@tensorflow/tfjs-layers/dist/exports.js

/**
 * @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.
 * =============================================================================
 */
import { CallbackConstructorRegistry } from './base_callbacks';
import { Input, } from './engine/input_layer';
import { LayersModel } from './engine/training';
import { loadLayersModelInternal, Sequential } from './models';
// TODO(cais): Add doc string to all the public static functions in this
//   class; include exectuable JavaScript code snippets where applicable
//   (b/74074458).
// LayersModel and related factory methods.
/**
 * A model is a data structure that consists of `Layers` and defines inputs
 * and outputs.
 *
 * The key difference between `tf.model` and `tf.sequential` is that
 * `tf.model` is more generic, supporting an arbitrary graph (without
 * cycles) of layers. `tf.sequential` is less generic and supports only a linear
 * stack of layers.
 *
 * When creating a `tf.LayersModel`, specify its input(s) and output(s). Layers
 * are used to wire input(s) to output(s).
 *
 * For example, the following code snippet defines a model consisting of
 * two `dense` layers, with 10 and 4 units, respectively.
 *
 * ```js
 * // Define input, which has a size of 5 (not including batch dimension).
 * const input = tf.input({shape: [5]});
 *
 * // First dense layer uses relu activation.
 * const denseLayer1 = tf.layers.dense({units: 10, activation: 'relu'});
 * // Second dense layer uses softmax activation.
 * const denseLayer2 = tf.layers.dense({units: 4, activation: 'softmax'});
 *
 * // Obtain the output symbolic tensor by applying the layers on the input.
 * const output = denseLayer2.apply(denseLayer1.apply(input));
 *
 * // Create the model based on the inputs.
 * const model = tf.model({inputs: input, outputs: output});
 *
 * // The model can be used for training, evaluation and prediction.
 * // For example, the following line runs prediction with the model on
 * // some fake data.
 * model.predict(tf.ones([2, 5])).print();
 * ```
 * See also:
 *   `tf.sequential`, `tf.loadLayersModel`.
 *
 * @doc {heading: 'Models', subheading: 'Creation'}
 */
export function model(args) {
    return new LayersModel(args);
}
/**
 * Creates a `tf.Sequential` model.  A sequential model is any model where the
 * outputs of one layer are the inputs to the next layer, i.e. the model
 * topology is a simple 'stack' of layers, with no branching or skipping.
 *
 * This means that the first layer passed to a `tf.Sequential` model should have
 * a defined input shape. What that means is that it should have received an
 * `inputShape` or `batchInputShape` argument, or for some type of layers
 * (recurrent, Dense...) an `inputDim` argument.
 *
 * The key difference between `tf.model` and `tf.sequential` is that
 * `tf.sequential` is less generic, supporting only a linear stack of layers.
 * `tf.model` is more generic and supports an arbitrary graph (without
 * cycles) of layers.
 *
 * Examples:
 *
 * ```js
 * const model = tf.sequential();
 *
 * // First layer must have an input shape defined.
 * model.add(tf.layers.dense({units: 32, inputShape: [50]}));
 * // Afterwards, TF.js does automatic shape inference.
 * model.add(tf.layers.dense({units: 4}));
 *
 * // Inspect the inferred shape of the model's output, which equals
 * // `[null, 4]`. The 1st dimension is the undetermined batch dimension; the
 * // 2nd is the output size of the model's last layer.
 * console.log(JSON.stringify(model.outputs[0].shape));
 * ```
 *
 * It is also possible to specify a batch size (with potentially undetermined
 * batch dimension, denoted by "null") for the first layer using the
 * `batchInputShape` key. The following example is equivalent to the above:
 *
 * ```js
 * const model = tf.sequential();
 *
 * // First layer must have a defined input shape
 * model.add(tf.layers.dense({units: 32, batchInputShape: [null, 50]}));
 * // Afterwards, TF.js does automatic shape inference.
 * model.add(tf.layers.dense({units: 4}));
 *
 * // Inspect the inferred shape of the model's output.
 * console.log(JSON.stringify(model.outputs[0].shape));
 * ```
 *
 * You can also use an `Array` of already-constructed `Layer`s to create
 * a `tf.Sequential` model:
 *
 * ```js
 * const model = tf.sequential({
 *   layers: [tf.layers.dense({units: 32, inputShape: [50]}),
 *            tf.layers.dense({units: 4})]
 * });
 * console.log(JSON.stringify(model.outputs[0].shape));
 * ```
 *
 * @doc {heading: 'Models', subheading: 'Creation'}
 */
export function sequential(config) {
    return new Sequential(config);
}
/**
 * Load a model composed of Layer objects, including its topology and optionally
 * weights. See the Tutorial named "How to import a Keras Model" for usage
 * examples.
