* Perform a step of the game. * * @param {0 | 1 | 2 | 3} action The action to take in the current step. * The meaning of the possible value

* Get plain JavaScript representation of the game state. * * @return An object with two keys: * - s: {Array<[number, number]>} representing

* Display a message to the info div. * * @param {string} message The message to be displayed.

* Get the size of the character set. * * @returns {number} Size of the character set, i.e., how many unique * characters there are in the t

* Execute all unit tests in the current directory.

* Get the length of each training example.

Display an indeterminate progress bar with message.

* Get the unique character at given index from the character set. * * @param {number} index * @returns {string} The unique character at `inde

* Initializes the Translation demo.

* If the underlying model is not loaded, load it. * * @param {() => any} loadingCallback An optional callback function that will * be invok

* Reset the state of the game. * * @return {object} Initial state of the game. * See the documentation of `getState()` for details.

* Perform classification on a batch of image tensors. * * @param {tf.Tensor} images Batch image tensor of shape * `[numExamples, height, wi

* Fills a canvas with segmenter predictions overlaid on top of the query image.

* The encoder portion of the model. * * @param {object} opts encoder configuration, includnig the following fields: * - originaDim {number} Lengt

* Fetches and parses the labelmap. If IDs are provided, they are used. * Otherwise list indices are used as IDs.

* Generate a random color style for canvas strokes and fills. * * @returns {string} Style string in the form of 'rgb(100,200,250)'.

* Get a data iterator function. * * @param {boolean} shuffle Whether the data is to be shuffled. If `false`, * the examples generated by re

* Get a random slice of text data. * * @returns {[string, number[]} The string and index representation of the * same slice.

* Load and preprocess data. * * This method first tries to load the data from `LOCAL_JENA_WEATHER_CSV_PATH` * (a relative path) and, if that

* Play one step of the game. * * @returns {number | null} If this step leads to the end of the game, * the total reward from the game as a

* Given an image element, makes a prediction through mobilenet returning the * probabilities of the top K classes.

* Run inference on manually-input Iris flower data. * * @param model The instance of `tf.Model` to run the inference with.

* Run inference on manually-input Iris flower data. * * @param model The instance of `tf.Model` to run the inference with.

* Preview an image on the console. * * @param {Float32Array} imageData

* Display a snackbar message on the screen. * * @param {string} message The message to be displayed. * @param {number} timeoutMillis How many milll

* Train the policy network's model. * * @param {CartPole} cartPoleSystem The cart-pole system object to use during * training. * @param

Translate the given English sentence into French.

* Check the status of locally saved model. * * @returns If the locally saved model exists, the model info as a JSON * object. Else, `undefi

* Builds and returns Multi Layer Perceptron Regression Model. * * @param {number} inputShape The input shape of the model. * @returns {tf.Sequentia

* The decoder portion of the model. * * @param {*} opts decoder configuration * @param {number} opts.originalDim number of dimensions in the origin

* Fills the query image overlaid canvas with detection results.

* Generate a set of examples using the generator model of the ACGAN. * * @param {tf.Model} generator The generator part of the ACGAN.

* Generate the next epoch of data for training models. * * @param {number} numExamples Number examples to generate. * @returns {[tf.Tensor, t

* Prepare an input image by converting it to tf.Tensor and resizing it * according to the expected model input.

* Refresh the status of locally saved model (in IndexedDB).

* Remove the locally saved model from IndexedDB.

* Synthesize an input image, run inference on it and visualize the results. * * @param {tf.Model} model Model to be used for inference.

* Run the model in case of image classification.

* Search for images with content matching target wrods. * * @param {string[]} filePaths An array of paths to image files * @param {string[]}

* Set the state of the cart-pole system randomly.