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Results 21 - 30 of 49 for zero_point (0.13 sec)
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tensorflow/compiler/mlir/lite/tests/mlir2flatbuffer/numeric_verify.mlir
// CHECK-NEXT: type: UINT8, // CHECK-NEXT: buffer: 2, // CHECK-NEXT: name: "arg1", // CHECK-NEXT: quantization: { // CHECK-NEXT: scale: [ 0.1 ], // CHECK-NEXT: zero_point: [ 0 ] // CHECK-NEXT: }, // CHECK-NEXT: has_rank: true // CHECK-NEXT: }, { // CHECK-NEXT: shape: [ 4 ], // CHECK-NEXT: buffer: 3, // CHECK-NEXT: name: "NumericVerify/arg1:1",
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Jul 14 16:41:28 UTC 2022 - 2.2K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/quantization/quantization_info.proto
float max = 2; } // Affine parameters to quantize the per axis value. The metadata of the // target properties should be specified as well. message AffineParams { float scale = 1; int32 zero_point = 2; } // Params to quantize the axis. Only one of the field can be used. message PerAxisParams { oneof params_oneof { // min/max of the ranges. MinMax min_max = 1;
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue Oct 08 03:45:04 UTC 2019 - 2.3K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/schema/schema_v3b.fbs
// t[:, 1, :, :] will have scale[1]=2.0, zero_point[0]=2 // t[:, 2, :, :] will have scale[2]=3.0, zero_point[0]=3 quantized_dimension:int; } // Sparse tensors. // We use a modification of the TACO format. // Reference: http://tensor-compiler.org/kjolstad-oopsla17-tensor-compiler.pdf // // To encode a conceptual n-dimensional dense tensor with dims (d0, ..., dn-1),
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue May 28 14:28:27 UTC 2024 - 30K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/utils/const_tensor_utils.cc
flags, storage_type, builder.getF32Type(), scales, quant_params.zero_point, quant_params.quantized_dimension, storage_min, storage_max); } return mlir::quant::UniformQuantizedType::get( flags, storage_type, builder.getF32Type(), scales[0], quant_params.zero_point.at(0), storage_min, storage_max); }
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue May 07 23:04:40 UTC 2024 - 16.6K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/tests/mlir2flatbuffer/depthwise_conv2d_v2.mlir
// CHECK-NEXT: type: UINT8, // CHECK-NEXT: buffer: 3, // CHECK-NEXT: name: "tfl.pseudo_qconst", // CHECK-NEXT: quantization: { // CHECK-NEXT: scale: [ 0.021827 ], // CHECK-NEXT: zero_point: [ 151 ] // CHECK-NEXT: }, // CHECK-NEXT: has_rank: true // CHECK-NEXT: }, { // CHECK-NEXT: shape: [ 32, 3, 3, 3 ], // CHECK-NEXT: buffer: 4,
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Jul 14 16:41:28 UTC 2022 - 9.1K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/schema/schema.fbs
// t[:, 1, :, :] will have scale[1]=2.0, zero_point[0]=2 // t[:, 2, :, :] will have scale[2]=3.0, zero_point[0]=3 quantized_dimension:int; } // Sparse tensors. // We use a modification of the TACO format. // Reference: http://tensor-compiler.org/kjolstad-oopsla17-tensor-compiler.pdf // // To encode a conceptual n-dimensional dense tensor with dims (d0, ..., dn-1),
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Fri May 03 18:01:23 UTC 2024 - 41.7K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/common/ir/FakeQuantSupport.h
// 1 // - the specified min/max values are "nudged" so that the result has a zero // that can be exactly expressed // - min=max=0 implies scale=0 and zero_point=0 // // With the above assumptions applied, every conforming specified FakeQuant op // can be represented by a UniformQuantizedType. This scheme is not expected to
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Mar 21 11:52:27 UTC 2024 - 3.7K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/tests/mlir2flatbuffer/depthwise_conv2d.mlir
// CHECK-NEXT: type: UINT8, // CHECK-NEXT: buffer: 3, // CHECK-NEXT: name: "tfl.pseudo_qconst", // CHECK-NEXT: quantization: { // CHECK-NEXT: scale: [ 0.021827 ], // CHECK-NEXT: zero_point: [ 151 ] // CHECK-NEXT: }, // CHECK-NEXT: has_rank: true // CHECK-NEXT: }, { // CHECK-NEXT: shape: [ 32, 3, 3, 3 ], // CHECK-NEXT: buffer: 4,
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Jul 14 16:41:28 UTC 2022 - 9K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/tests/end2end/back2back_fake_quant.pbtxt
# RESULT1: name: "Identity" # RESULT1-NEXT: quantization: # RESULT1-NEXT: scale: [ 0.007523 ], # RESULT1-NEXT: zero_point: [ 116 ] # TODO Actually RESULT1 represents in incomplete implementation # Currently TF2.2.0-rc3 all but the first fake_quant in
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Mon Nov 15 19:42:47 UTC 2021 - 25.9K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/tests/mlir2flatbuffer/unidirectional_sequence_lstm.mlir
// CHECK-NEXT: type: INT8, // CHECK-NEXT: name: "effective_hidden_scale_intermediate", // CHECK-NEXT: quantization: { // CHECK-NEXT: scale: [ 0.007788 ], // CHECK-NEXT: zero_point: [ 0 ] // CHECK-NEXT: }, // CHECK-NEXT: has_rank: true // CHECK-NEXT: }, { // CHECK-NEXT: shape: [ 4, 4, 4 ], // CHECK-NEXT: buffer: 25,
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed Dec 06 18:55:51 UTC 2023 - 11.7K bytes - Viewed (0)