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Results 151 - 160 of 229 for transposes (0.26 sec)
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tensorflow/cc/framework/gradient_checker.cc
// over the elements of tensors y and x, and doesn't depend on their shapes. // // If x = (x_1, x_2, ..., x_m) and y = (y_1, y_2, .., y_n) the matrix evaluated // is actually the Jacobian transpose, defined as this mxn matrix: // dy_1/d_x1 dy_2/dx_1 ... dy_n/dx_1 // dy_1/dx_2 dy_2/dx_2 ... dy_n/dx_2 // . // . // . // dy_1/dx_m dy_2/dx_m ... dy_n/dx_m //
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Sat Apr 13 05:57:22 UTC 2024 - 18.2K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/stablehlo/ops/stablehlo_op_quant_spec.cc
mlir::stablehlo::PadOp, mlir::stablehlo::ReduceWindowOp, mlir::stablehlo::ReshapeOp, mlir::stablehlo::SelectOp, mlir::stablehlo::SliceOp, mlir::stablehlo::TransposeOp>(op)) { scale_spec->has_same_scale_requirement = true; } if (llvm::isa<mlir::stablehlo::DynamicSliceOp, mlir::stablehlo::GatherOp, mlir::stablehlo::PadOp, mlir::stablehlo::SliceOp>(op)) {
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 09 05:56:10 UTC 2024 - 7.8K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/utils/lstm_utils_test.cc
mlir::TFL::ConvertLSTMCellSimpleToFusedLSTM convert(fused_lstm_func_); auto result = convert.RewriteFunc(); EXPECT_FALSE(failed(result)); fused_lstm_func_.dump(); // verify transpose EXPECT_EQ( fused_lstm_func_->getAttrOfType<StringAttr>(kTFImplements).getValue(), convert.GetCompositeOpName()); EXPECT_EQ(fused_lstm_func_.getNumArguments(), 5);
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Apr 25 16:01:03 UTC 2024 - 10K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/stablehlo/transforms/tfl_stablehlo_pass.cc
return true; if (op_name == "stablehlo.slice" && (field_name == "start_indices" || field_name == "limit_indices" || field_name == "strides")) return true; if (op_name == "stablehlo.transpose" && field_name == "permutation") return true; return false; } class TflToStablehloPass : public mlir::PassWrapper<TflToStablehloPass,
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed Jan 24 06:08:43 UTC 2024 - 10.8K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/tf_tfl_passes.cc
// Canonicalization includes const folding, which is utilized here to optimize // away ops that can't get constant folded after PrepareTF pass. For example, // tf.Conv2D is split into tf.Transpose and tfl.Conv2D. pass_manager->addNestedPass<mlir::func::FuncOp>( mlir::createCanonicalizerPass()); pass_manager->addNestedPass<mlir::func::FuncOp>(mlir::createCSEPass());
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Jun 06 18:45:51 UTC 2024 - 25.5K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/tests/ops.mlir
%0 = "tfl.range"(%arg0, %arg1, %arg2) : (tensor<i32>, tensor<i32>, tensor<i32>) -> tensor<?xf32> func.return %0 : tensor<?xf32> } // ----- func.func @transpose(%arg0 : tensor<2x2xi32>, %arg1 : tensor<2xi32>) -> tensor<2x2xi32> { %0 = "tfl.transpose"(%arg0, %arg1) : (tensor<2x2xi32>, tensor<2xi32>) -> tensor<2x2xi32> func.return %0 : tensor<2x2xi32> } // -----
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Jun 06 19:09:08 UTC 2024 - 189.2K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/tests/post-quantize.mlir
%2 = "tfl.quantize"(%arg0) {qtype = tensor<1x10x20x3x!quant.uniform<i8:f32, 3.9215686274509805E-9:-1>>} : (tensor<1x10x20x3xf32>) -> tensor<1x10x20x3x!quant.uniform<i8:f32, 3.9215686274509805E-9:-1>> %3 = "tfl.transpose"(%1, %cst_0) : (tensor<3x3x16x3x!quant.uniform<i8<-127:127>:f32, 0.047244094488188976>>, tensor<4xi32>) -> tensor<16x3x3x3x!quant.uniform<i8<-127:127>:f32, 0.047244094488188976>>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 02 09:41:17 UTC 2024 - 19.9K bytes - Viewed (0) -
tensorflow/c/c_api_test.cc
TF_Operation* r, const char* name, bool transpose_a = false, bool transpose_b = false) { TF_OperationDescription* desc = TF_NewOperation(graph, "MatMul", name); if (transpose_a) { TF_SetAttrBool(desc, "transpose_a", 1); } if (transpose_b) { TF_SetAttrBool(desc, "transpose_b", 1); } TF_AddInput(desc, {l, 0}); TF_AddInput(desc, {r, 0});
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Mon Apr 15 03:35:10 UTC 2024 - 96.9K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/common/attrs_and_constraints.h
inline constexpr std::array<int64_t, 4> kNchwToNhwcPermutation = {0, 2, 3, 1}; // Permutation from the OIHW (== (output features, input features, height, // width)) tensor format to HWIO. This is commonly used to transpose convolution // weights represented as OIHW format to HWIO, which is more desirable for // certain downstream optimization passes (e.g. XLA). inline constexpr std::array<int64_t, 4> kOihwToHwioPermutation = {2, 3, 1, 0};
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Apr 25 16:01:03 UTC 2024 - 9.9K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/ir/tfl_ops.td
Or<[ TFL_OperandIsNoneType<3>, TFL_NumElementsEqualsDim<3, 1, 4>]>>]> { let summary = "Transposed Convolution 3D operator"; let description = [{ Performs transposed convolution operation on 3D inputs. Inputs: `inputs[0]`: required: the shape of output tensor `inputs[1]`: required: the filter weight tensor
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Jun 06 19:09:08 UTC 2024 - 186K bytes - Viewed (0)