 *
 * This method is applicable to:
 *
 * 1. Models created with the `tf.layers.*`, `tf.sequential`, and
 * `tf.model` APIs of TensorFlow.js and later saved with the
 * `tf.LayersModel.save` method.
 * 2. Models converted from Keras or TensorFlow tf.keras using the
 * [tensorflowjs_converter](https://github.com/tensorflow/tfjs/tree/master/tfjs-converter).
 *
 * This mode is *not* applicable to TensorFlow `SavedModel`s or their converted
 * forms. For those models, use `tf.loadGraphModel`.
 *
 * Example 1. Load a model from an HTTP server.
 *
 * ```js
 * const model = await tf.loadLayersModel(
 *     'https://storage.googleapis.com/tfjs-models/tfjs/iris_v1/model.json');
 * model.summary();
 * ```
 *
 * Example 2: Save `model`'s topology and weights to browser [local
 * storage](https://developer.mozilla.org/en-US/docs/Web/API/Window/localStorage);
 * then load it back.
 *
 * ```js
 * const model = tf.sequential(
 *     {layers: [tf.layers.dense({units: 1, inputShape: [3]})]});
 * console.log('Prediction from original model:');
 * model.predict(tf.ones([1, 3])).print();
 *
 * const saveResults = await model.save('localstorage://my-model-1');
 *
 * const loadedModel = await tf.loadLayersModel('localstorage://my-model-1');
 * console.log('Prediction from loaded model:');
 * loadedModel.predict(tf.ones([1, 3])).print();
 * ```
 *
 * Example 3. Saving `model`'s topology and weights to browser
 * [IndexedDB](https://developer.mozilla.org/en-US/docs/Web/API/IndexedDB_API);
 * then load it back.
 *
 * ```js
 * const model = tf.sequential(
 *     {layers: [tf.layers.dense({units: 1, inputShape: [3]})]});
 * console.log('Prediction from original model:');
 * model.predict(tf.ones([1, 3])).print();
 *
 * const saveResults = await model.save('indexeddb://my-model-1');
 *
 * const loadedModel = await tf.loadLayersModel('indexeddb://my-model-1');
 * console.log('Prediction from loaded model:');
 * loadedModel.predict(tf.ones([1, 3])).print();
 * ```
 *
 * Example 4. Load a model from user-selected files from HTML
 * [file input
 * elements](https://developer.mozilla.org/en-US/docs/Web/HTML/Element/input/file).
 *
 * ```js
 * // Note: this code snippet will not work without the HTML elements in the
 * //   page
 * const jsonUpload = document.getElementById('json-upload');
 * const weightsUpload = document.getElementById('weights-upload');
 *
 * const model = await tf.loadLayersModel(
 *     tf.io.browserFiles([jsonUpload.files[0], weightsUpload.files[0]]));
 * ```
 *
 * @param pathOrIOHandler Can be either of the two formats
 *   1. A string path to the `ModelAndWeightsConfig` JSON describing
 *      the model in the canonical TensorFlow.js format. For file://
 *      (tfjs-node-only), http:// and https:// schemas, the path can be
 *      either absolute or relative.
 *   2. An `tf.io.IOHandler` object that loads model artifacts with its `load`
 *      method.
 * @param options Optional configuration arguments for the model loading,
 *   including:
 *   - `strict`: Require that the provided weights exactly match those required
 *     by the layers.  Default true.  Passing false means that both extra
 *     weights and missing weights will be silently ignored.
 *   - `onProgress`: A function of the signature `(fraction: number) => void',
 *     that can be used as the progress callback for the model loading.
 * @returns A `Promise` of `tf.LayersModel`, with the topology and weights
 *     loaded.
 *
 * @doc {heading: 'Models', subheading: 'Loading'}
 */
export function loadLayersModel(pathOrIOHandler, options) {
    if (options == null) {
        options = {};
    }
    return loadLayersModelInternal(pathOrIOHandler, options);
}
/**
 * Used to instantiate an input to a model as a `tf.SymbolicTensor`.
 *
 * Users should call the `input` factory function for
 * consistency with other generator functions.
 *
 * Example:
 *
 * ```js
 * // Defines a simple logistic regression model with 32 dimensional input
 * // and 3 dimensional output.
 * const x = tf.input({shape: [32]});
 * const y = tf.layers.dense({units: 3, activation: 'softmax'}).apply(x);
 * const model = tf.model({inputs: x, outputs: y});
 * model.predict(tf.ones([2, 32])).print();
 * ```
 *
 * Note: `input` is only necessary when using `model`. When using
 * `sequential`, specify `inputShape` for the first layer or use `inputLayer`
 * as the first layer.
