models.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.
 * =============================================================================
 */

/* Original source keras/models.py */

// tslint:disable:max-line-length
import {doc, io, Scalar, serialization, Tensor} from '@tensorflow/tfjs-core';

import {getUid} from './backend/state';
import {History} from './base_callbacks';
import {getSourceInputs, Input, Layer, Node, SymbolicTensor} from './engine/topology';
import {Model, ModelCompileConfig, ModelEvaluateConfig, ModelFitConfig} from './engine/training';
import {RuntimeError, ValueError} from './errors';
import {deserialize} from './layers/serialization';
import {Kwargs, NamedTensorMap, Shape} from './types';
import {JsonDict} from './types';
import * as generic_utils from './utils/generic_utils';
import {convertPythonicToTs} from './utils/serialization_utils';
import {getExactlyOneShape} from './utils/types_utils';
// tslint:enable:max-line-length

/**
 * Parses a JSON model configuration file and returns a model instance.
 *  @param modelAndWeightsConfig JSON object or string encoding a model and
 *       weights configuration.
 *  @param custom_objects Optional dictionary mapping names
 *       (strings) to custom classes or functions to be
 *       considered during deserialization.
 * @returns A TensorFlow.js Layers `Model` instance (uncompiled).
 */
export async function modelFromJSON(
    modelAndWeightsConfig: ModelAndWeightsConfig,
    customObjects?: serialization.ConfigDict): Promise<Model> {
  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'] as JsonDict;
  }
  const tsConfig =
      convertPythonicToTs(modelTopology) as serialization.ConfigDict;
  const model = deserialize(tsConfig, customObjects) as Model;

  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)) as NamedTensorMap;

    // Map the weights to the unique tensor names generated during model loading
    const uniqueWeightValues: NamedTensorMap = {};
    for (const weight of model.weights) {
      uniqueWeightValues[weight.originalName] =
          weightValues[weight.originalName];
    }

    const skipMismatches: boolean = null;
    const isNamedTensorMap = true;
    model.loadWeights(uniqueWeightValues, skipMismatches, isNamedTensorMap);
  }
  return model;
}

/**
 * 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: JsonDict;

  /**
   * 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;
}

// TODO(nielsene): Remove after: https://github.com/tensorflow/tfjs/issues/400
export interface ModelPredictConfig {
  /**
   * Optional. Batch size (Integer). If unspecified, it will default to 32.
   */
  batchSize?: number;

  /**
   * Optional. Verbosity mode. Defaults to false.
   */
  verbose?: boolean;
}

