- Sort Score
- Result 10 results
- Languages All
Results 1 - 10 of 330 for ShapeN (0.12 sec)
-
tensorflow/compiler/jit/xla_device_ops.h
.TypeConstraint("T", TYPES), \ ShapeOp<int64_t>); \ REGISTER_KERNEL_BUILDER(Name("ShapeN") \ .Device(DEVICE) \ .HostMemory("output") \
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue Nov 23 19:28:25 UTC 2021 - 17.1K bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/tests/constant-fold.mlir
// CHECK-DAG: %[[SHAPE1:.*]] = "tf.Const"() <{value = dense<[1, 32, 32, 16]> : tensor<4xi64>}> // CHECK: %[[SHAPE2:.*]] = "tf.Shape"(%arg2) : (tensor<*xf32>) -> tensor<?xi64> %0:3 = "tf.ShapeN"(%arg0, %arg1, %arg2) : (tensor<f32>, tensor<1x32x32x16xf32>, tensor<*xf32>) -> (tensor<0xi64>, tensor<4xi64>, tensor<?xi64>) // CHECK: return %[[SHAPE0]], %[[SHAPE1]], %[[SHAPE2]]
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed Jan 31 23:22:24 UTC 2024 - 36.7K bytes - Viewed (0) -
tensorflow/cc/gradients/array_grad.cc
inputs.push_back(op.input(i)); } auto shapes = ShapeN(scope, inputs); const auto unique_name = scope.GetUniqueNameForOp("ConcatOffset"); auto builder = ::tensorflow::NodeBuilder(unique_name, "ConcatOffset") .Input(::tensorflow::ops::AsNodeOut(scope, op.input(dim_index))) .Input(::tensorflow::ops::AsNodeOutList(scope, shapes.output)); scope.UpdateBuilder(&builder);
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue Oct 10 23:33:32 UTC 2023 - 31.7K bytes - Viewed (0) -
tensorflow/compiler/jit/shape_inference.cc
// Merge node causes a loop so we remove NextIteration->Merge edge before // performing shape inference. But removing those edges also prevents us // from inferring output shape for Merge node (we need shapes for all its // inputs). // For loop invariant resource input's Merge node, we set output resource // shape as Enter node's resource shape. // TODO(b/129367850): clean this up.
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Fri May 31 00:41:19 UTC 2024 - 13K bytes - Viewed (0) -
tensorflow/cc/gradients/math_grad_test.cc
xs.push_back(Placeholder(scope_, DT_FLOAT, Placeholder::Shape(shape))); auto y = AddN(scope_, xs); RunTest(xs, {shape, shape, shape}, {y}, {shape}); } TEST_F(NaryGradTest, Add) { TensorShape x1_shape({3, 2, 5}); TensorShape x2_shape({2, 5}); auto x1 = Placeholder(scope_, DT_FLOAT, Placeholder::Shape(x1_shape)); auto x2 = Placeholder(scope_, DT_FLOAT, Placeholder::Shape(x2_shape));
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Fri Aug 25 18:20:20 UTC 2023 - 36K bytes - Viewed (0) -
tensorflow/c/eager/parallel_device/parallel_device_lib.h
ParallelTensor(const ParallelDevice& device, std::vector<TensorHandlePtr> tensors, absl::Span<const int64_t> shape, const TF_DataType dtype) : device_(device), tensors_(std::move(tensors)), shape_(std::vector<int64_t>(shape.begin(), shape.end())), dtype_(dtype) {} ParallelTensor(const ParallelDevice& device,
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue Apr 25 15:21:13 UTC 2023 - 12.9K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/tests/shape-inference.mlir
// RUN: tf-opt -split-input-file -verify-diagnostics --tf-shape-inference %s | FileCheck %s module attributes {tf.versions = {producer = 888 : i32}} { // CHECK-LABEL: testConv2dShapeValidPadding func.func @testConv2dShapeValidPadding(%arg0: tensor<1x112x80x128xf32>, %arg1: tensor<128x3x3x128xf32>, %arg2: tensor<128xf32>) -> tensor<1x?x?x128xf32> {
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 02 09:41:17 UTC 2024 - 11.5K bytes - Viewed (0) -
tensorflow/compiler/mlir/tf2xla/api/v1/compile_mlir_util.cc
// Extracts shape from XlaArgument as TensorShape. If shape is a xla::Shape, // that is converted to a TensorShape. absl::StatusOr<TensorShape> GetTensorShapeFromXlaArgument( const XlaArgument& arg) { if (absl::holds_alternative<xla::Shape>(arg.shape)) { TensorShape arg_shape; TF_RETURN_IF_ERROR( XLAShapeToTensorShape(std::get<xla::Shape>(arg.shape), &arg_shape)); return arg_shape;
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/compiler/mlir/lite/tests/canonicalize.mlir
^bb0(%arg0: tensor<4x4x4xf32>) : %shape0 = arith.constant dense<[16, 4]> : tensor<2xi32> %shape1 = arith.constant dense<[64]> : tensor<1xi32> %0 = "tfl.reshape"(%arg0, %shape0) : (tensor<4x4x4xf32>, tensor<2xi32>) -> tensor<16x4xf32> %1 = "tfl.reshape"(%0, %shape1) : (tensor<16x4xf32>, tensor<1xi32>) -> tensor<64xf32> %2 = "tfl.reshape"(%0, %shape1) : (tensor<16x4xf32>, tensor<1xi32>) -> tensor<64xf32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 02 09:41:17 UTC 2024 - 20.6K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/stablehlo/transforms/optimize.cc
PatternRewriter &rewriter) { // Fail for non-static shapes if (!pad_op.getOperand().getType().hasStaticShape() || !pad_op.getResult().getType().hasStaticShape() || !pad_op.getPaddingValue().getType().hasStaticShape()) { return rewriter.notifyMatchFailure(pad_op, "dynamic shapes not supported"); } // Check if the operand is also a Pad op auto parent_pad =
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Apr 25 16:01:03 UTC 2024 - 26.9K bytes - Viewed (0)