@tensorflow/tfjs-node - npm Package Compare versions

binding.gyp-e

dist/tfjs_binding.d.ts

dist/tfjs_binding.js

dist/tfjs_binding.js.map

package.json-e

scripts/resources.js

scripts/test-ts-integration.sh

src/callbacks_test.ts

src/callbacks.ts

src/canvas_test.ts

src/index_test.ts

src/index.ts

src/int64_tensors_test.ts

src/int64_tensors.ts

src/io/file_system_test.ts

src/io/file_system.ts

src/io/index.ts

src/io/io_utils_test.ts

src/io/io_utils.ts

src/io/node_http_test.ts

src/io/node_http.ts

src/node.ts

src/nodejs_kernel_backend_test.ts

src/nodejs_kernel_backend.ts

src/ops/op_utils_test.ts

src/ops/op_utils.ts

src/run_tests.ts

src/tensorboard_test.ts

src/tensorboard.ts

src/tfjs_binding_test.ts

src/tfjs_binding.ts

src/version.ts

14

dist/callbacks_test.js

		@@ -237,3 +237,3 @@ "use strict";
		yDataset = tf.data.array([[1], [2], [3], [4]]).map(function (y) { return tf.tensor2d(y, [1, 1]); });
		dataset = tf.data.zip([xDataset, yDataset]).repeat(epochs);
		dataset = tf.data.zip({ xs: xDataset, ys: yDataset }).repeat(epochs);
		model = tf.sequential();
		@@ -274,12 +274,12 @@ model.add(tf.layers.dense({ units: 1, inputShape: [2] }));
		yDataset = tf.data.array([[1], [2], [3], [4]]).map(function (y) { return tf.tensor2d(y, [1, 1]); });
		dataset = tf.data.zip([xDataset, yDataset]).repeat(epochs);
		dataset = tf.data.zip({ xs: xDataset, ys: yDataset }).repeat(epochs);
		model = tf.sequential();
		model.add(tf.layers.dense({ units: 1, inputShape: [2] }));
		model.compile({ loss: 'meanSquaredError', optimizer: 'sgd' });
		// `batchesPerEpoch` is not specified. Instead, `fitDataset()` relies on the
		// `done` field being `true` to terminate the epoch(s).
		// `batchesPerEpoch` is not specified. Instead, `fitDataset()` relies on
		// the `done` field being `true` to terminate the epoch(s).
		return [4 /yield/, model.fitDataset(dataset, { epochs: epochs, verbose: 1 })];
		case 1:
		// `batchesPerEpoch` is not specified. Instead, `fitDataset()` relies on the
		// `done` field being `true` to terminate the epoch(s).
		// `batchesPerEpoch` is not specified. Instead, `fitDataset()` relies on
		// the `done` field being `true` to terminate the epoch(s).
		_a.sent();
		@@ -314,3 +314,3 @@ expect(consoleMessages.length).toEqual(4);
		yDataset = tf.data.array([[1], [2], [3], [4]]).map(function (y) { return tf.tensor2d(y, [1, 1]); });
		dataset = tf.data.zip([xDataset, yDataset]);
		dataset = tf.data.zip({ xs: xDataset, ys: yDataset });
		model = tf.sequential();
		@@ -317,0 +317,0 @@ model.add(tf.layers.dense({ units: 1, inputShape: [2] }));

89

dist/callbacks.d.ts

		@@ -46,1 +46,90 @@ /**
		export declare function getDisplayDecimalPlaces(x: number): number;
		export interface TensorBoardCallbackArgs {
		/**
		* The frequency at which loss and metric values are written to logs.
		*
		* Currently supported options are:
		*
		* - 'batch': Write logs at the end of every batch of training, in addition
		* to the end of every epoch of training.
		* - 'epoch': Write logs at the end of every epoch of training.
		*
		* Note that writing logs too often slows down the training.
		*
		* Default: 'epoch'.
		*/
		updateFreq?: 'batch' \| 'epoch';
		}
		/**
		* Callback for logging to TensorBoard durnig training.
		*
		* Users are expected to access this class through the `tensorBoardCallback()`
		* factory method instead.
		*/
		export declare class TensorBoardCallback extends CustomCallback {
		readonly logdir: string;
		private trainWriter;
		private valWriter;
		private batchesSeen;
		private epochsSeen;
		private readonly args;
		constructor(logdir?: string, args?: TensorBoardCallbackArgs);
		private logMetrics;
		private ensureTrainWriterCreated;
		private ensureValWriterCreated;
		}
		/**
		* Callback for logging to TensorBoard durnig training.
		*
		* Writes the loss and metric values (if any) to the specified log directory
		* (`logdir`) which can be ingested and visualized by TensorBoard.
		* This callback is usually passed as a callback to `tf.Model.fit()` or
		* `tf.Model.fitDataset()` calls during model training. The frequency at which
		* the values are logged can be controlled with the `updateFreq` field of the
		* configuration object (2nd argument).
		*
		* Usage example:
		* ```js
		* // Constructor a toy multilayer-perceptron regressor for demo purpose.
		* const model = tf.sequential();
		* model.add(
		* tf.layers.dense({units: 100, activation: 'relu', inputShape: [200]}));
		* model.add(tf.layers.dense({units: 1}));
		* model.compile({
		* loss: 'meanSquaredError',
		* optimizer: 'sgd',
		* metrics: ['MAE']
		* });
		*
		* // Generate some random fake data for demo purpose.
		* const xs = tf.randomUniform([10000, 200]);
		* const ys = tf.randomUniform([10000, 1]);
		* const valXs = tf.randomUniform([1000, 200]);
		* const valYs = tf.randomUniform([1000, 1]);
		*
		* // Start model training process.
		* await model.fit(xs, ys, {
		* epochs: 100,
		* validationData: [valXs, valYs],
		* // Add the tensorBoard callback here.
		* callbacks: tf.node.tensorBoard('/tmp/fit_logs_1')
		* });
		* ```
		*
		* Then you can use the following commands to point tensorboard
		* to the logdir:
		*
		* ```sh
		* pip install tensorboard # Unless you've already installed it.
		* tensorboard --logdir /tmp/fit_logs_1
		* ```
		*
		* @param logdir Directory to which the logs will be written.
		* @param args Optional configuration arguments.
		* @returns An instance of `TensorBoardCallback`, which is a subclass of
		* `tf.CustomCallback`.
		*/
		/**
		* @doc {heading: 'TensorBoard', namespace: 'node'}
		*/
		export declare function tensorBoard(logdir?: string, args?: TensorBoardCallbackArgs): TensorBoardCallback;

