@tensorflow/tfjs-core
Version:
Hardware-accelerated JavaScript library for machine intelligence
59 lines • 2.78 kB
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.
* =============================================================================
*/
import { ENGINE } from '../engine';
import { assert, assertNonNegativeIntegerDimensions, flatten, inferDtype, isTypedArray, sizeFromShape, toTypedArray } from '../util';
/** This is shared code across all tensor creation methods. */
export function makeTensor(values, shape, inferredShape, dtype) {
if (dtype == null) {
dtype = inferDtype(values);
}
if (dtype === 'complex64') {
throw new Error(`Cannot construct a complex64 tensor directly. ` +
`Please use tf.complex(real, imag).`);
}
if (!isTypedArray(values) && !Array.isArray(values) &&
typeof values !== 'number' && typeof values !== 'boolean' &&
typeof values !== 'string') {
throw new Error('values passed to tensor(values) must be a number/boolean/string or ' +
'an array of numbers/booleans/strings, or a TypedArray');
}
if (shape != null) {
assertNonNegativeIntegerDimensions(shape);
const providedSize = sizeFromShape(shape);
const inferredSize = sizeFromShape(inferredShape);
assert(providedSize === inferredSize, () => `Based on the provided shape, [${shape}], the tensor should have ` +
`${providedSize} values but has ${inferredSize}`);
for (let i = 0; i < inferredShape.length; ++i) {
const inferred = inferredShape[i];
const flatDimsDontMatch = i === inferredShape.length - 1 ?
inferred !== sizeFromShape(shape.slice(i)) :
true;
assert(inferredShape[i] === shape[i] || !flatDimsDontMatch, () => `Error creating a new Tensor. Inferred shape ` +
`(${inferredShape}) does not match the provided ` +
`shape (${shape}). `);
}
}
if (!isTypedArray(values) && !Array.isArray(values)) {
values = [values];
}
shape = shape || inferredShape;
values = dtype !== 'string' ?
toTypedArray(values, dtype) :
flatten(values, [], true);
return ENGINE.makeTensor(values, shape, dtype);
}
//# sourceMappingURL=tensor_ops_util.js.map