"use strict";
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Object.defineProperty(exports, "__esModule", { value: true });
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/**
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* Validate sparseToDense inputs.
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*
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* @param sparseIndices A 0-D, 1-D, or 2-D Tensor of type int32.
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* sparseIndices[i] contains the complete index where sparseValues[i] will be
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* placed.
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* @param sparseValues A 0-D or 1-D Tensor. Values
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* corresponding to each row of sparseIndices, or a scalar value to be used for
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* all sparse indices.
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* @param outputShape number[]. Shape of the dense output tensor.
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* @param validateIndices boolean. indice validation is not supported, error
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* will be thrown if it is set.
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*/
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function validateInput(sparseIndices, sparseValues, outputShape, defaultValues) {
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if (sparseIndices.dtype !== 'int32') {
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throw new Error('tf.sparseToDense() expects the indices to be int32 type,' +
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(" but the dtype was " + sparseIndices.dtype + "."));
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}
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if (sparseIndices.rank > 2) {
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throw new Error('sparseIndices should be a scalar, vector, or matrix,' +
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(" but got shape " + sparseIndices.shape + "."));
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}
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var numElems = sparseIndices.rank > 0 ? sparseIndices.shape[0] : 1;
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var numDims = sparseIndices.rank > 1 ? sparseIndices.shape[1] : 1;
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if (outputShape.length !== numDims) {
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throw new Error('outputShape has incorrect number of elements:,' +
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(" " + outputShape.length + ", should be: " + numDims + "."));
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}
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var numValues = sparseValues.size;
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if (!(sparseValues.rank === 0 ||
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sparseValues.rank === 1 && numValues === numElems)) {
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throw new Error('sparseValues has incorrect shape ' +
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(sparseValues.shape + ", should be [] or [" + numElems + "]"));
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}
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if (sparseValues.dtype !== defaultValues.dtype) {
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throw new Error('sparseValues.dtype must match defaultValues.dtype');
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}
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}
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exports.validateInput = validateInput;
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//# sourceMappingURL=sparse_to_dense_util.js.map
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