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ner-dl.mac._sparse_feature_cross_op.so Maven / Gradle / Ivy

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;?C-?[?????[%d??ȋBN=??D??2iWC??G3???#?I9P????KD??ؗ?Ҝ<@?0?B:?0?yE>e??A-C??6??cA{?G?z????.A????????L??]????????:??8??VUU??gff櫪??n۶??r??433?F]?VUU?wb'??m??xww??yxx?9???Q^C?????z?????.??Mo??????G?????????????:??:???8??8??VUUUUUU??gffffff櫪??????m۶m۶???q?q??4333333?]t?E]?VUUUUUU?(vb'vb'?ܶm۶m??xwwwwww??yxxxxxx??8??8????5??P^C??????????y??y????.?袋.???7??Mo?????????H?z?G??;?;???%???^B?n۶m۶m?|a??????????????{?????|???=<<<<<= 0hashed_output: boolnum_buckets: int >= 0sparse_types: list({int64, string}) >= 0dense_types: list({int64, string}) >= 0out_type: {int64, string}internal_type: {int64, string}
Generates sparse cross form a list of sparse tensors.

The op takes two lists, one of 2D `SparseTensor` and one of 2D `Tensor`, each
representing features of one feature column. It outputs a 2D `SparseTensor` with
the batchwise crosses of these features.

For example, if the inputs are

    inputs[0]: SparseTensor with shape = [2, 2]
    [0, 0]: "a"
    [1, 0]: "b"
    [1, 1]: "c"

    inputs[1]: SparseTensor with shape = [2, 1]
    [0, 0]: "d"
    [1, 0]: "e"

    inputs[2]: Tensor [["f"], ["g"]]

then the output will be

    shape = [2, 2]
    [0, 0]: "a_X_d_X_f"
    [1, 0]: "b_X_e_X_g"
    [1, 1]: "c_X_e_X_g"

if hashed_output=true then the output will be

    shape = [2, 2]
    [0, 0]: HashCombine(
                Fingerprint64("f"), HashCombine(
                    Fingerprint64("d"), Fingerprint64("a")))
    [1, 0]: HashCombine(
                Fingerprint64("g"), HashCombine(
                    Fingerprint64("e"), Fingerprint64("b")))
    [1, 1]: HashCombine(
                Fingerprint64("g"), HashCombine(
                    Fingerprint64("e"), Fingerprint64("c")))

indices: 2-D.  Indices of each input `SparseTensor`.
values: 1-D.   values of each `SparseTensor`.
shapes: 1-D.   Shapes of each `SparseTensor`.
dense: 2-D.    Columns represented by dense `Tensor`.
output_indices: 2-D.  Indices of the concatenated `SparseTensor`.
output_values: 1-D.  Non-empty values of the concatenated or hashed
  `SparseTensor`.
output_shape: 1-D.  Shape of the concatenated `SparseTensor`.
SparseFeatureCrossV2hash_key: int
Generates sparse cross form a list of sparse tensors.

The op takes two lists, one of 2D `SparseTensor` and one of 2D `Tensor`, each
representing features of one feature column. It outputs a 2D `SparseTensor` with
the batchwise crosses of these features.

For example, if the inputs are

    inputs[0]: SparseTensor with shape = [2, 2]
    [0, 0]: "a"
    [1, 0]: "b"
    [1, 1]: "c"

    inputs[1]: SparseTensor with shape = [2, 1]
    [0, 0]: "d"
    [1, 0]: "e"

    inputs[2]: Tensor [["f"], ["g"]]

then the output will be

    shape = [2, 2]
    [0, 0]: "a_X_d_X_f"
    [1, 0]: "b_X_e_X_g"
    [1, 1]: "c_X_e_X_g"

if hashed_output=true then the output will be

    shape = [2, 2]
    [0, 0]: FingerprintCat64(
                Fingerprint64("f"), FingerprintCat64(
                    Fingerprint64("d"), Fingerprint64("a")))
    [1, 0]: FingerprintCat64(
                Fingerprint64("g"), FingerprintCat64(
                    Fingerprint64("e"), Fingerprint64("b")))
    [1, 1]: FingerprintCat64(
                Fingerprint64("g"), FingerprintCat64(
                    Fingerprint64("e"), Fingerprint64("c")))

indices: 2-D.  Indices of each input `SparseTensor`.
values: 1-D.   values of each `SparseTensor`.
shapes: 1-D.   Shapes of each `SparseTensor`.
dense: 2-D.    Columns represented by dense `Tensor`.
output_indices: 2-D.  Indices of the concatenated `SparseTensor`.
output_values: 1-D.  Non-empty values of the concatenated or hashed
  `SparseTensor`.
output_shape: 1-D.  Shape of the concatenated `SparseTensor`.
out_typeinternal_typeSparseFeatureCrossOpSparseFeatureCrossOpSparseFeatureCrossOpSparseFeatureCrossOpSparseFeatureCrossOpSparseFeatureCrossOpnum_bucketsOP_REQUIRES_OK_ASYNCtensorflow/contrib/layers/kernels/sparse_feature_cross_kernel.ccindicesvaluesshapesdenseOP_REQUIRES_ASYNCInput indices should be a matrix but received shape  at position Expected D2 of index to be 2 got Expected  input values, got Input values should be a std::vector but received shape Expected size of values to be  got  input shapes, got Input shapes should be a std::vector but received shape shape should imply a 2D tensor, but got Expected batch size Dense inputs should be a matrix but received shape  at dense tensor allocator::allocate(size_t n) 'n' exceeds maximum supported sizefeature_counts_.size() == feature_start_indices_.size()_X_hash_keyabsl::string_view::substrString cannot end with \Value of \ exceeds 0xffString cannot end with \x\x cannot be followed by a non-hex digit\u must be followed by 4 hex digits: \\U must be followed by 8 hex digits: \ exceeds Unicode limit (0x10FFFF)Unknown escape sequence: \invalid surrogate character (0xD800-DFFF): \\n\r\t\"\'\\\x0123456789abcdef\external/com_google_absl/absl/strings/escaping.ccThis can't happen; base64 decoder state = %dLogic problem? szsrc = %zu000102030405060708090a0b0c0d0e0f101112131415161718191a1b1c1d1e1f202122232425262728292a2b2c2d2e2f303132333435363738393a3b3c3d3e3f404142434445464748494a4b4c4d4e4f505152535455565758595a5b5c5d5e5f606162636465666768696a6b6c6d6e6f707172737475767778797a7b7c7d7e7f808182838485868788898a8b8c8d8e8f909192939495969798999a9b9c9d9e9fa0a1a2a3a4a5a6a7a8a9aaabacadaeafb0b1b2b3b4b5b6b7b8b9babbbcbdbebfc0c1c2c3c4c5c6c7c8c9cacbcccdcecfd0d1d2d3d4d5d6d7d8d9dadbdcdddedfe0e1e2e3e4e5e6e7e8e9eaebecedeeeff0f1f2f3f4f5f6f7f8f9fafbfcfdfeffnfinityanexternal/com_google_absl/absl/strings/numbers.ccCheck %s failed: %sout != nullptrOutput pointer must not be nullptr.truetyesy1falsefnon00123456789abcdefexternal/com_google_absl/absl/strings/str_split.cclength > 0NULL0123456789abcdef[%s : %d] RAW: 
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