146

dist/callbacks.js

		@@ -19,5 +19,8 @@ "use strict";
		var __extends = (this && this.__extends) \|\| (function () {
		var extendStatics = Object.setPrototypeOf \|\|
		({ __proto__: [] } instanceof Array && function (d, b) { d.__proto__ = b; }) \|\|
		function (d, b) { for (var p in b) if (b.hasOwnProperty(p)) d[p] = b[p]; };
		var extendStatics = function (d, b) {
		extendStatics = Object.setPrototypeOf \|\|
		({ __proto__: [] } instanceof Array && function (d, b) { d.__proto__ = b; }) \|\|
		function (d, b) { for (var p in b) if (b.hasOwnProperty(p)) d[p] = b[p]; };
		return extendStatics(d, b);
		};
		return function (d, b) {
		@@ -66,3 +69,5 @@ extendStatics(d, b);
		var tfjs_1 = require("@tensorflow/tfjs");
		var path = require("path");
		var ProgressBar = require("progress");
		var tensorboard_1 = require("./tensorboard");
		// A helper class created for testing with the jasmine `spyOn` method, which
		@@ -208,1 +213,136 @@ // operates only on member methods of objects.
		exports.getDisplayDecimalPlaces = getDisplayDecimalPlaces;
		/**
		* Callback for logging to TensorBoard durnig training.
		*
		* Users are expected to access this class through the `tensorBoardCallback()`
		* factory method instead.
		*/
		var TensorBoardCallback = /** @class */ (function (_super) {
		__extends(TensorBoardCallback, _super);
		function TensorBoardCallback(logdir, args) {
		if (logdir === void 0) { logdir = './logs'; }
		var _this = _super.call(this, {
		onBatchEnd: function (batch, logs) { return __awaiter(_this, void 0, void 0, function () {
		return __generator(this, function (_a) {
		this.batchesSeen++;
		if (this.args.updateFreq !== 'epoch') {
		this.logMetrics(logs, 'batch_', this.batchesSeen);
		}
		return [2 /return/];
		});
		}); },
		onEpochEnd: function (epoch, logs) { return __awaiter(_this, void 0, void 0, function () {
		return __generator(this, function (_a) {
		this.epochsSeen++;
		this.logMetrics(logs, 'epoch_', this.epochsSeen);
		return [2 /return/];
		});
		}); },
		onTrainEnd: function (logs) { return __awaiter(_this, void 0, void 0, function () {
		return __generator(this, function (_a) {
		if (this.trainWriter != null) {
		this.trainWriter.flush();
		}
		if (this.valWriter != null) {
		this.valWriter.flush();
		}
		return [2 /return/];
		});
		}); }
		}) \|\| this;
		_this.logdir = logdir;
		_this.args = args == null ? {} : args;
		if (_this.args.updateFreq == null) {
		_this.args.updateFreq = 'epoch';
		}
		tfjs_1.util.assert(['batch', 'epoch'].indexOf(_this.args.updateFreq) !== -1, function () { return "Expected updateFreq to be 'batch' or 'epoch', but got " +
		("" + _this.args.updateFreq); });
		_this.batchesSeen = 0;
		_this.epochsSeen = 0;
		return _this;
		}
		TensorBoardCallback.prototype.logMetrics = function (logs, prefix, step) {
		for (var key in logs) {
		if (key === 'batch' \|\| key === 'size' \|\| key === 'num_steps') {
		continue;
		}
		var VAL_PREFIX = 'val_';
		if (key.startsWith(VAL_PREFIX)) {
		this.ensureValWriterCreated();
		var scalarName = prefix + key.slice(VAL_PREFIX.length);
		this.valWriter.scalar(scalarName, logs[key], step);
		}
		else {
		this.ensureTrainWriterCreated();
		this.trainWriter.scalar("" + prefix + key, logs[key], step);
		}
		}
		};
		TensorBoardCallback.prototype.ensureTrainWriterCreated = function () {
		this.trainWriter = tensorboard_1.summaryFileWriter(path.join(this.logdir, 'train'));
		};
		TensorBoardCallback.prototype.ensureValWriterCreated = function () {
		this.valWriter = tensorboard_1.summaryFileWriter(path.join(this.logdir, 'val'));
		};
		return TensorBoardCallback;
		}(tfjs_1.CustomCallback));
		exports.TensorBoardCallback = TensorBoardCallback;
		/**
		* Callback for logging to TensorBoard durnig training.
		*
		* Writes the loss and metric values (if any) to the specified log directory
		* (`logdir`) which can be ingested and visualized by TensorBoard.
		* This callback is usually passed as a callback to `tf.Model.fit()` or
		* `tf.Model.fitDataset()` calls during model training. The frequency at which
		* the values are logged can be controlled with the `updateFreq` field of the
		* configuration object (2nd argument).
		*
		* Usage example:
		* ```js
		* // Constructor a toy multilayer-perceptron regressor for demo purpose.
		* const model = tf.sequential();
		* model.add(
		* tf.layers.dense({units: 100, activation: 'relu', inputShape: [200]}));
		* model.add(tf.layers.dense({units: 1}));
		* model.compile({
		* loss: 'meanSquaredError',
		* optimizer: 'sgd',
		* metrics: ['MAE']
		* });
		*
		* // Generate some random fake data for demo purpose.
		* const xs = tf.randomUniform([10000, 200]);
		* const ys = tf.randomUniform([10000, 1]);
		* const valXs = tf.randomUniform([1000, 200]);
		* const valYs = tf.randomUniform([1000, 1]);
		*
		* // Start model training process.
		* await model.fit(xs, ys, {
		* epochs: 100,
		* validationData: [valXs, valYs],
		* // Add the tensorBoard callback here.
		* callbacks: tf.node.tensorBoard('/tmp/fit_logs_1')
		* });
		* ```
		*
		* Then you can use the following commands to point tensorboard
		* to the logdir:
		*
		* ```sh
		* pip install tensorboard # Unless you've already installed it.
		* tensorboard --logdir /tmp/fit_logs_1
		* ```
		*
		* @param logdir Directory to which the logs will be written.
		* @param args Optional configuration arguments.
		* @returns An instance of `TensorBoardCallback`, which is a subclass of
		* `tf.CustomCallback`.
		*/
		/**
		* @doc {heading: 'TensorBoard', namespace: 'node'}
		*/
		function tensorBoard(logdir, args) {
		if (logdir === void 0) { logdir = './logs'; }
		return new TensorBoardCallback(logdir, args);
		}
		exports.tensorBoard = tensorBoard;

