options.onProgress);
        if (handlers.length === 0) {
            // For backward compatibility: if no load handler can be found,
            // assume it is a relative http path.
            // TODO(cais): Reformat the args into a single `LoadOptions` once the core
            // is refactored.
            handlers.push(io.browserHTTPRequest(pathOrIOHandler, options));
        }
        else if (handlers.length > 1) {
            throw new ValueError(`Found more than one (${handlers.length}) load handlers for ` +
                `URL '${pathOrIOHandler}'`);
        }
        pathOrIOHandler = handlers[0];
    }
    return loadLayersModelFromIOHandler(pathOrIOHandler, undefined, options);
}
/**
 * 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 async function loadLayersModelFromIOHandler(handler, customObjects, options) {
    if (options == null) {
        options = {};
    }
    if (handler.load == null) {
        throw new ValueError('Cannot proceed with model loading because the IOHandler provided ' +
            'does not have the `load` method implemented.');
    }
    const artifacts = await handler.load();
    let modelTopology = artifacts.modelTopology;
    if (modelTopology['model_config'] != null) {
        modelTopology = modelTopology['model_config'];
    }
    const strict = options.strict == null ? true : options.strict;
    // If weights are provided and the weight-loading mode is strict, use
    // fast weight initialization. This skips costly initializers such as
    // 'orthogonal' and saves unnecessary computation in cases where
    // the initialized weight values will immediately be overwritten by
    // loaded weight values.
    const fastWeightInit = artifacts.weightData != null && artifacts.weightSpecs != null && strict;
    const model = deserialize(convertPythonicToTs(modelTopology), customObjects, fastWeightInit);
    const trainingConfig = artifacts.trainingConfig;
    if (trainingConfig != null) {
        model.loadTrainingConfig(trainingConfig);
    }
    if (artifacts.userDefinedMetadata != null) {
        model.setUserDefinedMetadata(artifacts.userDefinedMetadata);
    }
    // If weightData is present, load the weights into the model.
    if (artifacts.weightData != null) {
        // Loading weights requires weightSpecs.
        if (artifacts.weightSpecs == null) {
            throw new ValueError('LayersModel artifacts contains weight data, but not weight specs. ' +
                'Therefore loading of weights cannot proceed.');
        }
        const { modelWeights, optimizerWeights } = decodeModelAndOptimizerWeights(artifacts.weightData, artifacts.weightSpecs);
        model.loadWeights(modelWeights, strict);
        if (model.optimizer != null && optimizerWeights.length > 0) {
            await model.optimizer.setWeights(optimizerWeights);
        }
        // Dispose temporary weight values.
        dispose(modelWeights);
        dispose(optimizerWeights.map(w => w.tensor));
    }
    return model;
}
function decodeModelAndOptimizerWeights(buffer, specs) {
    const name2Tensor = io.decodeWeights(buffer, specs);
    const modelWeights = {};
    const optimizerWeights = [];
    specs.forEach(spec => {
        if (spec.group === 'optimizer') {
            optimizerWeights.push({ name: spec.name, tensor: name2Tensor[spec.name] });
        }
        else {
            modelWeights[spec.name] = name2Tensor[spec.name];
        }
    });
    return { modelWeights, optimizerWeights };
}
/**
 * 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 class Sequential extends LayersModel {
    constructor(args) {
        super({ inputs: [], outputs: [] });
        args = args || {};
        this.trainable = true;
        this.built = false;
        // Set model name.
        this.name = (args.name != null) ? args.name : getUid('sequential_');
        // Add to the model any layers passed to the constructor.
        if (args.layers != null) {
            for (const layer of args.layers) {
                this.add(layer);
            }
        }
    }
    // Helper function to Sequential.add  Throws if the new output shape will be
    // invalid.
    checkShape(layer) {
        const shape = layer.inboundNodes[0].outputTensors[0].shape;
        if (shape.some(x => x < 0)) {
            throw new ValueError('Negative dimension size caused by adding layer ' +
                `${layer.name} with input shape [` +
                `${layer.inboundNodes[0].inputTensors[0].shape}]`);
        }
    }
    /**
     * 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) {
        const isLayerModelInstance = layer instanceof Sequential || layer instanceof LayersModel;
        let modelLayer;
        if (isLayerModelInstance) {
            modelLayer = layer;
            if (modelLayer.outputs.length !== 1) {
                throw new ValueError('All layers in a Sequential model ' +
                    'should have a single output tensor. ' +
                    'For multi-output layers, ' +
                    'use the functional API.');
            }
            if (modelLayer.inputs.length !== 1) {
                throw new ValueError('All layers in a Sequential model ' +
                    'should have a single input tensor. ' +
                    'For multi-input layers, ' +
                    'use the functional API.');
            }
        }
        if (this.outputs.length === 0) {
            // first layer in model: check that it is an input layer
            if (layer.inboundNodes.length === 0) {
                // create an input layer
                if (layer.batchInputShape == null) {
                    throw new ValueError('The first layer in a Sequential model must ' +
                        'get an `inputShape` or `batchInputShape` argument.');
                }
                // Instantiate the input layer.
