/external/tensorflow/tensorflow/core/kernels/ |
H A D | sparse_concat_op.cc | 77 const int input_rank = input_shape.dims(); variable 79 ? input_rank + concat_dim_attr_ 81 OP_REQUIRES(context, concat_dim >= 0 && concat_dim < input_rank, 83 -input_rank, ", ", input_rank, 88 context, current_shape.dims() == input_rank, 90 "Ranks of all input tensors must match: expected ", input_rank, 92 for (int j = 0; j < input_rank; ++j) { 112 gtl::InlinedVector<int64, 8> std_order(input_rank); 116 concat_order.reserve(input_rank); [all...] |
H A D | linalg_ops_common.cc | 118 int input_rank = -1; local 122 input_rank = in.dims(); 124 context, input_rank >= 2, 126 " must have rank >= 2, got ", input_rank)); 130 for (int dim = 0; dim < input_rank - 2; ++dim) { 135 OP_REQUIRES(context, input_rank == in.dims(), 138 for (int dim = 0; dim < input_rank - 2; ++dim) { 146 const int row_dimension = input_rank - 2; 147 const int col_dimension = input_rank - 1;
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H A D | reshape_util.cc | 50 const int64 input_rank = input_shape_in.NumElements(); local 109 gtl::InlinedVector<int64, 8> input_strides(input_rank); 110 input_strides[input_rank - 1] = 1; 111 for (int d = input_rank - 2; d >= 0; --d) { 130 for (int j = 0; j < input_rank; ++j) {
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/external/tensorflow/tensorflow/compiler/tf2xla/kernels/ |
H A D | depthtospace_op.cc | 45 int input_rank = input_tensor_shape.dims(); variable 47 OP_REQUIRES(ctx, kRequiredDims == input_rank, 49 "; got: ", input_rank)); 56 int feature_dim = GetTensorFeatureDimIndex(input_rank, data_format_); 57 int num_spatial_dims = GetTensorSpatialDims(input_rank, data_format_); 62 reshaped_shape.reserve(input_rank); 63 transpose_order.reserve(input_rank); 64 output_shape.reserve(input_rank);
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H A D | spacetodepth_op.cc | 45 int input_rank = input_tensor_shape.dims(); variable 47 OP_REQUIRES(ctx, kRequiredDims == input_rank, 49 "; got ", input_rank)); 56 int feature_dim = GetTensorFeatureDimIndex(input_rank, data_format_); 57 int num_spatial_dims = GetTensorSpatialDims(input_rank, data_format_); 62 reshaped_shape.reserve(input_rank); 63 transpose_order.reserve(input_rank); 64 output_shape.reserve(input_rank);
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H A D | batchtospace_op.cc | 28 const int input_rank = input_tensor_shape.dims(); local 34 ctx, input_rank >= 1 + block_rank, 36 " instead of ", input_rank)); 71 std::vector<int64> reshaped_shape(input_rank + block_rank); 106 std::vector<int64> reshaped_permuted_shape(input_rank); 126 std::vector<int64> start_indices(input_rank, 0); 128 std::vector<int64> strides(input_rank, 1);
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H A D | spacetobatch_op.cc | 28 const int input_rank = input_tensor_shape.dims(); local 34 ctx, input_rank >= 1 + block_rank, 36 " instead of ", input_rank)); 89 std::vector<int64> reshaped_padded_shape(input_rank + block_rank); 137 std::vector<int64> output_shape(input_rank);
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/external/tensorflow/tensorflow/contrib/model_pruning/python/layers/ |
H A D | layers.py | 198 input_rank = inputs.get_shape().ndims 200 if input_rank == 3: 202 input_rank) 203 elif input_rank == 4: 205 elif input_rank == 5: 207 input_rank) 210 input_rank)
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/external/tensorflow/tensorflow/contrib/layers/python/layers/ |
H A D | feature_column_ops.py | 45 1. If `output_rank > input_rank + 1`, raise a `ValueError`. 46 2. If `output_rank == input_rank + 1`, expand the tensor by one dimension. 47 3. If `output_rank == input_rank`, do nothing. 48 4. If `output_rank < input_rank`, flatten the inner dimensions of the tensor. 57 ValueError: if `output_rank > input_rank + 1` for the input tensor. 59 input_rank = tensor.get_shape().ndims 61 if input_rank is None and isinstance(tensor, sparse_tensor_py.SparseTensor): 63 input_rank = tensor.dense_shape.get_shape().as_list()[0] 65 if input_rank is None: 69 if output_rank > input_rank [all...] |
H A D | layers.py | 1017 input_rank = inputs.get_shape().ndims 1019 if input_rank == 3: 1021 elif input_rank == 4: 1023 elif input_rank == 5: 1027 input_rank) 2370 input_rank = inputs.get_shape().ndims 2371 if input_rank is None: 2373 if input_rank < 3: 2375 num_spatial_dims = input_rank - 2 2927 input_rank [all...] |
H A D | feature_column.py | 1633 1. If `output_rank > input_rank + 1` raise a `ValueError`. 1634 2. If `output_rank == input_rank + 1`, expand `input_tensor` by one 1636 3. If `output_rank == input_rank`, return `input_tensor`. 1637 4. If `output_rank < input_rank`, flatten the inner dimensions of 1648 ValueError: if `output_rank > input_rank + 1`. 1650 input_rank = input_tensor.get_shape().ndims 1651 if input_rank is not None: 1652 if output_rank > input_rank + 1: 1657 input_rank, output_rank)) 1662 if output_rank == input_rank [all...] |
/external/tensorflow/tensorflow/contrib/lite/kernels/ |
