- Sort Score
- Result 10 results
- Languages All
Results 11 - 20 of 47 for RELU (0.13 sec)
-
tensorflow/compiler/mlir/quantization/tensorflow/python/integration_test/quantize_model_test.py
('none', None, False, False, quant_opts_pb2.TF, False, 'SAME'), ('relu', nn_ops.relu, False, False, quant_opts_pb2.TF, False, 'SAME'), ('relu6', nn_ops.relu6, False, False, quant_opts_pb2.TF, False, 'SAME'), ('with_bias', None, True, False, quant_opts_pb2.TF, False, 'SAME'), ( 'with_bias_and_relu', nn_ops.relu, True, False, quant_opts_pb2.TF,
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/tests/optimize_no_verify.mlir
%cst = arith.constant dense<0.0> : tensor<2x3xbf16> %0 = "tfl.maximum"(%arg0, %cst) : (tensor<2x3xbf16>, tensor<2x3xbf16>) -> tensor<2x3xbf16> func.return %0 : tensor<2x3xbf16> // CHECK: %[[RESULT:.*]] = "tfl.relu"(%arg0) // CHECK: return %[[RESULT]] } // CHECK-LABEL: fuseScalarAddIntoConv2dBf16 func.func @fuseScalarAddIntoConv2dBf16(%arg0: tensor<256x32x32x3xbf16>, %arg1: tensor<16x3x3x3xbf16>) -> tensor<256x8x7x16xbf16> {
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 02 09:41:17 UTC 2024 - 5.8K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/tests/optimize.mlir
// Fusing: %[[add1:[0-9].*]] = tfl.add %arg0, %[[add]] {fused_activation_function = "RELU"} : tensor<1xf32> // Fusing: %[[relu:[0-9].*]] = "tfl.relu"(%arg0) : (tensor<1xf32>) -> tensor<1xf32> // Fusing: %[[add2:[0-9].*]] = tfl.add %[[relu]], %[[add1]] {fused_activation_function = "RELU6"} : tensor<1xf32> // Fusing: %[[add3:[0-9].*]] = tfl.add %[[add2]], %[[relu]] {fused_activation_function = "RELU6"} : tensor<1xf32> // Fusing: return
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 16 20:31:41 UTC 2024 - 284.1K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/transforms/optimize_patterns.td
(HasRankAtMost<4> $a), (HasRankAtMost<4> $b)]>; } // We can eliminate Relu from Relu(SquaredDifference(x, y)), // since the result of SquaredDifference is always non-negative. // TFLite interpreter doesn't support Relu+int32 for now. So the test cases // are failing without the following pattern to optimize Relu away fixes // the problem. def OptimizeReluSquaredDifference : Pat<
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 16 20:31:41 UTC 2024 - 66.4K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/tests/tfl_while_outline.mlir
%14 = "tfl.relu"(%10#1) : (tensor<4x2xf32>) -> tensor<4x2xf32> %15 = "tfl.logistic"(%10#0) : (tensor<4x2xf32>) -> tensor<4x2xf32> %16 = tfl.mul %15, %14 {fused_activation_function = "NONE"} : tensor<4x2xf32> %17 = tfl.add %13, %16 {fused_activation_function = "NONE"} : tensor<4x2xf32> %18 = "tfl.relu"(%17) : (tensor<4x2xf32>) -> tensor<4x2xf32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 02 09:41:17 UTC 2024 - 13.5K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/stablehlo/passes/merge_fusion_with_dequantize.cc
func_op.eraseResult(0); func_op.insertResult(0, new_call_op.getResult(0).getType(), /*resultAttrs=*/nullptr); // Modify the quantized fused function to do dequantize+relu(6). rewriter.setInsertionPoint(req_op); Value new_result = rewriter.create<mlir::stablehlo::UniformDequantizeOp>( req_op.getLoc(), func_op.getResultTypes()[0], req_op.getOperand());
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Apr 25 16:01:03 UTC 2024 - 5.9K bytes - Viewed (0) -
tensorflow/compiler/mlir/tfrt/tests/tf_to_corert/tf_to_corert_pipeline.mlir
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed May 08 00:18:59 UTC 2024 - 7.7K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/tests/prepare-quantize-dynamic-range.mlir
%broad2_s = "quantfork.stats"(%broad2) {layerStats = dense<[0.000000e+00, 1.000000e+01]> : tensor<2xf32>} : (tensor<?x26x26x26x16xf32>) -> tensor<?x26x26x26x16xf32> %add = "tfl.add"(%broad1_s, %broad2_s) {fused_activation_function = "RELU"} : (tensor<?x26x26x26x16xf32>, tensor<?x26x26x26x16xf32>) -> tensor<?x26x26x26x16xf32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 02 09:41:17 UTC 2024 - 38.2K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/transforms/legalize_patterns.td
(TFL_RangeOp $start, $limit, $delta)>; def LegalizeRelu6 : Pat<(TF_Relu6Op $arg), (TFL_Relu6Op $arg)>; def LegalizeRelu : Pat<(TF_ReluOp $arg), (TFL_ReluOp $arg)>; // TFL Relu doesn't support I32/I64 type, so legalizes TF Relu to TFL Maximum. def LegalizeReluI32 : Pat<(TF_ReluOp TensorOf<[I32]>:$arg), (TFL_MaximumOp $arg, (Arith_ConstantOp ConstantAttr<RankedI32ElementsAttr<[]>,"0">))>;
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue Jun 04 13:30:42 UTC 2024 - 28.5K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/stablehlo/tests/pipelines/process_nchw_tensor.mlir
%0 = stablehlo.constant dense<2.000000e+00> : tensor<4x2x3x3xf32> // weight %1 = stablehlo.constant dense<3.000000e+00> : tensor<4xf32> // bias %2 = stablehlo.constant dense<0.000000e+00> : tensor<1x4x5x5xf32> // relu %3 = stablehlo.broadcast_in_dim %1, dims = [1] : (tensor<4xf32>) -> tensor<1x4x5x5xf32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Apr 18 20:32:46 UTC 2024 - 12.6K bytes - Viewed (0)