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How to prevent weight update in caffe

score 538 · Answer 1 · 2017-08-29 08:35:32

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How to prevent weight update in caffe

538 views Asked by RichieZhu At 29 August 2017 at 08:35

score 660 · Answer 2 · 2017-09-10 00:07:37

Use tf.layers.batch_normalization to preprocess inputs for SELU activation function?

660 views Asked by Maosi Chen At 10 September 2017 at 00:07

score 117 · Answer 3 · 2017-09-10 21:50:31

In tf.slim, whether I need to add the dependency to the loss

117 views Asked by Kevin Sun At 10 September 2017 at 21:50

score 279 · Answer 4 · 2017-09-13 02:21:27

tensorflow batch normalization gives doesn't work as expected when is_training flag is False

279 views Asked by kauDaOtha At 13 September 2017 at 02:21

score 248 · Answer 5 · 2017-09-16 09:09:29

OOM when using placeholder for is_training when use slim BN

248 views Asked by xxxzhi At 16 September 2017 at 09:09

score 64 · Answer 6 · 2020-09-29 13:26:21

On the use of Batch Normalization

64 views Asked by Mathew Wilson At 29 September 2020 at 13:26

score 7626 · Answer 7 · 2020-10-05 06:46:07

tf.keras.layers.BatchNormalization with trainable=False appears to not update its internal moving mean and variance

7.6k views Asked by Addy At 05 October 2020 at 06:46

score 7703 · Answer 8 · 2020-10-15 03:13:33

How to implement Batchnorm2d in Pytorch myself?

7.7k views Asked by lemonramen3 At 15 October 2020 at 03:13

score 780 · Answer 9 · 2020-10-23 14:06:56

Theory behind state normalization in Reinforcement Learning

780 views Asked by Barış Yazıcı At 23 October 2020 at 14:06

score 211 · Answer 10 · 2023-11-15 09:56:37

Batchnorms force set to training mode on torch.onnx.export when running stats are None

211 views Asked by lpe At 15 November 2023 at 09:56

TechQA.

List Question

How to prevent weight update in caffe

Use tf.layers.batch_normalization to preprocess inputs for SELU activation function?

In tf.slim, whether I need to add the dependency to the loss

tensorflow batch normalization gives doesn't work as expected when is_training flag is False

OOM when using placeholder for is_training when use slim BN

On the use of Batch Normalization

tf.keras.layers.BatchNormalization with trainable=False appears to not update its internal moving mean and variance

How to implement Batchnorm2d in Pytorch myself?

Theory behind state normalization in Reinforcement Learning

Batchnorms force set to training mode on torch.onnx.export when running stats are None

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