H A D | gather.cc | 87 const int input_rank = NumDimensions(input); local 90 GetTensorData<data_type>(input), GetTensorDims(input), input_rank, \
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/external/tensorflow/tensorflow/python/ops/ |
H A D | spectral_ops.py | 109 input_rank = _array_ops.rank(input_tensor) 111 outer_dims = _math_ops.maximum(0, input_rank - fft_rank)
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H A D | nn_ops.py | 1692 input_rank = array_ops.rank(logits) 1694 logits = _swap_axis(logits, dim_axis, math_ops.subtract(input_rank, 1)) 1706 output, dim_axis, math_ops.subtract(input_rank, 1), name=name) 1847 input_rank = array_ops.rank(precise_logits) 1862 precise_logits = _move_dim_to_end(precise_logits, dim, input_rank) 1863 labels = _move_dim_to_end(labels, dim, input_rank) 1879 [math_ops.subtract(input_rank, 1)])
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H A D | math_ops.py | 2544 input_rank = array_ops.size(input_shape) # 4 2545 axes = (axes + input_rank) % input_rank 2549 range(input_rank), # [0, 1, 2, 3]
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H A D | array_grad.py | 246 input_rank = array_ops.rank(input_vec) 249 shape = array_ops.stack([input_rank, 1])
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/external/tensorflow/tensorflow/core/framework/ |
H A D | shape_inference.cc | 857 int idx, int input_rank, DimensionHandle* out) { 866 if (input_rank < 0) { 869 } else if (val + input_rank < 0) { 871 val, " must be in range [-", input_rank, 872 ", ", input_rank, ")"); 874 val += input_rank; 876 } else if (input_rank >= 0 && val >= input_rank) { 878 val, " must be in range [-", input_rank, 879 ", ", input_rank, ")"); 856 MakeDimForScalarInputWithNegativeIndexing( int idx, int input_rank, DimensionHandle* out) argument [all...] |
H A D | common_shape_fns.cc | 1040 const int32 input_rank, 1045 if (reduction_index < -input_rank || reduction_index >= input_rank) { 1048 input_rank, " dimensions."); 1053 wrapped_index += input_rank; 1090 const int32 input_rank = c->Rank(input); local 1094 input_rank, true_indices)); 1097 input_rank, true_indices)); 1104 for (int i = 0; i < input_rank; ++i) { 1039 ReductionShapeHelper(const Tensor* reduction_indices_t, const int32 input_rank, std::set<int64>& true_indices) argument
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H A D | shape_inference.h | 499 Status MakeDimForScalarInputWithNegativeIndexing(int idx, int input_rank,
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/external/tensorflow/tensorflow/core/ops/ |
H A D | math_ops.cc | 791 const int32 input_rank = c->Rank(input_shape); local 792 if (input_rank <= 1) { 802 std::vector<DimensionHandle> dims(input_rank - 1); 818 int64 axis = dimension_val < 0 ? dimension_val + input_rank : dimension_val; 819 if (axis < 0 || axis >= input_rank) { 821 "Dimension (", dimension_val, ") must be in the range [", -input_rank, 822 ", ", input_rank, "), where ", input_rank, 828 for (int i = 0; i < input_rank; ++i) {
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H A D | array_ops.cc | 74 // Paddings is a matrix of [input_rank, 2]. 1261 const int32 input_rank = c->Rank(input); 1262 if (batch_dim >= input_rank) { 1264 "batch_dim must be < input rank: ", batch_dim, " vs. ", input_rank); 1266 if (seq_dim >= input_rank) { 1268 "seq_dim must be < input rank: ", seq_dim, " vs. ", input_rank); 1663 const Tensor* paddings_t, int64 input_rank) { 1665 std::vector<DimensionHandle> dims(input_rank); 1666 for (int64 i = 0; i < input_rank; ++i) { 1699 int64 input_rank 1662 MirrorPadKnown(InferenceContext* c, ShapeHandle input, const Tensor* paddings_t, int64 input_rank) argument [all...] |
/external/tensorflow/tensorflow/core/common_runtime/ |
H A D | shape_refiner.cc | 488 int input_rank = c->Rank(c->input(0)); local 489 Tensor t(node->output_type(0), TensorShape({input_rank})); 492 for (int i = 0; i < input_rank; i++) { 503 for (int i = 0; i < input_rank; i++) { 516 int32 input_rank = c->Rank(c->input(0)); local 518 t.flat<int32>()(0) = input_rank;
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/external/tensorflow/tensorflow/compiler/xla/ |
H A D | shape_util.cc | 1271 int64 input_rank = Rank(input_shape); 1293 std::vector<int64> dimension_to_alignment_index(input_rank); 1295 for (int64 i = 0, j = 0; i < input_rank || j < output_rank;) { 1304 if (i == input_rank) { 1320 alignment.push_back({input_rank, output_rank}); 1330 for (int64 i = 0; i < input_rank;) { 1359 if (i == input_rank) {
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/external/tensorflow/tensorflow/cc/gradients/ |
H A D | math_grad.cc | 627 // input_rank = 4 628 auto input_rank = Size(scope, input_shape); local 632 auto axes = Mod(scope, Add(scope, reduction_axes, input_rank), input_rank); 634 // This [0..input_rank) range of integers is used in DynamicStitch to 637 auto input_rank_range = Range(scope, zero, input_rank, one);
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/external/tensorflow/tensorflow/contrib/lite/toco/ |
H A D | model.h | 1315 int input_rank = 0; member in struct:toco::GatherOperator
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