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Results 11 - 20 of 33 for x1_shape (0.34 sec)
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tensorflow/cc/gradients/linalg_grad.cc
x = Conj(scope, x); y = Conj(scope, y); } const auto x_shape = Shape(scope, x); const auto y_shape = Shape(scope, y); Output grad_x = EinsumGradWrt(scope, grad, y, x_shape, x_subs, y_subs, output_subs); Output grad_y = EinsumGradWrt(scope, grad, x, y_shape, y_subs, x_subs, output_subs); if (!absl::StrContains(output_subs, kEllipsis)) {
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Mon Mar 07 23:11:54 UTC 2022 - 20.4K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/stablehlo/tests/unfuse_mhlo_batch_norm.mlir
// CHECK-DAG: %[[RHS:.+]] = mhlo.subtract %[[OFFSET]], %[[MUL_MEAN]] : tensor<?xf32> // CHECK-DAG: %[[X_SHAPE:.+]] = shape.shape_of %[[X]] : tensor<?x?x?x?xf32> -> tensor<4xindex> // CHECK-DAG: %[[MULTIPLIER_BCAST:.+]] = "mhlo.dynamic_broadcast_in_dim"(%[[MULTIPLIER]], %[[X_SHAPE]]) <{broadcast_dimensions = dense<1> : tensor<1xi64>}> : (tensor<?xf32>, tensor<4xindex>) -> tensor<?x?x?x?xf32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Sat Apr 06 15:32:52 UTC 2024 - 10.4K bytes - Viewed (0) -
tensorflow/cc/gradients/image_grad.cc
// The internal gradient implementation needs the shape of the input image. // x_shape = shape(x)[1:3] // = slice(shape(x), {1}, {3 - 1}) auto x_shape = Slice(scope, Shape(scope, op.input(0)), {1}, {2}); grad_outputs->push_back(internal::ResizeNearestNeighborGrad( scope, grad_inputs[0], x_shape, internal::ResizeNearestNeighborGrad::AlignCorners(align_corners)
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Nov 11 00:29:23 UTC 2021 - 5.7K bytes - Viewed (0) -
tensorflow/compiler/jit/pjrt_tensor_buffer_util_test.cc
std::vector<int32_t> data{1, 2, 3, 4, 5, 6}; xla::Shape xla_shape = xla::ShapeUtil::MakeShape(xla::S32, dimensions); TF_ASSERT_OK_AND_ASSIGN( auto pjrt_buffer, pjrt_client->BufferFromHostBuffer( data.data(), xla_shape.element_type(), xla_shape.dimensions(), /*byte_strides=*/std::nullopt,
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Sep 14 18:14:47 UTC 2023 - 2.7K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/tensorflow/python/integration_test/quantize_model_test.py
has_bias: bool, use_kernel: bool, ): n = 5 x_shape = [v if v is not None else n for v in shapes[0]] y_shape = [v if v is not None else n for v in shapes[1]] class MatmulModel(module.Module): def __init__(self, bias: Optional[core.Tensor]): self._bias = bias self._kernel = np.random.uniform(size=y_shape).astype('f4') self._min = (-0.8, -0.8, -0.9)
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Fri May 17 03:36:50 UTC 2024 - 235.6K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/utils/utils.h
std::vector<int64_t> in_shape{input_type.getShape().vec()}; std::vector<int64_t> out_shape{output_type.getShape().vec()}; // If the reshape changes the number of dimensions so it cannot be interpreted // as a transpose. if (in_shape.size() != out_shape.size()) { return false; } in_shape.erase(std::remove(in_shape.begin(), in_shape.end(), 1), in_shape.end());
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue Apr 30 00:40:15 UTC 2024 - 11.6K bytes - Viewed (0) -
tensorflow/compiler/mlir/tf2xla/internal/utils/test_metadata_config.cc
mlir::FunctionType func_type = main_fn.getFunctionType(); for (auto input_type : func_type.getInputs()) { tensorflow::TensorShape tensor_shape; xla::Shape xla_shape = xla::TypeToShape(input_type); TF_RETURN_IF_ERROR(tensorflow::TensorShape::BuildTensorShape( xla_shape.dimensions(), &tensor_shape)); arg_shapes.emplace_back(tensor_shape); DataType dtype; TF_RETURN_IF_ERROR(ConvertToDataType(input_type, &dtype));
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Jun 13 23:59:33 UTC 2024 - 3.9K bytes - Viewed (0) -
tensorflow/c/eager/c_api_unified_experimental.h
// Represents a (partially-defined) shape. typedef struct TF_Shape { int num_dims; // Must be >= -1; -1 represents unknown rank. int64_t* dim_sizes; } TF_Shape; // Add a new parameter to a TensorFlow Function. TF_AbstractTensor* TF_AddFunctionParameter(TF_ExecutionContext* func, TF_DataType dtype, TF_Shape shape, TF_Status* s);
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Sun Oct 24 11:27:00 UTC 2021 - 7K bytes - Viewed (0) -
tensorflow/compiler/mlir/tf2xla/api/v1/compile_mlir_util.cc
[shape_determination_fns]( const xla::Shape& xla_shape) -> absl::StatusOr<xla::Shape> { TensorShape shape; TF_RETURN_IF_ERROR(XLAShapeToTensorShape(xla_shape, &shape)); TF_ASSIGN_OR_RETURN(DataType dtype, EncodePrimitiveTypeAsDataType( xla_shape.element_type())); auto layout_preference = shape_determination_fns.layout_preference_fn(
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue May 21 17:24:39 UTC 2024 - 45.3K bytes - Viewed (0) -
tensorflow/cc/gradients/math_grad.cc
auto x_shape = Shape(scope, x); auto output_shape = Shape(scope, op.output(0)); // Reduce away broadcasted leading dims. auto reduce_x = internal::BroadcastGradientArgs(scope, x_shape, output_shape); auto gx_sum = ReduceSum(scope, gx, /*axis=*/reduce_x.r0, ReduceSum::KeepDims(true)); auto gx_sum_reshape = Reshape(scope, gx_sum, x_shape);
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Fri Aug 25 18:20:20 UTC 2023 - 50.7K bytes - Viewed (0)