| /external/tensorflow/tensorflow/python/framework/ |
| H A D | sparse_tensor.py | 40 `indices`, `values`, and `dense_shape`. In Python, the three tensors are
42 `indices`, `values`, and `dense_shape` tensors, wrap them in a `SparseTensor`
45 Concretely, the sparse tensor `SparseTensor(indices, values, dense_shape)`
49 * `indices`: A 2-D int64 tensor of dense_shape `[N, ndims]`, which specifies
55 * `values`: A 1-D tensor of any type and dense_shape `[N]`, which supplies the
61 * `dense_shape`: A 1-D int64 tensor of dense_shape `[ndims]`, which specifies
62 the dense_shape of the sparse tensor. Takes a list indicating the number of
63 elements in each dimension. For example, `dense_shape=[3,6]` specifies a
64 two-dimensional 3x6 tensor, `dense_shape
181 def dense_shape(self): member in class:SparseTensor
[all...] |
| H A D | ops.py | 1358 def __init__(self, values, indices, dense_shape=None):
1360 _get_graph_from_inputs([values, indices, dense_shape])
1363 self._dense_shape = dense_shape
1376 def dense_shape(self): member in class:IndexedSlices
1407 self._indices, self._values, (", dense_shape=%s" % self._dense_shape)
1411 return IndexedSlices(-self.values, self.indices, self.dense_shape)
1415 "IndexedSlicesValue", ["values", "indices", "dense_shape"])
|
| /external/tensorflow/tensorflow/core/kernels/data/ |
| H A D | sparse_tensor_slice_dataset_op.cc | 65 std::vector<int64> dense_shape; variable
66 dense_shape.reserve(sparse_tensor_.shape().size());
68 dense_shape.emplace_back(sparse_tensor_.shape()[i]);
69 TF_RETURN_IF_ERROR(b->AddVector(dense_shape, &dense_shape_node));
222 const Tensor* dense_shape; variable
223 OP_REQUIRES_OK(ctx, ctx->input("dense_shape", &dense_shape));
233 OP_REQUIRES(ctx, TensorShapeUtils::IsVector(dense_shape->shape()),
236 dense_shape->shape().DebugString()));
254 gtl::InlinedVector<int64, 8> std_order(dense_shape
[all...] |
| /external/tensorflow/tensorflow/contrib/lite/kernels/ |
| H A D | embedding_lookup_sparse.cc | 18 // indices, and dense_shape. The representation assume that the corresponding
20 // * dense.shape = dense_shape
48 // Where dense_shape == [l0, ..., ln] and Tensor[4].dim == [e0, e1, ..., em]
144 TfLiteTensor* dense_shape = GetInput(context, node, 2); local
157 // (loopkup, indices, dense_shape) is consistent.
158 TF_LITE_ENSURE_EQ(context, SizeOfDimension(dense_shape, 0), lookup_rank);
166 const int dim = dense_shape->data.i32[i];
201 stride *= dense_shape->data.i32[k];
|
| /external/tensorflow/tensorflow/core/kernels/ |
| H A D | sparse_fill_empty_rows_op.cc | 49 OP_REQUIRES_OK(context, context->input("dense_shape", &dense_shape_t));
55 errors::InvalidArgument("dense_shape must be a vector, saw: ",
68 // dense_shape. Also add check that dense rank > 0.
73 const auto dense_shape = dense_shape_t->vec<int64>(); variable
76 const int64 dense_rows = dense_shape(0);
94 errors::InvalidArgument("Received SparseTensor with dense_shape[0] = "
|
| /external/tensorflow/tensorflow/core/util/ |
| H A D | example_proto_helper.h | 181 TensorShape dense_shape; local
185 dense_shape.AddDim(dense_shapes[i].dim_size(d));
189 dense_shapes[i].AsTensorShape(&dense_shape);
191 elements_per_stride.push_back(dense_shape.num_elements());
247 TensorShape dense_shape; local
251 dense_shape.AddDim(dense_shapes[i].dim_size(d));
255 dense_shapes[i].AsTensorShape(&dense_shape);
257 elements_per_stride.push_back(dense_shape.num_elements());
|
| /external/tensorflow/tensorflow/python/kernel_tests/ |
| H A D | sparse_ops_test.py | 48 indices=x_indices, values=x_values, dense_shape=x_shape), len(x_values)
138 self.assertAllEqual(output.dense_shape, [3, vocab_size])
144 self.assertAllEqual(output.dense_shape, [3, vocab_size])
239 self.assertAllEqual(output.dense_shape, [3] + vocab_size)
292 self.assertAllEqual(output.dense_shape, [5, 6])
304 self.assertAllEqual(output.dense_shape, [5, 6])
356 self.assertAllEqual(output.dense_shape, [3, 6, 7])
369 self.assertAllEqual(output.dense_shape, [3, 6, 7])
383 self.assertAllEqual(output.dense_shape, [3, 6, 7])
395 self.assertAllEqual(output.dense_shape, [
589 dense_shape = np.array([2, 3]).astype(np.int64) variable in class:SparseReduceTest
[all...] |