8

dist/canvas_test.js

		@@ -42,7 +42,7 @@ "use strict";
		}());
		describe('tf.fromPixels', function () {
		describe('tf.browser.fromPixels with polyfills', function () {
		it('accepts a canvas-like element', function () {
		var c = new MockCanvas(2, 2);
		// tslint:disable-next-line:no-any
		var t = tf.fromPixels(c);
		var t = tf.browser.fromPixels(c);
		expect(t.dtype).toBe('int32');
		@@ -55,3 +55,3 @@ expect(t.shape).toEqual([2, 2, 3]);
		// tslint:disable-next-line:no-any
		var t = tf.fromPixels(c, 4);
		var t = tf.browser.fromPixels(c, 4);
		expect(t.dtype).toBe('int32');
		@@ -64,5 +64,5 @@ expect(t.shape).toEqual([2, 2, 4]);
		// tslint:disable-next-line:no-any
		expect(function () { return tf.fromPixels(c); })
		expect(function () { return tf.browser.fromPixels(c); })
		.toThrowError(/When running in node, pixels must be an HTMLCanvasElement/);
		});
		});

17

dist/index.js

		@@ -18,9 +18,12 @@ "use strict";
		*/
		var __assign = (this && this.__assign) \|\| Object.assign \|\| function(t) {
		for (var s, i = 1, n = arguments.length; i < n; i++) {
		s = arguments[i];
		for (var p in s) if (Object.prototype.hasOwnProperty.call(s, p))
		t[p] = s[p];
		}
		return t;
		var __assign = (this && this.__assign) \|\| function () {
		__assign = Object.assign \|\| function(t) {
		for (var s, i = 1, n = arguments.length; i < n; i++) {
		s = arguments[i];
		for (var p in s) if (Object.prototype.hasOwnProperty.call(s, p))
		t[p] = s[p];
		}
		return t;
		};
		return __assign.apply(this, arguments);
		};
		@@ -27,0 +30,0 @@ function __export(m) {

8

dist/int64_tensors.js

		@@ -54,5 +54,7 @@ "use strict";
		}
		tfjs_1.util.assert(value > -INT32_MAX && value < INT32_MAX - 1, "Got a value outside of the bound of values supported for int64 " +
		("dtype ([-" + INT32_MAX + ", " + (INT32_MAX - 1) + "]): " + value));
		tfjs_1.util.assert(Number.isInteger(value), "Expected value to be an integer, but got " + value);
		tfjs_1.util.assert(value > -INT32_MAX && value < INT32_MAX - 1, function () {
		return "Got a value outside of the bound of values supported for int64 " +
		("dtype ([-" + INT32_MAX + ", " + (INT32_MAX - 1) + "]): " + value);
		});
		tfjs_1.util.assert(Number.isInteger(value), function () { return "Expected value to be an integer, but got " + value; });
		// We use two int32 elements to represent a int64 value. This assumes
		@@ -59,0 +61,0 @@ // little endian, which is checked above.

8

dist/io/file_system.d.ts

		@@ -19,7 +19,7 @@ /**
		export declare class NodeFileSystem implements tfc.io.IOHandler {
		static readonly URL_SCHEME: string;
		static readonly URL_SCHEME = "file://";
		protected readonly path: string \| string[];
		readonly MODEL_JSON_FILENAME: string;
		readonly WEIGHTS_BINARY_FILENAME: string;
		readonly MODEL_BINARY_FILENAME: string;
		readonly MODEL_JSON_FILENAME = "model.json";
		readonly WEIGHTS_BINARY_FILENAME = "weights.bin";
		readonly MODEL_BINARY_FILENAME = "tensorflowjs.pb";
		/**
		@@ -26,0 +26,0 @@ * Constructor of the NodeFileSystem IOHandler.