 *
 * @doc {heading: 'Models', subheading: 'Inputs'}
 */
export function input(config) {
    return Input(config);
}
export function registerCallbackConstructor(verbosityLevel, callbackConstructor) {
    CallbackConstructorRegistry.registerCallbackConstructor(verbosityLevel, callbackConstructor);
}
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Layers\n * are used to wire input(s) to output(s).\n *\n * For example, the following code snippet defines a model consisting of\n * two `dense` layers, with 10 and 4 units, respectively.\n *\n * ```js\n * // Define input, which has a size of 5 (not including batch dimension).\n * const input = tf.input({shape: [5]});\n *\n * // First dense layer uses relu activation.\n * const denseLayer1 = tf.layers.dense({units: 10, activation: 'relu'});\n * // Second dense layer uses softmax activation.\n * const denseLayer2 = tf.layers.dense({units: 4, activation: 'softmax'});\n *\n * // Obtain the output symbolic tensor by applying the layers on the input.\n * const output = denseLayer2.apply(denseLayer1.apply(input));\n *\n * // Create the model based on the inputs.\n * const model = tf.model({inputs: input, outputs: output});\n *\n * // The model can be used for training, evaluation and prediction.\n * // For example, the following line runs prediction with the model on\n * // some fake data.\n * model.predict(tf.ones([2, 5])).print();\n * ```\n * See also:\n *   `tf.sequential`, `tf.loadLayersModel`.\n *\n * @doc {heading: 'Models', subheading: 'Creation'}\n */\nexport function model(args: ContainerArgs): LayersModel {\n  return new LayersModel(args);\n}\n\n/**\n * Creates a `tf.Sequential` model.  A sequential model is any model where the\n * outputs of one layer are the inputs to the next layer, i.e. the model\n * topology is a simple 'stack' of layers, with no branching or skipping.\n *\n * This means that the first layer passed to a `tf.Sequential` model should have\n * a defined input shape. What that means is that it should have received an\n * `inputShape` or `batchInputShape` argument, or for some type of layers\n * (recurrent, Dense...) an `inputDim` argument.\n *\n * The key difference between `tf.model` and `tf.sequential` is that\n * `tf.sequential` is less generic, supporting only a linear stack of layers.\n * `tf.model` is more generic and supports an arbitrary graph (without\n * cycles) of layers.\n *\n * Examples:\n *\n * ```js\n * const model = tf.sequential();\n *\n * // First layer must have an input shape defined.\n * model.add(tf.layers.dense({units: 32, inputShape: [50]}));\n * // Afterwards, TF.js does automatic shape inference.\n * model.add(tf.layers.dense({units: 4}));\n *\n * // Inspect the inferred shape of the model's output, which equals\n * // `[null, 4]`. The 1st dimension is the undetermined batch dimension; the\n * // 2nd is the output size of the model's last layer.\n * console.log(JSON.stringify(model.outputs[0].shape));\n * ```\n *\n * It is also possible to specify a batch size (with potentially undetermined\n * batch dimension, denoted by \"null\") for the first layer using the\n * `batchInputShape` key. The following example is equivalent to the above:\n *\n * ```js\n * const model = tf.sequential();\n *\n * // First layer must have a defined input shape\n * model.add(tf.layers.dense({units: 32, batchInputShape: [null, 50]}));\n * // Afterwards, TF.js does automatic shape inference.\n * model.add(tf.layers.dense({units: 4}));\n *\n * // Inspect the inferred shape of the model's output.\n * console.log(JSON.stringify(model.outputs[0].shape));\n * ```\n *\n * You can also use an `Array` of already-constructed `Layer`s to create\n * a `tf.Sequential` model:\n *\n * ```js\n * const model = tf.sequential({\n *   layers: [tf.layers.dense({units: 32, inputShape: [50]}),\n *            tf.layers.dense({units: 4})]\n * });\n * console.log(JSON.stringify(model.outputs[0].shape));\n * ```\n *\n * @doc {heading: 'Models', subheading: 'Creation'}\n */\nexport function sequential(config?: SequentialArgs): Sequential {\n  return new Sequential(config);\n}\n\n/**\n * Load a model composed of Layer objects, including its topology and optionally\n * weights. See the Tutorial named \"How to import a Keras Model\" for usage\n * examples.\n *\n * This method is applicable to:\n *\n * 1. Models created with the `tf.layers.*`, `tf.sequential`, and\n * `tf.model` APIs of TensorFlow.js and later saved with the\n * `tf.LayersModel.save` method.\n * 2. Models converted from Keras or TensorFlow tf.keras using the\n * [tensorflowjs_converter](https://github.com/tensorflow/tfjs/tree/master/tfjs-converter).