                const x = Input({
                    batchShape: layer.batchInputShape,
                    dtype: layer.dtype,
                    name: layer.name + '_input'
                });
                // This will build the current layer and create the node connecting
                // the current layer to the input layer we just created.
                layer.apply(x);
            }
            if (isLayerModelInstance) {
                this.outputs = modelLayer.outputs;
                this.inputs = modelLayer.inputs;
            }
            else {
                if (layer.inboundNodes.length !== 1) {
                    throw new ValueError('A layer added to a Sequential model must not already be ' +
                        `connected somewhere else. LayersModel received layer ${layer.name} ` +
                        `which has ${layer.inboundNodes.length} pre-existing inbound ` +
                        'connections.');
                }
                if (layer.inboundNodes[0].outputTensors.length !== 1) {
                    throw new ValueError('All layers in a Sequential model ' +
                        'should have a single output tensor. ' +
                        'For multi-output layers, ' +
                        'use the functional API.');
                }
                this.checkShape(layer);
                this.outputs = [layer.inboundNodes[0].outputTensors[0]];
                this.inputs = getSourceInputs(this.outputs[0]);
            }
            this.inboundNodes = [];
            // We create an input node, which we will keep updated
            // as we add more layers.
            // (This call has side effects.)
            // tslint:disable-next-line:no-unused-expression
            new Node({
                outboundLayer: this,
                inboundLayers: [],
                nodeIndices: [],
                tensorIndices: [],
                inputTensors: this.inputs,
                outputTensors: this.outputs,
                // no model-level masking for now
                inputMasks: generic_utils.pyListRepeat(null, this.inputs.length),
                outputMasks: [null],
                inputShapes: this.inputs.map(x => x.shape),
                outputShapes: this.outputs[0].shape
            });
        }
        else {
            const outputTensor = layer.apply(this.outputs[0]);
            if (Array.isArray(outputTensor)) {
                throw new TypeError('All layers in a Sequential model ' +
                    'should have a single output tensor. ' +
                    'For multi-output layers, ' +
                    'use the functional API.');
            }
            this.checkShape(layer);
            this.outputs = [outputTensor];
            // update self.inbound_nodes
            this.inboundNodes[0].outputTensors = this.outputs;
            this.inboundNodes[0].outputShapes = [this.outputs[0].shape];
        }
        this.layers.push(layer);
        this.built = false;
    }
    /**
     * Removes the last layer in the model.
     *
     * @exception TypeError if there are no layers in the model.
     */
    pop() {
        if (this.layers.length === 0) {
            throw new TypeError('There are no layers in the model.');
        }
        this.layers.pop();
        if (this.layers.length === 0) {
            this.outputs = [];
            this.inboundNodes = [];
            this.outboundNodes = [];
        }
        else {
            const lastLayerIndex = this.layers.length - 1;
            this.layers[lastLayerIndex].outboundNodes = [];
            this.outputs = [this.layers[lastLayerIndex].output];
            // update self.inbound_nodes
            this.inboundNodes[0].outputTensors = this.outputs;
            this.inboundNodes[0].outputShapes = [this.outputs[0].shape];
        }
    }
    call(inputs, kwargs) {
        if (this.model == null) {
            this.build();
        }
        return this.model.call(inputs, kwargs);
    }
    build(inputShape) {
        // Call `getExactlyOneShape` without using its return value,
        // to verify that exactly one input shape is provided.
        getExactlyOneShape(inputShape);
        if (this.inputs.length === 0 || this.outputs.length === 0) {
            throw new TypeError('Sequential model cannot be built: model is empty.' +
                ' Add some layers first.');
        }
        // actually create the model
        this.model = new LayersModel({
            inputs: this.inputs,
            outputs: this.outputs[0],
            name: this.name + '_model'
        });
        this.model.trainable = this.trainable;
        // mirror model attributes
        this.supportsMasking = this.model.supportsMasking;
        // TODO(michaelterry): Add caches
        this.inputLayers = this.model.inputLayers;
        this.inputLayersNodeIndices = this.model.inputLayersNodeIndices;
        this.inputLayersTensorIndices = this.model.inputLayersTensorIndices;
        this.outputLayers = this.model.outputLayers;
        this.outputLayersNodeIndices = this.model.outputLayersNodeIndices;
        this.outputLayersTensorIndices = this.model.outputLayersTensorIndices;
        this.nodesByDepth = this.model.nodesByDepth;
        this.containerNodes = this.model.containerNodes;
        this.outputNames = this.model.outputNames;
        this.inputNames = this.model.inputNames;
        // TODO(michaelterry): Add feedInputNames, feedInputs, if needed.
