batch-normalization

问题 I'm using PyTorch to implement a classification network for skeleton-based action recognition. The model consists of three convolutional layers and two fully connected layers. This base model gave me an accuracy of around 70% in the NTU-RGB+D dataset. I wanted to learn more about batch normalization, so I added a batch normalization for all the layers except for the last one. To my surprise, the evaluation accuracy dropped to 60% rather than increasing But the training accuracy has increased

问题 I'm using PyTorch to implement a classification network for skeleton-based action recognition. The model consists of three convolutional layers and two fully connected layers. This base model gave me an accuracy of around 70% in the NTU-RGB+D dataset. I wanted to learn more about batch normalization, so I added a batch normalization for all the layers except for the last one. To my surprise, the evaluation accuracy dropped to 60% rather than increasing But the training accuracy has increased

问题 I'm trying to copy pre-trained BN weights from a pytorch model to its equivalent Keras model but I keep getting different outputs. I read Keras and Pytorch BN documentation and I think that the difference lies in the way they calculate the "mean" and "var". Pytorch: The mean and standard-deviation are calculated per-dimension over the mini-batches source: Pytorch BatchNorm Thus, they average over samples. Keras: axis: Integer, the axis that should be normalized (typically the features axis).

问题 I am using Stochastic Weight Averaging (SWA) with Batch Normalization layers in Tensorflow 2.2. For Batch Norm I use tf.keras.layers.BatchNormalization . For SWA I use my own code to average the weights (I wrote my code before tfa.optimizers.SWA appeared). I have read in multiple sources that if using batch norm and SWA we must run a forward pass to make certain data (running mean and st dev of activation weights and/or momentum values?) available to the batch norm layers. What I do not

问题 I am using Stochastic Weight Averaging (SWA) with Batch Normalization layers in Tensorflow 2.2. For Batch Norm I use tf.keras.layers.BatchNormalization . For SWA I use my own code to average the weights (I wrote my code before tfa.optimizers.SWA appeared). I have read in multiple sources that if using batch norm and SWA we must run a forward pass to make certain data (running mean and st dev of activation weights and/or momentum values?) available to the batch norm layers. What I do not

问题 I am using Stochastic Weight Averaging (SWA) with Batch Normalization layers in Tensorflow 2.2. For Batch Norm I use tf.keras.layers.BatchNormalization . For SWA I use my own code to average the weights (I wrote my code before tfa.optimizers.SWA appeared). I have read in multiple sources that if using batch norm and SWA we must run a forward pass to make certain data (running mean and st dev of activation weights and/or momentum values?) available to the batch norm layers. What I do not

问题 According to the official documents from tensorflow: About setting layer.trainable = False on a `BatchNormalization layer: The meaning of setting layer.trainable = False is to freeze the layer, i.e. its internal state will not change during training: its trainable weights will not be updated during fit() or train_on_batch(), and its state updates will not be run. Usually, this does not necessarily mean that the layer is run in inference mode (which is normally controlled by the training

问题 The following content comes from Keras tutorial This behavior has been introduced in TensorFlow 2.0, in order to enable layer.trainable = False to produce the most commonly expected behavior in the convnet fine-tuning use case. Why we should freeze the layer when fine-tuning a convolutional neural network? Is it because some mechanisms in tensorflow keras or because of the algorithm of batch normalization? I run an experiment myself and I found that if trainable is not set to false the model

问题 I have been trying to implement the DCGan, the face book's paper, and blocked by below two issues almost for 2 weeks. Any suggestions would be appreciated. Thanks. Issue 1: DCGAN paper suggest to use BN(Batch Normalization) both the generator and discriminator. But, I couldn't get better result with BN rather than w/out BN. I copied DCGAN model I used which is exactly the same with a DCGAN paper. I don't think it is due to overfitting. Because (1) It keeps showing the noise the same with an

问题 I have been trying to implement the DCGan, the face book's paper, and blocked by below two issues almost for 2 weeks. Any suggestions would be appreciated. Thanks. Issue 1: DCGAN paper suggest to use BN(Batch Normalization) both the generator and discriminator. But, I couldn't get better result with BN rather than w/out BN. I copied DCGAN model I used which is exactly the same with a DCGAN paper. I don't think it is due to overfitting. Because (1) It keeps showing the noise the same with an