1 /* Copyright 2016 The TensorFlow Authors. All Rights Reserved. 2 3 Licensed under the Apache License, Version 2.0 (the "License"); 4 you may not use this file except in compliance with the License. 5 You may obtain a copy of the License at 6 7 http://www.apache.org/licenses/LICENSE-2.0 8 9 Unless required by applicable law or agreed to in writing, software 10 distributed under the License is distributed on an "AS IS" BASIS, 11 WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 12 See the License for the specific language governing permissions and 13 limitations under the License. 14 ==============================================================================*/ 15 16 #include "tensorflow/core/framework/op.h" 17 #include "tensorflow/core/framework/shape_inference.h" 18 19 namespace tensorflow { 20 REGISTER_OP("SparseFeatureCross") 21 .Input("indices: N * int64") 22 .Input("values: sparse_types") 23 .Input("shapes: N * int64") 24 .Input("dense: dense_types") 25 .Output("output_indices: int64") 26 .Output("output_values: out_type") 27 .Output("output_shape: int64") 28 .Attr("N: int >= 0") 29 .Attr("hashed_output: bool") 30 .Attr("num_buckets: int >= 0") 31 .Attr("sparse_types: list({int64, string}) >= 0") 32 .Attr("dense_types: list({int64, string}) >= 0") 33 .Attr("out_type: {int64, string}") 34 .Attr("internal_type: {int64, string}") 35 .SetShapeFn([](shape_inference::InferenceContext* c) { 36 c->set_output(0, c->Matrix(c->UnknownDim(), 2)); 37 c->set_output(1, c->Vector(c->UnknownDim())); 38 c->set_output(2, c->Vector(2)); 39 return Status::OK(); 40 }) 41 .Doc(R"doc( 42 Generates sparse cross form a list of sparse tensors. 43 44 The op takes two lists, one of 2D `SparseTensor` and one of 2D `Tensor`, each 45 representing features of one feature column. It outputs a 2D `SparseTensor` with 46 the batchwise crosses of these features. 47 48 For example, if the inputs are 49 50 inputs[0]: SparseTensor with shape = [2, 2] 51 [0, 0]: "a" 52 [1, 0]: "b" 53 [1, 1]: "c" 54 55 inputs[1]: SparseTensor with shape = [2, 1] 56 [0, 0]: "d" 57 [1, 0]: "e" 58 59 inputs[2]: Tensor [["f"], ["g"]] 60 61 then the output will be 62 63 shape = [2, 2] 64 [0, 0]: "a_X_d_X_f" 65 [1, 0]: "b_X_e_X_g" 66 [1, 1]: "c_X_e_X_g" 67 68 if hashed_output=true then the output will be 69 70 shape = [2, 2] 71 [0, 0]: HashCombine( 72 Fingerprint64("f"), HashCombine( 73 Fingerprint64("d"), Fingerprint64("a"))) 74 [1, 0]: HashCombine( 75 Fingerprint64("g"), HashCombine( 76 Fingerprint64("e"), Fingerprint64("b"))) 77 [1, 1]: HashCombine( 78 Fingerprint64("g"), HashCombine( 79 Fingerprint64("e"), Fingerprint64("c"))) 80 81 indices: 2-D. Indices of each input `SparseTensor`. 82 values: 1-D. values of each `SparseTensor`. 83 shapes: 1-D. Shapes of each `SparseTensor`. 84 dense: 2-D. Columns represented by dense `Tensor`. 85 output_indices: 2-D. Indices of the concatenated `SparseTensor`. 86 output_values: 1-D. Non-empty values of the concatenated or hashed 87 `SparseTensor`. 88 output_shape: 1-D. Shape of the concatenated `SparseTensor`. 89 )doc"); 90 91 REGISTER_OP("SparseFeatureCrossV2") 92 .Input("indices: N * int64") 93 .Input("values: sparse_types") 94 .Input("shapes: N * int64") 95 .Input("dense: dense_types") 96 .Output("output_indices: int64") 97 .Output("output_values: out_type") 98 .Output("output_shape: int64") 99 .Attr("N: int >= 0") 100 .Attr("hashed_output: bool") 101 .Attr("num_buckets: int >= 0") 102 .Attr("hash_key: int") 103 .Attr("sparse_types: list({int64, string}) >= 0") 104 .Attr("dense_types: list({int64, string}) >= 0") 105 .Attr("out_type: {int64, string}") 106 .Attr("internal_type: {int64, string}") 107 .SetShapeFn([](shape_inference::InferenceContext* c) { 108 c->set_output(0, c->Matrix(c->UnknownDim(), 2)); 109 c->set_output(1, c->Vector(c->UnknownDim())); 110 c->set_output(2, c->Vector(2)); 111 return Status::OK(); 112 }) 113 .Doc(R"doc( 114 Generates sparse cross form a list of sparse tensors. 115 116 The op takes two lists, one of 2D `SparseTensor` and one of 2D `Tensor`, each 117 representing features of one feature column. It outputs a 2D `SparseTensor` with 118 the batchwise crosses of these features. 119 120 For example, if the inputs are 121 122 inputs[0]: SparseTensor with shape = [2, 2] 123 [0, 0]: "a" 124 [1, 0]: "b" 125 [1, 1]: "c" 126 127 inputs[1]: SparseTensor with shape = [2, 1] 128 [0, 0]: "d" 129 [1, 0]: "e" 130 131 inputs[2]: Tensor [["f"], ["g"]] 132 133 then the output will be 134 135 shape = [2, 2] 136 [0, 0]: "a_X_d_X_f" 137 [1, 0]: "b_X_e_X_g" 138 [1, 1]: "c_X_e_X_g" 139 140 if hashed_output=true then the output will be 141 142 shape = [2, 2] 143 [0, 0]: FingerprintCat64( 144 Fingerprint64("f"), FingerprintCat64( 145 Fingerprint64("d"), Fingerprint64("a"))) 146 [1, 0]: FingerprintCat64( 147 Fingerprint64("g"), FingerprintCat64( 148 Fingerprint64("e"), Fingerprint64("b"))) 149 [1, 1]: FingerprintCat64( 150 Fingerprint64("g"), FingerprintCat64( 151 Fingerprint64("e"), Fingerprint64("c"))) 152 153 indices: 2-D. Indices of each input `SparseTensor`. 154 values: 1-D. values of each `SparseTensor`. 155 shapes: 1-D. Shapes of each `SparseTensor`. 156 dense: 2-D. Columns represented by dense `Tensor`. 157 output_indices: 2-D. Indices of the concatenated `SparseTensor`. 158 output_values: 1-D. Non-empty values of the concatenated or hashed 159 `SparseTensor`. 160 output_shape: 1-D. Shape of the concatenated `SparseTensor`. 161 )doc"); 162 } // namespace tensorflow 163