4

dist/io/file_system.js

		@@ -98,4 +98,4 @@ "use strict";
		if (Array.isArray(path)) {
		tfc.util.assert(path.length === 2, 'file paths must have a length of 2, ' +
		("(actual length is " + path.length + ")."));
		tfc.util.assert(path.length === 2, function () { return 'file paths must have a length of 2, ' +
		("(actual length is " + path.length + ")."); });
		this.path = path.map(function (p) { return path_1.resolve(p); });
		@@ -102,0 +102,0 @@ }

2

dist/io/node_http_test.js

		@@ -192,3 +192,3 @@ "use strict";
		});
		return [4 /yield/, tfl.loadModel('https://localhost/model.json')];
		return [4 /yield/, tfl.loadLayersModel('https://localhost/model.json')];
		case 1:
		@@ -195,0 +195,0 @@ model = _a.sent();

4

dist/io/node_http.d.ts

		@@ -29,3 +29,3 @@ /**
		*/
		export declare function nodeHTTPRequest(path: string \| string[], requestInit?: RequestInit, weightPathPrefix?: string): io.IOHandler;
		export declare const nodeHTTPRequestRouter: (url: string \| string[]) => io.IOHandler;
		export declare function nodeHTTPRequest(path: string, requestInit?: RequestInit, weightPathPrefix?: string): io.IOHandler;
		export declare const nodeHTTPRequestRouter: (url: string) => io.IOHandler;

2

dist/io/node_http.js

		@@ -33,3 +33,3 @@ "use strict";
		function nodeHTTPRequest(path, requestInit, weightPathPrefix) {
		return tfjs_core_1.io.browserHTTPRequest(path, requestInit, weightPathPrefix, exports.fetchWrapper.fetch);
		return tfjs_core_1.io.browserHTTPRequest(path, { requestInit: requestInit, weightPathPrefix: weightPathPrefix, fetchFunc: exports.fetchWrapper.fetch });
		}
		@@ -36,0 +36,0 @@ exports.nodeHTTPRequest = nodeHTTPRequest;

2

dist/node.d.ts

		@@ -20,5 +20,7 @@ /**
		*/
		import { tensorBoard } from './callbacks';
		import { summaryFileWriter } from './tensorboard';
		export declare const node: {
		summaryFileWriter: typeof summaryFileWriter;
		tensorBoard: typeof tensorBoard;
		};

6

dist/node.js

		@@ -22,3 +22,7 @@ "use strict";
		*/
		var callbacks_1 = require("./callbacks");
		var tensorboard_1 = require("./tensorboard");
		exports.node = { summaryFileWriter: tensorboard_1.summaryFileWriter };
		exports.node = {
		summaryFileWriter: tensorboard_1.summaryFileWriter,
		tensorBoard: callbacks_1.tensorBoard
		};

4

dist/nodejs_kernel_backend.d.ts

		@@ -27,2 +27,3 @@ /**
		setDataMover(dataMover: DataMover): void;
		private getDTypeInteger;
		private typeAttributeFromTensor;
		@@ -57,2 +58,5 @@ private createOutputTensor;
		register(dataId: object, shape: number[], dtype: DataType): void;
		fill<R extends Rank>(shape: ShapeMap[R], value: number \| string, dtype?: DataType): Tensor<R>;
		onesLike<R extends Rank>(x: Tensor<R>): Tensor<R>;
		zerosLike<R extends Rank>(x: Tensor<R>): Tensor<R>;
		stridedSlice<T extends Tensor>(x: T, begin: number[], end: number[], strides: number[], beginMask: number, endMask: number, ellipsisMask: number, newAxisMask: number, shrinkAxisMask: number): T;
		@@ -59,0 +63,0 @@ unstack(x: Tensor<Rank>, axis: number): Tensor[];

4

dist/run_tests.js

		@@ -52,3 +52,5 @@ "use strict";
		'maxPool test-tensorflow {} x=[2,2,3] f=[1,1] s=2 p=1 dimRoundingMode=floor',
		'avgPool test-tensorflow {} x=[2,2,3] f=[1,1] s=2 p=1 dimRoundingMode=floor'
		'avgPool test-tensorflow {} x=[2,2,3] f=[1,1] s=2 p=1 dimRoundingMode=floor',
		// libtensorflow doesn't support 6D ArgMax yet.
		'Reduction: argmax test-tensorflow {} 6D, axis=0'
		];
		@@ -55,0 +57,0 @@ // Windows has two failing tests:

