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tensorflow::
ops::
QuantizedBatchNormWithGlobalNormalization
#include <nn_ops.h>
Quantized Batch normalization.
Summary
This op is deprecated and will be removed in the future. Prefer
tf.nn.batch_normalization
.
Args:
-
scope: A
Scope
object
-
t: A 4D input
Tensor
.
-
t_min: The value represented by the lowest quantized input.
-
t_max: The value represented by the highest quantized input.
-
m: A 1D mean
Tensor
with size matching the last dimension of t. This is the first output from tf.nn.moments, or a saved moving average thereof.
-
m_min: The value represented by the lowest quantized mean.
-
m_max: The value represented by the highest quantized mean.
-
v: A 1D variance
Tensor
with size matching the last dimension of t. This is the second output from tf.nn.moments, or a saved moving average thereof.
-
v_min: The value represented by the lowest quantized variance.
-
v_max: The value represented by the highest quantized variance.
-
beta: A 1D beta
Tensor
with size matching the last dimension of t. An offset to be added to the normalized tensor.
-
beta_min: The value represented by the lowest quantized offset.
-
beta_max: The value represented by the highest quantized offset.
-
gamma: A 1D gamma
Tensor
with size matching the last dimension of t. If "scale_after_normalization" is true, this tensor will be multiplied with the normalized tensor.
-
gamma_min: The value represented by the lowest quantized gamma.
-
gamma_max: The value represented by the highest quantized gamma.
-
variance_epsilon: A small float number to avoid dividing by 0.
-
scale_after_normalization: A bool indicating whether the resulted tensor needs to be multiplied with gamma.
Returns:
Constructors and Destructors
|
QuantizedBatchNormWithGlobalNormalization
(const ::
tensorflow::Scope
& scope, ::
tensorflow::Input
t, ::
tensorflow::Input
t_min, ::
tensorflow::Input
t_max, ::
tensorflow::Input
m, ::
tensorflow::Input
m_min, ::
tensorflow::Input
m_max, ::
tensorflow::Input
v, ::
tensorflow::Input
v_min, ::
tensorflow::Input
v_max, ::
tensorflow::Input
beta, ::
tensorflow::Input
beta_min, ::
tensorflow::Input
beta_max, ::
tensorflow::Input
gamma, ::
tensorflow::Input
gamma_min, ::
tensorflow::Input
gamma_max, DataType out_type, float variance_epsilon, bool scale_after_normalization)
|
Public attributes
Public functions
QuantizedBatchNormWithGlobalNormalization
QuantizedBatchNormWithGlobalNormalization(
const ::tensorflow::Scope & scope,
::tensorflow::Input t,
::tensorflow::Input t_min,
::tensorflow::Input t_max,
::tensorflow::Input m,
::tensorflow::Input m_min,
::tensorflow::Input m_max,
::tensorflow::Input v,
::tensorflow::Input v_min,
::tensorflow::Input v_max,
::tensorflow::Input beta,
::tensorflow::Input beta_min,
::tensorflow::Input beta_max,
::tensorflow::Input gamma,
::tensorflow::Input gamma_min,
::tensorflow::Input gamma_max,
DataType out_type,
float variance_epsilon,
bool scale_after_normalization
)
Except as otherwise noted, the content of this page is licensed under the Creative Commons Attribution 4.0 License, and code samples are licensed under the Apache 2.0 License. For details, see the Google Developers Site Policies. Java is a registered trademark of Oracle and/or its affiliates. Some content is licensed under the numpy license.
Last updated 2021-05-14 UTC.