// tslint:disable:max-line-length
/**
 * 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.loadModel('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.loadModel('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.loadModel(
 *     tf.io.browserFiles([jsonUpload.files[0], weightsUpload.files[0]]));
 * ```
 *
 * Example 4. Load a model from an HTTP server.
 *
 * ```js
 * const model = await
 *     tf.loadModel('https://storage.googleapis.com/tfjs-models/tfjs/iris_v1/model.json')
 * ```
 *
 * @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.
 *
 * @returns A `Promise` of `Model`, with the topology and weights loaded.
 */
// tslint:enable:max-line-length
export async function loadModelInternal(pathOrIOHandler: string|
                                        io.IOHandler): Promise<Model> {
  if (typeof pathOrIOHandler === 'string') {
    const handlers = io.getLoadHandlers(pathOrIOHandler);
    if (handlers.length === 0) {
      // For backward compatibility: if no load handler can be found,
      // assume it is a relative http path.
      handlers.push(io.browserHTTPRequest(pathOrIOHandler));
    } else if (handlers.length > 1) {
      throw new ValueError(
          `Found more than one (${handlers.length}) load handlers for ` +
          `URL '${pathOrIOHandler}'`);
    }
    pathOrIOHandler = handlers[0];
  }
  return loadModelFromIOHandler(pathOrIOHandler as io.IOHandler);
}

/**
 * Load a model and optionally its weights, using an IOHandler object.
 */
export async function loadModelFromIOHandler(
    handler: io.IOHandler,
    customObjects?: serialization.ConfigDict): Promise<Model> {
  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 as JsonDict;
  if (modelTopology['model_config'] != null) {
    modelTopology = modelTopology['model_config'] as JsonDict;
  }
  const model =
      deserialize(
          convertPythonicToTs(modelTopology) as serialization.ConfigDict,
          customObjects) as Model;

  // 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(
          'Model artifacts contains weight data, but not weight specs. ' +
          'Therefore loading of weights cannot proceed.');
    }

    const skipMismatch = false;
    const isNamedTensorMap = true;
    model.loadWeights(
        io.decodeWeights(artifacts.weightData, artifacts.weightSpecs),
        skipMismatch, isNamedTensorMap);
  }
  return model;
}

/**
 * Configuration for a Sequential model.
 */
export interface SequentialConfig {
  /** 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.
 *
 * `sequential` is a factory function that creates an instance of
 * `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 Model {
  static className = 'Sequential';
  private model: Model;
  private _updatable: boolean;
  constructor(config?: SequentialConfig) {
    super({inputs: [], outputs: []});
    config = config || {};

    this.trainable = true;
    this._updatable = true;
    this.built = false;

    // Set model name.
    this.name = (config.name != null) ? config.name : getUid('sequential_');

    // Add to the model any layers passed to the constructor.
    if (config.layers != null) {
      for (const layer of config.layers) {
        this.add(layer);
      }
    }
  }

  /**
   * 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 {
    const isLayerModelInstance =
        layer instanceof Sequential || layer instanceof Model;
    let modelLayer: Model;
    if (isLayerModelInstance) {
      modelLayer = layer as Model;
      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. Model 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.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.outputs = [outputTensor as SymbolicTensor];
      // 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(): void {
    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 as SymbolicTensor];
      // update self.inbound_nodes
      this.inboundNodes[0].outputTensors = this.outputs;
      this.inboundNodes[0].outputShapes = [this.outputs[0].shape];
    }
  }

  call(inputs: Tensor|Tensor[], kwargs: Kwargs): Tensor|Tensor[] {
    if (this.model == null) {
      this.build();
    }
    return this.model.call(inputs, kwargs);
  }

  build(inputShape?: Shape|Shape[]) {
    // 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 Model({
      inputs: this.inputs,
      outputs: this.outputs[0],
      name: this.name + '_model'
    });
    this.model.trainable = this.trainable;
    this.model.updatable = this.updatable;

    // 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(): number {
    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?: number, positions?: number[],
      printFn:
          // tslint:disable-next-line:no-any
      (message?: any, ...optionalParams: any[]) => void = 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: Tensor[]): void {
    if (this.model == null) {
      this.build();
    }
    this.model.setWeights(weights);
  }

  get updatable(): boolean {
    return this._updatable;
  }

  set updatable(value: boolean) {
    if (this.built) {
      this.model.updatable = value;
    }
    this._updatable = value;
  }

  /**
   * 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 `Tensor` of test data, or an `Array` of `Tensor`s if the model
   * has multiple inputs.
   * @param y `Tensor` of target data, or an `Array` of `Tensor`s if the model
   *   has multiple outputs.
   * @param config 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', configParamIndices: [2]})
  evaluate(
      x: Tensor|Tensor[], y: Tensor|Tensor[], config: ModelEvaluateConfig = {}):
      Scalar|Scalar[] {
    if (!this.built) {
      throw new RuntimeError(
          'The model needs to be compiled before being used.');
    }
    return this.model.evaluate(x, y, config);
  }

  /**
   * 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 `Tensor`s if
   *   the model has multiple inputs.
   * @param conifg A `ModelPredictConfig` object containing optional fields.
   *
   * @return `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', configParamIndices: [1]})
  predict(x: Tensor|Tensor[], config: ModelPredictConfig = {}): Tensor
      |Tensor[] {
    if (this.model == null) {
      this.build();
    }
    return this.model.predict(x, config);
  }

  /**
   * 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: Tensor): Tensor|Tensor[] {
    if (this.model == null) {
      this.build();
    }
    return this.model.predictOnBatch(x);
  }

  /**
   * See `Model.compile`.
   *
   * @param config
   */
  compile(config: ModelCompileConfig): void {
    this.build();
    this.model.compile(config);
    this.optimizer = this.model.optimizer;
    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.
  }

  /**
   * 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 `Tensor` of training data, or an array of `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 `Tensor`s.
   * @param y `Tensor` of target (label) data, or an array of `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 `Tensor`s.
   * @param config  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', configParamIndices: [2]})
  async fit(
      x: Tensor|Tensor[]|{[inputName: string]: Tensor},
      y: Tensor|Tensor[]|{[inputName: string]: Tensor},
      config: ModelFitConfig = {}): Promise<History> {
    if (!this.built) {
      throw new RuntimeError(
          'The model needs to be compiled before ' +
          'being used.');
    }
    return this.model.fit(x, y, config);
  }

  /* See parent class for JsDoc */
  static fromConfig<T extends serialization.Serializable>(
      cls: serialization.SerializableConstructor<T>,
      config: serialization.ConfigDict): T {
    const model = new cls({});
    if (!(model instanceof Sequential)) {
      throw new ValueError(
          `Sequential.fromConfig called on non-Sequential input: ${model}`);
    }
    if (!(config instanceof Array)) {
      throw new ValueError(
          `Sequential.fromConfig called without an array of configs`);
    }
    if (!(config[0].className != null) || config[0]['className'] === 'Merge') {
      throw new ValueError('Legacy serialization format not supported yet.');
    }
    for (const conf of config as serialization.ConfigDictArray) {
      const layer = deserialize(conf as serialization.ConfigDict) as Layer;
      model.add(layer);
    }
    return model;
  }

  // TODO(cais): Override get trainableWeights() here

  // tslint:disable-next-line:no-any
  getConfig(): any {
    // 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 config: serialization.ConfigDict[] = [];
    for (const layer of this.layers) {
      config.push({
        className: layer.getClassName(),
        config: layer.getConfig(),
      });
    }
    return config;
  }
}
serialization.SerializationMap.register(Sequential);