184

dist/tensorboard_test.js

		@@ -18,2 +18,38 @@ "use strict";
		*/
		var __awaiter = (this && this.__awaiter) \|\| function (thisArg, _arguments, P, generator) {
		return new (P \|\| (P = Promise))(function (resolve, reject) {
		function fulfilled(value) { try { step(generator.next(value)); } catch (e) { reject(e); } }
		function rejected(value) { try { step(generator["throw"](value)); } catch (e) { reject(e); } }
		function step(result) { result.done ? resolve(result.value) : new P(function (resolve) { resolve(result.value); }).then(fulfilled, rejected); }
		step((generator = generator.apply(thisArg, _arguments \|\| [])).next());
		});
		};
		var __generator = (this && this.__generator) \|\| function (thisArg, body) {
		var _ = { label: 0, sent: function() { if (t[0] & 1) throw t[1]; return t[1]; }, trys: [], ops: [] }, f, y, t, g;
		return g = { next: verb(0), "throw": verb(1), "return": verb(2) }, typeof Symbol === "function" && (g[Symbol.iterator] = function() { return this; }), g;
		function verb(n) { return function (v) { return step([n, v]); }; }
		function step(op) {
		if (f) throw new TypeError("Generator is already executing.");
		while (_) try {
		if (f = 1, y && (t = op[0] & 2 ? y["return"] : op[0] ? y["throw"] \|\| ((t = y["return"]) && t.call(y), 0) : y.next) && !(t = t.call(y, op[1])).done) return t;
		if (y = 0, t) op = [op[0] & 2, t.value];
		switch (op[0]) {
		case 0: case 1: t = op; break;
		case 4: _.label++; return { value: op[1], done: false };
		case 5: _.label++; y = op[1]; op = [0]; continue;
		case 7: op = _.ops.pop(); _.trys.pop(); continue;
		default:
		if (!(t = _.trys, t = t.length > 0 && t[t.length - 1]) && (op[0] === 6 \|\| op[0] === 2)) { _ = 0; continue; }
		if (op[0] === 3 && (!t \|\| (op[1] > t[0] && op[1] < t[3]))) { _.label = op[1]; break; }
		if (op[0] === 6 && _.label < t[1]) { _.label = t[1]; t = op; break; }
		if (t && _.label < t[2]) { _.label = t[2]; _.ops.push(op); break; }
		if (t[2]) _.ops.pop();
		_.trys.pop(); continue;
		}
		op = body.call(thisArg, _);
		} catch (e) { op = [6, e]; y = 0; } finally { f = t = 0; }
		if (op[0] & 5) throw op[1]; return { value: op[0] ? op[1] : void 0, done: true };
		}
		};
		var _this = this;
		Object.defineProperty(exports, "__esModule", { value: true });
		@@ -126,1 +162,149 @@ var tfjs_1 = require("@tensorflow/tfjs");
		});
		describe('tensorBoard callback', function () {
		var tmpLogDir;
		beforeEach(function () {
		tmpLogDir = tmp.dirSync().name;
		});
		afterEach(function () {
		if (tmpLogDir != null) {
		shelljs.rm('-rf', tmpLogDir);
		}
		});
		function createModelForTest() {
		var model = tfn.sequential();
		model.add(tfn.layers.dense({ units: 5, activation: 'relu', inputShape: [10] }));
		model.add(tfn.layers.dense({ units: 1 }));
		model.compile({ loss: 'meanSquaredError', optimizer: 'sgd', metrics: ['MAE'] });
		return model;
		}
		it('fit(): default epoch updateFreq, with validation', function () { return __awaiter(_this, void 0, void 0, function () {
		var model, xs, ys, valXs, valYs, subDirs, trainLogDir, trainFiles, trainFileSize0, valLogDir, valFiles, valFileSize0, history, trainFileSize1, valFileSize1;
		return __generator(this, function (_a) {
		switch (_a.label) {
		case 0:
		model = createModelForTest();
		xs = tfn.randomUniform([100, 10]);
		ys = tfn.randomUniform([100, 1]);
		valXs = tfn.randomUniform([10, 10]);
		valYs = tfn.randomUniform([10, 1]);
		// Warm-up training.
		return [4 /yield/, model.fit(xs, ys, {
		epochs: 1,
		verbose: 0,
		validationData: [valXs, valYs],
		callbacks: tfn.node.tensorBoard(tmpLogDir)
		})];
		case 1:
		// Warm-up training.
		_a.sent();
		subDirs = fs.readdirSync(tmpLogDir);
		expect(subDirs).toContain('train');
		expect(subDirs).toContain('val');
		trainLogDir = path.join(tmpLogDir, 'train');
		trainFiles = fs.readdirSync(trainLogDir);
		trainFileSize0 = fs.statSync(path.join(trainLogDir, trainFiles[0])).size;
		expect(trainFileSize0).toBeGreaterThan(0);
		valLogDir = path.join(tmpLogDir, 'val');
		valFiles = fs.readdirSync(valLogDir);
		valFileSize0 = fs.statSync(path.join(valLogDir, valFiles[0])).size;
		expect(valFileSize0).toBeGreaterThan(0);
		// With updateFreq === epoch, the train and val subset should have generated
		// the same amount of logs.
		expect(valFileSize0).toEqual(trainFileSize0);
		return [4 /yield/, model.fit(xs, ys, {
		epochs: 3,
		verbose: 0,
		validationData: [valXs, valYs],
		callbacks: tfn.node.tensorBoard(tmpLogDir)
		})];
		case 2:
		history = _a.sent();
		expect(history.history.loss.length).toEqual(3);
		expect(history.history.val_loss.length).toEqual(3);
		expect(history.history.MAE.length).toEqual(3);
		expect(history.history.val_MAE.length).toEqual(3);
		trainFileSize1 = fs.statSync(path.join(trainLogDir, trainFiles[0])).size;
		valFileSize1 = fs.statSync(path.join(valLogDir, valFiles[0])).size;
		// We currently only assert that new content has been written to the log
		// file.
		expect(trainFileSize1).toBeGreaterThan(trainFileSize0);
		expect(valFileSize1).toBeGreaterThan(valFileSize0);
		// With updateFreq === epoch, the train and val subset should have generated
		// the same amount of logs.
		expect(valFileSize1).toEqual(trainFileSize1);
		return [2 /return/];
		}
		});
		}); });
		it('fit(): batch updateFreq, with validation', function () { return __awaiter(_this, void 0, void 0, function () {
		var model, xs, ys, valXs, valYs, subDirs, trainLogDir, trainFiles, trainFileSize0, valLogDir, valFiles, valFileSize0, history, trainFileSize1, valFileSize1;
		return __generator(this, function (_a) {
		switch (_a.label) {
		case 0:
		model = createModelForTest();
		xs = tfn.randomUniform([100, 10]);
		ys = tfn.randomUniform([100, 1]);
		valXs = tfn.randomUniform([10, 10]);
		valYs = tfn.randomUniform([10, 1]);
		// Warm-up training.
		return [4 /yield/, model.fit(xs, ys, {
		epochs: 1,
		verbose: 0,
		validationData: [valXs, valYs],
		// Use batch updateFreq here.
		callbacks: tfn.node.tensorBoard(tmpLogDir, { updateFreq: 'batch' })
		})];
		case 1:
		// Warm-up training.
		_a.sent();
		subDirs = fs.readdirSync(tmpLogDir);
		expect(subDirs).toContain('train');
		expect(subDirs).toContain('val');
		trainLogDir = path.join(tmpLogDir, 'train');
		trainFiles = fs.readdirSync(trainLogDir);
		trainFileSize0 = fs.statSync(path.join(trainLogDir, trainFiles[0])).size;
		expect(trainFileSize0).toBeGreaterThan(0);
		valLogDir = path.join(tmpLogDir, 'val');
		valFiles = fs.readdirSync(valLogDir);
		valFileSize0 = fs.statSync(path.join(valLogDir, valFiles[0])).size;
		expect(valFileSize0).toBeGreaterThan(0);
		// The train subset should have generated more logs than the val subset,
		// because the train subset gets logged every batch, while the val subset
		// gets logged every epoch.
		expect(trainFileSize0).toBeGreaterThan(valFileSize0);
		return [4 /yield/, model.fit(xs, ys, {
		epochs: 3,
		verbose: 0,
		validationData: [valXs, valYs],
		callbacks: tfn.node.tensorBoard(tmpLogDir)
		})];
		case 2:
		history = _a.sent();
		expect(history.history.loss.length).toEqual(3);
		expect(history.history.val_loss.length).toEqual(3);
		expect(history.history.MAE.length).toEqual(3);
		expect(history.history.val_MAE.length).toEqual(3);
		trainFileSize1 = fs.statSync(path.join(trainLogDir, trainFiles[0])).size;
		valFileSize1 = fs.statSync(path.join(valLogDir, valFiles[0])).size;
		// We currently only assert that new content has been written to the log
		// file.
		expect(trainFileSize1).toBeGreaterThan(trainFileSize0);
		expect(valFileSize1).toBeGreaterThan(valFileSize0);
		// The train subset should have generated more logs than the val subset,
		// because the train subset gets logged every batch, while the val subset
		// gets logged every epoch.
		expect(trainFileSize1).toBeGreaterThan(valFileSize1);
		return [2 /return/];
		}
		});
		}); });
		it('Invalid updateFreq value causes error', function () { return __awaiter(_this, void 0, void 0, function () {
		return __generator(this, function (_a) {
		expect(function () { return tfn.node.tensorBoard(tmpLogDir, {
		// tslint:disable-next-line:no-any
		updateFreq: 'foo'
		}); }).toThrowError(/Expected updateFreq/);
		return [2 /return/];
		});
		}); });
		});