[null,null,["Last updated 2021-05-14 UTC."],[],[],null,["# tensorflow::ops::QuantizedBatchNormWithGlobalNormalization Class Reference\n\ntensorflow::\nops::\nQuantizedBatchNormWithGlobalNormalization\n============================================================\n\n`\n#include \u003cnn_ops.h\u003e\n`\n\n\nQuantized Batch normalization.\n\nSummary\n-------\n\n\nThis op is deprecated and will be removed in the future. Prefer\n`\ntf.nn.batch_normalization\n`\n.\n\n\nArgs:\n\n- scope: A [Scope](/versions/r2.5/api_docs/cc/class/tensorflow/scope#classtensorflow_1_1_scope) object\n- t: A 4D input [Tensor](/versions/r2.5/api_docs/cc/class/tensorflow/tensor#classtensorflow_1_1_tensor) .\n- t_min: The value represented by the lowest quantized input.\n- t_max: The value represented by the highest quantized input.\n- m: A 1D mean [Tensor](/versions/r2.5/api_docs/cc/class/tensorflow/tensor#classtensorflow_1_1_tensor) with size matching the last dimension of t. This is the first output from tf.nn.moments, or a saved moving average thereof.\n- m_min: The value represented by the lowest quantized mean.\n- m_max: The value represented by the highest quantized mean.\n- v: A 1D variance [Tensor](/versions/r2.5/api_docs/cc/class/tensorflow/tensor#classtensorflow_1_1_tensor) with size matching the last dimension of t. This is the second output from tf.nn.moments, or a saved moving average thereof.\n- v_min: The value represented by the lowest quantized variance.\n- v_max: The value represented by the highest quantized variance.\n- beta: A 1D beta [Tensor](/versions/r2.5/api_docs/cc/class/tensorflow/tensor#classtensorflow_1_1_tensor) with size matching the last dimension of t. An offset to be added to the normalized tensor.\n- beta_min: The value represented by the lowest quantized offset.\n- beta_max: The value represented by the highest quantized offset.\n- gamma: A 1D gamma [Tensor](/versions/r2.5/api_docs/cc/class/tensorflow/tensor#classtensorflow_1_1_tensor) with size matching the last dimension of t. If \"scale_after_normalization\" is true, this tensor will be multiplied with the normalized tensor.\n- gamma_min: The value represented by the lowest quantized gamma.\n- gamma_max: The value represented by the highest quantized gamma.\n- variance_epsilon: A small float number to avoid dividing by 0.\n- scale_after_normalization: A bool indicating whether the resulted tensor needs to be multiplied with gamma.\n\n\u003cbr /\u003e\n\n\nReturns:\n\n- `\n `[Output](/versions/r2.5/api_docs/cc/class/tensorflow/output#classtensorflow_1_1_output)`\n ` result\n- `\n `[Output](/versions/r2.5/api_docs/cc/class/tensorflow/output#classtensorflow_1_1_output)`\n ` result_min\n- `\n `[Output](/versions/r2.5/api_docs/cc/class/tensorflow/output#classtensorflow_1_1_output)`\n ` result_max\n\n\u003cbr /\u003e\n\n| ### Constructors and Destructors ||\n|---|---|\n| ` `[QuantizedBatchNormWithGlobalNormalization](#classtensorflow_1_1ops_1_1_quantized_batch_norm_with_global_normalization_1a06c79c043a3a55b798944a5ae0a0f148)` (const :: `[tensorflow::Scope](/versions/r2.5/api_docs/cc/class/tensorflow/scope#classtensorflow_1_1_scope)` & scope, :: `[tensorflow::Input](/versions/r2.5/api_docs/cc/class/tensorflow/input#classtensorflow_1_1_input)` t, :: `[tensorflow::Input](/versions/r2.5/api_docs/cc/class/tensorflow/input#classtensorflow_1_1_input)` t_min, :: `[tensorflow::Input](/versions/r2.5/api_docs/cc/class/tensorflow/input#classtensorflow_1_1_input)` t_max, :: `[tensorflow::Input](/versions/r2.5/api_docs/cc/class/tensorflow/input#classtensorflow_1_1_input)` m, :: `[tensorflow::Input](/versions/r2.5/api_docs/cc/class/tensorflow/input#classtensorflow_1_1_input)` m_min, :: `[tensorflow::Input](/versions/r2.5/api_docs/cc/class/tensorflow/input#classtensorflow_1_1_input)` m_max, :: `[tensorflow::Input](/versions/r2.5/api_docs/cc/class/tensorflow/input#classtensorflow_1_1_input)` v, :: `[tensorflow::Input](/versions/r2.5/api_docs/cc/class/tensorflow/input#classtensorflow_1_1_input)` v_min, :: `[tensorflow::Input](/versions/r2.5/api_docs/cc/class/tensorflow/input#classtensorflow_1_1_input)` v_max, :: `[tensorflow::Input](/versions/r2.5/api_docs/cc/class/tensorflow/input#classtensorflow_1_1_input)` beta, :: `[tensorflow::Input](/versions/r2.5/api_docs/cc/class/tensorflow/input#classtensorflow_1_1_input)` beta_min, :: `[tensorflow::Input](/versions/r2.5/api_docs/cc/class/tensorflow/input#classtensorflow_1_1_input)` beta_max, :: `[tensorflow::Input](/versions/r2.5/api_docs/cc/class/tensorflow/input#classtensorflow_1_1_input)` gamma, :: `[tensorflow::Input](/versions/r2.5/api_docs/cc/class/tensorflow/input#classtensorflow_1_1_input)` gamma_min, :: `[tensorflow::Input](/versions/r2.5/api_docs/cc/class/tensorflow/input#classtensorflow_1_1_input)` gamma_max, DataType out_type, float variance_epsilon, bool scale_after_normalization) ` ||\n\n| ### Public attributes ||\n|-----------------------------------------------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------------|\n| ` `[operation](#classtensorflow_1_1ops_1_1_quantized_batch_norm_with_global_normalization_1a84804acca133131cda9e9235b954f9af)` ` | ` `[Operation](/versions/r2.5/api_docs/cc/class/tensorflow/operation#classtensorflow_1_1_operation)` ` |\n| ` `[result](#classtensorflow_1_1ops_1_1_quantized_batch_norm_with_global_normalization_1ab4d42bdea55b03a105681930993cf3d4)` ` | ` :: `[tensorflow::Output](/versions/r2.5/api_docs/cc/class/tensorflow/output#classtensorflow_1_1_output)` ` |\n| ` `[result_max](#classtensorflow_1_1ops_1_1_quantized_batch_norm_with_global_normalization_1aacfdd86eadc8f7972ff620b36692ef19)` ` | ` :: `[tensorflow::Output](/versions/r2.5/api_docs/cc/class/tensorflow/output#classtensorflow_1_1_output)` ` |\n| ` `[result_min](#classtensorflow_1_1ops_1_1_quantized_batch_norm_with_global_normalization_1a608925a87be94416e98c14506e98fb64)` ` | ` :: `[tensorflow::Output](/versions/r2.5/api_docs/cc/class/tensorflow/output#classtensorflow_1_1_output)` ` |\n\nPublic attributes\n-----------------\n\n### operation\n\n```text\nOperation operation\n``` \n\n### result\n\n```text\n::tensorflow::Output result\n``` \n\n### result_max\n\n```scdoc\n::tensorflow::Output result_max\n``` \n\n### result_min\n\n```scdoc\n::tensorflow::Output result_min\n``` \n\nPublic functions\n----------------\n\n### QuantizedBatchNormWithGlobalNormalization\n\n```gdscript\n QuantizedBatchNormWithGlobalNormalization(\n const ::tensorflow::Scope & scope,\n ::tensorflow::Input t,\n ::tensorflow::Input t_min,\n ::tensorflow::Input t_max,\n ::tensorflow::Input m,\n ::tensorflow::Input m_min,\n ::tensorflow::Input m_max,\n ::tensorflow::Input v,\n ::tensorflow::Input v_min,\n ::tensorflow::Input v_max,\n ::tensorflow::Input beta,\n ::tensorflow::Input beta_min,\n ::tensorflow::Input beta_max,\n ::tensorflow::Input gamma,\n ::tensorflow::Input gamma_min,\n ::tensorflow::Input gamma_max,\n DataType out_type,\n float variance_epsilon,\n bool scale_after_normalization\n)\n```"]]
