| /external/tensorflow/tensorflow/core/kernels/ |
| parameterized_truncated_normal_op_test.cc | 29 Tensor shape_t(DT_INT32, TensorShape({2}));
30 shape_t.flat<int32>().setValues({num_batches, samples_per_batch});
46 .Input(test::graph::Constant(g, shape_t))
58 Tensor shape_t(DT_INT32, TensorShape({2}));
59 shape_t.flat<int32>().setValues({num_batches, samples_per_batch});
73 .Input(test::graph::Constant(g, shape_t))
85 Tensor shape_t(DT_INT32, TensorShape({2}));
86 shape_t.flat<int32>().setValues({num_batches, samples_per_batch});
100 .Input(test::graph::Constant(g, shape_t))
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| image_resizer_state.h | 62 const Tensor& shape_t = context->input(1); local
63 OP_REQUIRES(context, shape_t.dims() == 1,
64 errors::InvalidArgument("shape_t must be 1-dimensional",
65 shape_t.shape().DebugString()));
66 OP_REQUIRES(context, shape_t.NumElements() == 2,
67 errors::InvalidArgument("shape_t must have two elements",
68 shape_t.shape().DebugString()));
69 auto Svec = shape_t.vec<int32>();
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| random_crop_op.cc | 39 const Tensor& shape_t = context->input(1); variable
40 OP_REQUIRES(context, shape_t.dims() == 1,
41 errors::InvalidArgument("shape_t must be 1-dimensional",
42 shape_t.shape().DebugString()));
43 OP_REQUIRES(context, shape_t.NumElements() == 2,
44 errors::InvalidArgument("shape_t must have two elements",
45 shape_t.shape().DebugString()));
47 auto shape_vec = shape_t.vec<int64>();
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| sparse_reduce_op.cc | 106 Status ValidateInputs(const Tensor *shape_t, const Tensor *reduction_axes_t) {
108 if (!TensorShapeUtils::IsVector(shape_t->shape())) {
111 shape_t->shape().DebugString());
123 if (axis < -shape_t->NumElements() || axis >= shape_t->NumElements()) {
126 shape_t->NumElements(), " dimensions.");
161 const Tensor *indices_t, *values_t, *shape_t, *reduction_axes_t; variable
164 OP_REQUIRES_OK(ctx, ctx->input("input_shape", &shape_t));
167 OP_REQUIRES_OK(ctx, ValidateInputs(shape_t, reduction_axes_t));
174 const auto shape_vec = shape_t->vec<int64>()
255 const Tensor *indices_t, *values_t, *shape_t, *reduction_axes_t; variable
[all...] |
| sparse_softmax_op.cc | 43 const Tensor *indices_t, *values_t, *shape_t; variable
46 OP_REQUIRES_OK(context, context->input("sp_shape", &shape_t));
55 TensorShapeUtils::IsVector(shape_t->shape()),
60 shape_t->shape().DebugString()));
61 OP_REQUIRES(context, shape_t->NumElements() >= 2,
64 shape_t->SummarizeValue(3)));
73 TensorShape(shape_t->flat<int64>()));
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| resize_nearest_neighbor_op.cc | 135 const Tensor& shape_t = context->input(1); variable
136 OP_REQUIRES(context, shape_t.dims() == 1,
137 errors::InvalidArgument("shape_t must be 1-dimensional",
138 shape_t.shape().DebugString()));
139 OP_REQUIRES(context, shape_t.NumElements() == 2,
140 errors::InvalidArgument("shape_t must have two elements",
141 shape_t.shape().DebugString()));
143 auto sizes = shape_t.vec<int32>();
145 errors::InvalidArgument("shape_t's elements must be positive"));
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| sparse_dense_binary_op_shared.cc | 62 const Tensor *indices_t, *values_t, *shape_t, *dense_t; variable
65 OP_REQUIRES_OK(ctx, ctx->input("sp_shape", &shape_t));
75 TensorShapeUtils::IsVector(shape_t->shape()),
80 shape_t->shape().DebugString()));
86 const auto shape_vec = shape_t->vec<int64>();
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| random_op.cc | 271 const Tensor& shape_t = ctx->input(0); variable
275 TensorShapeUtils::IsVector(shape_t.shape()) &&
276 (shape_t.dtype() == DataType::DT_INT32 ||
277 shape_t.dtype() == DataType::DT_INT64),
280 shape_t.DebugString()));
282 if (shape_t.dtype() == DataType::DT_INT32) {
283 auto vec = shape_t.flat<int32>();
286 } else if (shape_t.dtype() == DataType::DT_INT64) {
287 auto vec = shape_t.flat<int64>();
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| stateless_random_ops.cc | 39 const Tensor& shape_t = context->input(0); variable
42 OP_REQUIRES_OK(context, MakeShape(shape_t, &shape));
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| example_parsing_ops.cc | 516 auto shape_t = sp_shape_d->vec<int64>(); variable
517 shape_t(0) = num_elements;
532 auto shape_t = sp_shape_d->vec<int64>(); variable
533 shape_t(0) = 0;
642 auto shape_t = sp_shape_d->vec<int64>(); variable
643 shape_t(0) = feature_list_size;
644 shape_t(1) = max_num_features;
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| ctc_decoder_ops.cc | 145 auto shape_t = p_shape->vec<int64>(); local
161 shape_t(0) = batch_size;
162 shape_t(1) = max_decoded;
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| resize_bicubic_op_test.cc | 260 auto shape_t = shape.flat<int32>(); local
261 shape_t(0) = scale_y * size;
262 shape_t(1) = scale_x * size;
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| random_poisson_op.cc | 289 const Tensor& shape_t = ctx->input(0); variable
293 OP_REQUIRES_OK(ctx, MakeShape(shape_t, &samples_shape));
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| resize_bilinear_op_test.cc | 480 .contains("Invalid argument: shape_t must be 1-dimensional"))
489 .contains("Invalid argument: shape_t must have two elements"))
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| /external/tensorflow/tensorflow/contrib/nccl/kernels/ |
| nccl_ops.cc | 205 const Tensor& shape_t = c->input(0); variable
208 c, TensorShapeUtils::MakeShape(shape_t.vec<int32>(), &shape), done);
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| /external/tensorflow/tensorflow/core/util/ |
| example_proto_helper.cc | 388 auto shape_t = (*output_sparse_shapes_tensor)[d].vec<int64>(); local
389 shape_t(0) = batch_size;
390 shape_t(1) = sparse_tensor_batch_shapes.max_num_features;
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| /external/python/cpython3/Lib/test/ |
| test_buffer.py | [all...] |
| /external/tensorflow/tensorflow/contrib/seq2seq/python/ops/ |
| beam_search_decoder.py | 79 shape_t = array_ops.shape(t)
89 ([shape_t[0] * multiplier], shape_t[1:]), 0))
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| /external/tensorflow/tensorflow/core/ops/ |
| nn_ops_test.cc | 528 Tensor shape_t = test::AsTensor<int32>(shape); local
529 op.input_tensors[0] = &shape_t;
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| /external/tensorflow/tensorflow/python/training/ |
| server_lib_test.py | 126 shape_t = array_ops.shape(c)
127 self.assertAllEqual([10000, 3000], sess.run(shape_t))
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