@tensorflow/tfjs-node
Version:
This repository provides native TensorFlow execution in backend JavaScript applications under the Node.js runtime, accelerated by the TensorFlow C binary under the hood. It provides the same API as [TensorFlow.js](https://js.tensorflow.org/api/latest/).
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JavaScript
;
/**
* @license
* Copyright 2018 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
Object.defineProperty(exports, "__esModule", { value: true });
exports.encodeInt32ArrayAsInt64 = exports.Int64Scalar = void 0;
var tfjs_1 = require("@tensorflow/tfjs");
var os_1 = require("os");
var INT32_MAX = 2147483648;
/**
* Node.js-specific tensor type: int64-type scalar.
*
* This class is created for a specific purpose: to support
* writing `step`s to TensorBoard via op-kernel bindings.
* `step` is required to have an int64 dtype, but TensorFlow.js
* (tfjs-core) doesn't have a built-in int64 dtype. This is
* related to a lack of `Int64Array` or `Uint64Array` typed
* array in basic JavaScript.
*
* This class is introduced as a workaround.
*/
var Int64Scalar = /** @class */ (function () {
function Int64Scalar(value) {
this.value = value;
this.dtype = 'int64';
this.rank = 1;
// The reason why we need to check endianness of the machine here is
// negative int64 values and the way in which we represent them
// using Int32Arrays in JavaScript. We represent each int64 value with
// two consecutive elements of an Int32Array. For positive values,
// the high part is simply zero; for negative values, the high part
// should be -1. The ordering of the low and high parts assumes
// little endian (i.e., least significant digits appear first).
// This assumption is checked by the lines below.
if (Int64Scalar.endiannessOkay_ == null) {
if ((0, os_1.endianness)() !== 'LE') {
throw new Error("Int64Scalar does not support endianness of this machine: " +
"".concat((0, os_1.endianness)()));
}
Int64Scalar.endiannessOkay_ = true;
}
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 ([-".concat(INT32_MAX, ", ").concat(INT32_MAX - 1, "]): ").concat(value);
});
tfjs_1.util.assert(Number.isInteger(value), function () { return "Expected value to be an integer, but got ".concat(value); });
// We use two int32 elements to represent a int64 value. This assumes
// little endian, which is checked above.
var highPart = value >= 0 ? 0 : -1;
var lowPart = value % INT32_MAX;
this.valueArray_ = new Int32Array([lowPart, highPart]);
}
Object.defineProperty(Int64Scalar.prototype, "shape", {
get: function () {
return [];
},
enumerable: false,
configurable: true
});
Object.defineProperty(Int64Scalar.prototype, "valueArray", {
/** Get the Int32Array that represents the int64 value. */
get: function () {
return this.valueArray_;
},
enumerable: false,
configurable: true
});
return Int64Scalar;
}());
exports.Int64Scalar = Int64Scalar;
/**
* This method encodes a Int32Array as Int64 layout in order to create TF_INT64
* tensor through binding.
*/
function encodeInt32ArrayAsInt64(value) {
if ((0, os_1.endianness)() !== 'LE') {
throw new Error("Int64Scalar does not support endianness of this machine: " +
"".concat((0, os_1.endianness)()));
}
var buffer = new Int32Array(value.length * 2);
for (var i = 0; i < value.length; i++) {
buffer[i * 2] = value[i];
}
return buffer;
}
exports.encodeInt32ArrayAsInt64 = encodeInt32ArrayAsInt64;