        // TODO(michaelterry): Add callbackModel if needed.
        this.built = true;
    }
    countParams() {
        if (!this.built) {
            this.build();
        }
        return super.countParams();
    }
    /**
     * 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, positions, printFn = console.log) {
        if (!this.built) {
            this.build();
        }
        super.summary(lineLength, positions, printFn);
    }
    /**
     * 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) {
        if (this.model == null) {
            this.build();
        }
        this.model.setWeights(weights);
    }
    /**
     * 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, y, args = {}) {
        if (!this.built) {
            throw new RuntimeError('The model needs to be compiled before being used.');
        }
        return this.model.evaluate(x, y, args);
    }
    // TODO(cais): Add code snippet below once real dataset objects are
    //   available.
    /**
     * 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'}
     */
    async evaluateDataset(dataset, args) {
        if (!this.built) {
            throw new RuntimeError('The model needs to be compiled before being used.');
        }
        return this.model.evaluateDataset(dataset, args);
    }
    /**
     * 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 TensorFow.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 an 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, args = {}) {
        if (this.model == null) {
            this.build();
        }
        return this.model.predict(x, args);
    }
    /**
     * Returns predictions for a single batch of samples.
     *
     * @param x: Input samples, as an Tensor, or list of Tensors (if the model
     *   has multiple inputs).
     * @return Tensor(s) of predictions
     */
    predictOnBatch(x) {
        if (this.model == null) {
            this.build();
        }
        return this.model.predictOnBatch(x);
    }
    /**
     * See `LayersModel.compile`.
     *
     * @param args
     */
    compile(args) {
        this.build();
        this.model.compile(args);
        this.optimizer_ = this.model.optimizer;
        // tslint:disable-next-line:no-any
        this.isOptimizerOwned = this.model.isOptimizerOwned;
        this.loss = this.model.loss;
        this.metrics = this.model.metrics;
        // TODO(cais): Add this.lossWeights, this.sampleWeightMode,
        //   this.weightedMetrics, this.targets.
        this.metricsTensors = this.model.metricsTensors;
        this.metricsNames = this.model.metricsNames;
        // TODO(cais): Add sampleWeights.
    }
    get optimizer() {
        return this.model == null ? undefined : this.model.optimizer;
    }
    set optimizer(optimizer) {
        this.model.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'}
     */
    async fit(x, y, args = {}) {
        if (!this.built) {
            throw new RuntimeError('The model needs to be compiled before ' +
                'being used.');
        }
        return this.model.fit(x, y, args);
    }
    /**
     * 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}
     */
    async fitDataset(dataset, args) {
        if (!this.built) {
            throw new RuntimeError('The model needs to be compiled before ' +
                'being used.');
        }
        return this.model.fitDataset(dataset, args);
    }
    /**
     * 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'}
     */
    async trainOnBatch(x, y) {
        return this.model.trainOnBatch(x, y);
    }
    /* See parent class for JsDoc */
    /** @nocollapse */
    static fromConfig(cls, config, customObjects = {}, fastWeightInit = false) {
        let configArray;
        let extraModelConfig = {};
        if (config instanceof Array) {
            if (!(config[0].className != null) ||
                config[0]['className'] === 'Merge') {
                throw new ValueError('Legacy serialization format not supported yet.');
            }
            configArray = config;
        }
        else {
            util.assert(config['layers'] != null, () => `When the config data for a Sequential model is not an Array, ` +
                `it must be an Object that contains the 'layers' field.`);
            configArray = config['layers'];
            delete config['layers'];
            extraModelConfig = config;
        }
        const model = new cls(extraModelConfig);
        if (!(model instanceof Sequential)) {
            throw new NotImplementedError(`Sequential.fromConfig called on non-Sequential input: ${model}`);
        }
        for (const conf of configArray) {
            const customObjects = undefined;
            const layer = deserialize(conf, customObjects, fastWeightInit);
            if (fastWeightInit) {
                layer.setFastWeightInitDuringBuild(true);
            }
            model.add(layer);
        }
        return model;
    }
    /**
     * 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);
     * ```
     */
    set stopTraining(stop) {
        // TODO(cais): When refactoring to remove the composition pattern happens,
        // remove this method overriding.
        if (this.model == null) {
            throw new ValueError('Cannot set the stopTraining property of a sequential model before ' +
                'it is compiled.');
        }
        this.model.stopTraining = stop;
    }
    get stopTraining() {
        if (this.model == null) {
            throw new ValueError('Cannot get the stopTraining property of a sequential model before ' +
                'it is compiled.');
        }
        return this.model.stopTraining;
    }
    // TODO(cais): Override get trainableWeights() here
    // tslint:disable-next-line:no-any
    getConfig() {
        // NOTE(cais): We override the return type of getConfig() to `any` here,
        //   because the `Sequential` class is a special case among `Container`
        //   subtypes in that its getConfig() method returns an Array (not a
        //   dict).
        const layers = [];
        for (const layer of this.layers) {
            const dict = {};
            dict['className'] = layer.getClassName();
            dict['config'] = layer.getConfig();
            layers.push(dict);
        }
        return { name: this.name, layers };
    }
}
/** @nocollapse */
Sequential.className = 'Sequential';
serialization.registerClass(Sequential);
//# sourceMappingURL=models.js.map