\n *\n * This mode is *not* applicable to TensorFlow `SavedModel`s or their converted\n * forms. For those models, use `tf.loadGraphModel`.\n *\n * Example 1. Load a model from an HTTP server.\n *\n * ```js\n * const model = await tf.loadLayersModel(\n *     'https://storage.googleapis.com/tfjs-models/tfjs/iris_v1/model.json');\n * model.summary();\n * ```\n *\n * Example 2: Save `model`'s topology and weights to browser [local\n * storage](https://developer.mozilla.org/en-US/docs/Web/API/Window/localStorage);\n * then load it back.\n *\n * ```js\n * const model = tf.sequential(\n *     {layers: [tf.layers.dense({units: 1, inputShape: [3]})]});\n * console.log('Prediction from original model:');\n * model.predict(tf.ones([1, 3])).print();\n *\n * const saveResults = await model.save('localstorage://my-model-1');\n *\n * const loadedModel = await tf.loadLayersModel('localstorage://my-model-1');\n * console.log('Prediction from loaded model:');\n * loadedModel.predict(tf.ones([1, 3])).print();\n * ```\n *\n * Example 3. Saving `model`'s topology and weights to browser\n * [IndexedDB](https://developer.mozilla.org/en-US/docs/Web/API/IndexedDB_API);\n * then load it back.\n *\n * ```js\n * const model = tf.sequential(\n *     {layers: [tf.layers.dense({units: 1, inputShape: [3]})]});\n * console.log('Prediction from original model:');\n * model.predict(tf.ones([1, 3])).print();\n *\n * const saveResults = await model.save('indexeddb://my-model-1');\n *\n * const loadedModel = await tf.loadLayersModel('indexeddb://my-model-1');\n * console.log('Prediction from loaded model:');\n * loadedModel.predict(tf.ones([1, 3])).print();\n * ```\n *\n * Example 4. Load a model from user-selected files from HTML\n * [file input\n * elements](https://developer.mozilla.org/en-US/docs/Web/HTML/Element/input/file).\n *\n * ```js\n * // Note: this code snippet will not work without the HTML elements in the\n * //   page\n * const jsonUpload = document.getElementById('json-upload');\n * const weightsUpload = document.getElementById('weights-upload');\n *\n * const model = await tf.loadLayersModel(\n *     tf.io.browserFiles([jsonUpload.files[0], weightsUpload.files[0]]));\n * ```\n *\n * @param pathOrIOHandler Can be either of the two formats\n *   1. A string path to the `ModelAndWeightsConfig` JSON describing\n *      the model in the canonical TensorFlow.js format. For file://\n *      (tfjs-node-only), http:// and https:// schemas, the path can be\n *      either absolute or relative.\n *   2. An `tf.io.IOHandler` object that loads model artifacts with its `load`\n *      method.\n * @param options Optional configuration arguments for the model loading,\n *   including:\n *   - `strict`: Require that the provided weights exactly match those required\n *     by the layers.  Default true.  Passing false means that both extra\n *     weights and missing weights will be silently ignored.\n *   - `onProgress`: A function of the signature `(fraction: number) => void',\n *     that can be used as the progress callback for the model loading.\n * @returns A `Promise` of `tf.LayersModel`, with the topology and weights\n *     loaded.\n *\n * @doc {heading: 'Models', subheading: 'Loading'}\n */\nexport function loadLayersModel(\n    pathOrIOHandler: string|io.IOHandler,\n    options?: io.LoadOptions): Promise<LayersModel> {\n  if (options == null) {\n    options = {};\n  }\n  return loadLayersModelInternal(pathOrIOHandler, options);\n}\n\n/**\n * Used to instantiate an input to a model as a `tf.SymbolicTensor`.\n *\n * Users should call the `input` factory function for\n * consistency with other generator functions.\n *\n * Example:\n *\n * ```js\n * // Defines a simple logistic regression model with 32 dimensional input\n * // and 3 dimensional output.\n * const x = tf.input({shape: [32]});\n * const y = tf.layers.dense({units: 3, activation: 'softmax'}).apply(x);\n * const model = tf.model({inputs: x, outputs: y});\n * model.predict(tf.ones([2, 32])).print();\n * ```\n *\n * Note: `input` is only necessary when using `model`. When using\n * `sequential`, specify `inputShape` for the first layer or use `inputLayer`\n * as the first layer.\n *\n * @doc {heading: 'Models', subheading: 'Inputs'}\n */\nexport function input(config: InputConfig): SymbolicTensor {\n  return Input(config);\n}\n\nexport function registerCallbackConstructor(\n    verbosityLevel: number,\n    callbackConstructor: BaseCallbackConstructor): void {\n  CallbackConstructorRegistry.registerCallbackConstructor(\n      verbosityLevel, callbackConstructor);\n}\n"]}