3

dist/tensorboard.d.ts

		@@ -62,2 +62,5 @@ /**
		*/
		/**
		* @doc {heading: 'TensorBoard', namespace: 'node'}
		*/
		export declare function summaryFileWriter(logdir: string, maxQueue?: number, flushMillis?: number, filenameSuffix?: string): SummaryFileWriter;

7

dist/tensorboard.js

		@@ -83,2 +83,5 @@ "use strict";
		*/
		/**
		* @doc {heading: 'TensorBoard', namespace: 'node'}
		*/
		function summaryFileWriter(logdir, maxQueue, flushMillis, filenameSuffix) {
		@@ -88,3 +91,5 @@ if (maxQueue === void 0) { maxQueue = 10; }
		if (filenameSuffix === void 0) { filenameSuffix = '.v2'; }
		tfjs_1.util.assert(logdir != null && typeof logdir === 'string' && logdir.length > 0, "Invalid logdir: " + logdir + ". Expected a non-empty string for logdir.");
		tfjs_1.util.assert(logdir != null && typeof logdir === 'string' && logdir.length > 0, function () {
		return "Invalid logdir: " + logdir + ". Expected a non-empty string for logdir.";
		});
		if (!(logdir in summaryFileWriterCache)) {
		@@ -91,0 +96,0 @@ var backend = op_utils_1.nodeBackend();

2

dist/version.d.ts

		/** @license See the LICENSE file. */
		declare const version = "1.0.0-alpha2";
		declare const version = "1.0.1";
		export { version };

2

dist/version.js

		@@ -5,3 +5,3 @@ "use strict";
		// This code is auto-generated, do not modify this file!
		var version = '1.0.0-alpha2';
		var version = '1.0.1';
		exports.version = version;

8

package.json

		{
		"name": "@tensorflow/tfjs-node",
		"version": "1.0.0-alpha2",
		"version": "1.0.1",
		"main": "dist/index.js",
		@@ -36,3 +36,3 @@ "types": "dist/index.d.ts",
		"jasmine": "~3.1.0",
		"nyc": "^12.0.2",
		"nyc": "^13.3.0",
		"shelljs": "^0.8.3",
		@@ -42,7 +42,7 @@ "tmp": "^0.0.33",
		"tslint": "~5.9.1",
		"typescript": "~2.9.2",
		"typescript": "3.3.3333",
		"yalc": "~1.0.0-pre.21"
		},
		"dependencies": {
		"@tensorflow/tfjs": "~1.0.0-alpha2",
		"@tensorflow/tfjs": "~1.0.1",
		"adm-zip": "^0.4.11",
		@@ -49,0 +49,0 @@ "bindings": "~1.3.0",

3

README.md

		@@ -7,5 +7,2 @@ <a id="travis-badge" href="https://travis-ci.org/tensorflow/tfjs-node" alt="Build Status">

		**This repo is under active development and is not production-ready. We are
		actively developing as an open source project.**

		## Installing
		@@ -12,0 +9,0 @@

3

scripts/deps-constants.js

		@@ -23,3 +23,3 @@ /**
		const frameworkLibName =
		os.platform() === 'linux' ? 'libtensorflow_framework.so' : '';
		os.platform() !== 'win32' ? 'libtensorflow_framework.so' : '';

		@@ -32,2 +32,3 @@ const depsPath = path.join(__dirname, '..', 'deps');
		module.exports = {
		depsLibPath,
		depsLibTensorFlowFrameworkPath,
		@@ -34,0 +35,0 @@ depsLibTensorFlowPath,

19

scripts/deps-stage.js

		@@ -53,6 +53,4 @@ /**
		await symlink(depsLibTensorFlowPath, destLibPath);
		if (os.platform() === 'linux') {
		await symlink(
		depsLibTensorFlowFrameworkPath,
		destFrameworkLibPath);
		if (os.platform() !== 'win32') {
		await symlink(depsLibTensorFlowFrameworkPath, destFrameworkLibPath);
		}
		@@ -63,7 +61,4 @@ } catch (e) {
		await copy(depsLibTensorFlowPath, destLibPath);
		// Linux will require this library as well:
		if (os.platform() === 'linux') {
		await copy(
		depsLibTensorFlowFrameworkPath,
		destFrameworkLibPath);
		if (os.platform() !== 'win32') {
		await copy(depsLibTensorFlowFrameworkPath, destFrameworkLibPath);
		}
		@@ -78,6 +73,4 @@ }
		await rename(depsLibTensorFlowPath, destLibPath);
		if (os.platform() === 'linux') {
		await rename(
		depsLibTensorFlowFrameworkPath,
		destFrameworkLibPath);
		if (os.platform() !== 'win32') {
		await rename(depsLibTensorFlowFrameworkPath, destFrameworkLibPath);
		}
		@@ -84,0 +77,0 @@ }

137

scripts/install.js

		/**
		* @license
		* Copyright 2018 Google Inc. All Rights Reserved.
		* Copyright 2019 Google Inc. All Rights Reserved.
		* Licensed under the Apache License, Version 2.0 (the "License");
		@@ -17,30 +17,31 @@ * you may not use this file except in compliance with the License.
		*/
		const https = require('https');
		const HttpsProxyAgent = require('https-proxy-agent');
		const url = require('url');
		const fs = require('fs');
		let path = require('path');
		const rimraf = require('rimraf');
		const tar = require('tar');
		const util = require('util');
		const zip = require('adm-zip');
		const cp = require('child_process');
		const os = require('os');
		const ProgressBar = require('progress');
		const {depsPath, depsLibTensorFlowPath} = require('./deps-constants.js');
		const {depsPath, depsLibPath, depsLibTensorFlowPath} =
		require('./deps-constants.js');
		const resources = require('./resources');

		const exists = util.promisify(fs.exists);
		const mkdir = util.promisify(fs.mkdir);
		const rename = util.promisify(fs.rename);
		const rimrafPromise = util.promisify(rimraf);
		const unlink = util.promisify(fs.unlink);
		const exec = util.promisify(cp.exec);

		const BASE_URI =
		'https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-';
		const CPU_DARWIN = 'cpu-darwin-x86_64-1.12.0.tar.gz';
		const CPU_LINUX = 'cpu-linux-x86_64-1.12.0.tar.gz';
		const GPU_LINUX = 'gpu-linux-x86_64-1.12.0.tar.gz';
		const CPU_WINDOWS = 'cpu-windows-x86_64-1.12.0.zip';
		const GPU_WINDOWS = 'gpu-windows-x86_64-1.12.0.zip';
		const CPU_DARWIN = 'cpu-darwin-x86_64-1.13.1.tar.gz';
		const CPU_LINUX = 'cpu-linux-x86_64-1.13.1.tar.gz';
		const GPU_LINUX = 'gpu-linux-x86_64-1.13.1.tar.gz';
		const CPU_WINDOWS = 'cpu-windows-x86_64-1.13.1.zip';
		const GPU_WINDOWS = 'gpu-windows-x86_64-1.13.1.zip';

		// TODO(kreeger): Update to TensorFlow 1.13:
		// https://github.com/tensorflow/tfjs/issues/1369
		const TF_WIN_HEADERS_URI =
		'https://storage.googleapis.com/tf-builds/tensorflow-headers-1.12.zip';

		const platform = os.platform();
		@@ -50,7 +51,11 @@ let libType = process.argv[2] === undefined ? 'cpu' : process.argv[2];

		let targetUri = BASE_URI;

		async function getTargetUri() {
		/**
		* Returns the libtensorflow hosted path of the current platform.
		*/
		function getPlatformLibtensorflowUri() {
		let targetUri = BASE_URI;
		if (platform === 'linux') {
		if (os.arch() === 'arm') {
		// TODO(kreeger): Update to TensorFlow 1.13:
		// https://github.com/tensorflow/tfjs/issues/1370
		targetUri =
		@@ -78,2 +83,3 @@ 'https://storage.googleapis.com/tf-builds/libtensorflow_r1_12_linux_arm.tar.gz';
		}
		return targetUri;
		}
		@@ -104,68 +110,51 @@
		async function downloadLibtensorflow(callback) {
		await getTargetUri();
		// The deps folder and resources do not exist, download and callback as
		// needed:
		console.error('* Downloading libtensorflow');

		// Ensure dependencies staged directory is available:
		await ensureDir(depsPath);

		// If HTTPS_PROXY, https_proxy, HTTP_PROXY, or http_proxy is set
		const proxy = process.env['HTTPS_PROXY'] \|\| process.env['https_proxy'] \|\|
		process.env['HTTP_PROXY'] \|\| process.env['http_proxy'] \|\| '';
		console.warn('* Downloading libtensorflow');
		resources.downloadAndUnpackResource(
		getPlatformLibtensorflowUri(), depsPath, async () => {
		if (platform === 'win32') {
		// Some windows libtensorflow zip files are missing structure and the
		// eager headers. Check, restructure, and download resources as
		// needed.
		const depsIncludePath = path.join(depsPath, 'include');
		if (!await exists(depsLibTensorFlowPath)) {
		// Verify that tensorflow.dll exists
		const libtensorflowDll = path.join(depsPath, 'tensorflow.dll');
		if (!await exists(libtensorflowDll)) {
		throw new Error('Could not find libtensorflow.dll');
		}

		// Using object destructuring to construct the options object for the
		// http request. the '...url.parse(targetUri)' part fills in the host,
		// path, protocol, etc from the targetUri and then we set the agent to the
		// default agent which is overridden a few lines down if there is a proxy
		const options = {...url.parse(targetUri), agent: https.globalAgent};
		await ensureDir(depsLibPath);
		await rename(libtensorflowDll, depsLibTensorFlowPath);
		}

		if (proxy !== '') {
		options.agent = new HttpsProxyAgent(proxy);
		}
		// Next check the structure for the C-library headers. If they don't
		// exist, download and unzip them.
		if (!await exists(depsIncludePath)) {
		// Remove duplicated assets from the original libtensorflow package.
		// They will be replaced by the download below:
		await unlink(path.join(depsPath, 'c_api.h'));
		await unlink(path.join(depsPath, 'LICENSE'));

		const request = https.get(options, response => {
		const bar = new ProgressBar('[:bar] :rate/bps :percent :etas', {
		complete: '=',
		incomplete: ' ',
		width: 30,
		total: parseInt(response.headers['content-length'], 10)
		});

		if (platform === 'win32') {
		// Windows stores builds in a zip file. Save to disk, extract, and delete
		// the downloaded archive.
		const tempFileName = path.join(__dirname, '_libtensorflow.zip');
		const outputFile = fs.createWriteStream(tempFileName);
		response
		.on('data',
		(chunk) => {
		bar.tick(chunk.length);
		})
		.pipe(outputFile)
		.on('close', async () => {
		const zipFile = new zip(tempFileName);
		zipFile.extractAllTo(depsPath, true /* overwrite */);
		await unlink(tempFileName);

		// Download the C headers only and unpack:
		resources.downloadAndUnpackResource(
		TF_WIN_HEADERS_URI, depsPath, () => {
		if (callback !== undefined) {
		callback();
		}
		});
		} else {
		if (callback !== undefined) {
		callback();
		}
		});
		} else {
		// All other platforms use a tarball:
		response
		.on('data',
		(chunk) => {
		bar.tick(chunk.length);
		})
		.pipe(tar.x({C: depsPath, strict: true}))
		.on('close', () => {
		if (callback !== undefined) {
		callback();
		}
		});
		}
		request.end();
		});
		}
		} else {
		// No other work is required on other platforms.
		if (callback !== undefined) {
		callback();
		}
		}
		});
		}
		@@ -172,0 +161,0 @@

31

WINDOWS_TROUBLESHOOTING.md

		@@ -9,2 +9,33 @@ # TensorFlow.js Node.js bindings Windows troubleshooting.

		## 'The system cannot find the patch specified' Exceptions

		This can happen for a variety of reasons. First, to inspect what is missing either `cd node_modules/@tensorflow/tfjs-node` or clone the [tensorflow/tfjs-node repo](https://github.com/tensorflow/tfjs-node).

		After `cd`'ing or cloning, run the following command (you might need node-gyp installed globablly `npm install -g node-gyp`):

		```sh
		node-gyp configure --verbose
		```

		### Missing `python2`

		From the verbose output, if you see something like:

		```sh
		gyp verb check python checking for Python executable "python2" in the PATH
		gyp verb `which` failed Error: not found: python2
		```

		This means that node-gyp expects a 'python2' exe somewhere in `%PATH%`. Try running this command from an Admin (elevated privilaged prompt):

		You can try running this from an Adminstrative prompt:

		```sh
		$ npm --add-python-to-path='true' --debug install --global windows-build-tools
		```

		### Something else?

		If another missing component shows up - please file an issue on [tensorflow/tfjs](https://github.com/tensorflow/tfjs/issues/new) with the output from the `node-gyp configure --verbose` command.

		## msbuild.exe Exceptions
		@@ -11,0 +42,0 @@

binding/tf_auto_status.h