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 ...

I am wondering, if in Convolutional Neural Networks batch normalization should be applied with respect to every pixel separately, or should I take the mean of pixels with respect to each channel? ...

Can I use batch normalization layer right after input layer and not normalize my data? May I expect to get similar effect/performance? In keras functional it would be something like this: x = Inp...

Once you do normalisation of your data so the values are between 0-1, how do you de-normalise it so you can interpret the result? So when you normalise your data, and feed it to your network and g...

I met an error when I use BatchNorm1d, code: ##% first I set a model class net(nn.Module): def __init__(self, max_len, feature_linear, rnn, input_size, hidden_size, output_dim, num__rnn_layers, bi...

I am following the Transfer learning and fine-tuning guide on the official TensorFlow website. It points out that during fine-tuning, batch normalization layers should be in inference mode: Import...

I am trying to use batch normalization layers whith U-net for the segmentation task. Same layers works fine for res-net, vgg, xception etc., and I'm curious if it is an architecture dependent probl...

What will happen when I use batch normalization but set batch_size = 1? Because I am using 3D medical images as training dataset, the batch size can only be set to 1 because of GPU limitation. Nor...

The model.eval() method modifies certain modules (layers) which are required to behave differently during training and inference. Some examples are listed in the docs: This has [an] effect only on...

I have the following architecture: Conv1 Relu1 Pooling1 Conv2 Relu2 Pooling3 FullyConnect1 FullyConnect2 My question is, where do I apply batch normalization? And what would be the best function...

If I want to use the BatchNormalization function in Keras, then do I need to call it once only at the beginning? I read this documentation for it: http://keras.io/layers/normalization/ I don't se...

May I ask if the following understanding of batch normalization in convolutional neural network is correct? As shown in the diagram below, the mean and variance are calculated using all the cells o...

I'm adding some batch normalization to my model in order to improve the training time, following some tutorials. This is my model: model = Sequential() model.add(Conv2D(16, kernel_size=(3, 3), ac...

I am trying to find out, how exactly does BatchNormalization layer behave in TensorFlow. I came up with the following piece of code which to the best of my knowledge should be a perfectly valid ker...

I'm implementing a Keras model with a custom batch-renormalization layer, which has 4 weights (beta, gamma, running_mean, and running_std) and 3 state variables (r_max, d_max, and t): self.gamma =...

I'd like to know the possible ways to implement batch normalization layers with synchronizing batch statistics when training with multi-GPU. Caffe Maybe there are some variants of caffe that coul...

I'm wondering what the current available options are for simulating BatchNorm folding during quantization aware training in Tensorflow 2. Tensorflow 1 has the tf.contrib.quantize.create_training_gr...

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 th...

Why do I need to pass the previous nummber of channels to the batchnorm? The batchnorm should normalize over each datapoint in the batch, why does it need to have the number of channels then ?

inputs = Input((img_height, img_width, img_ch)) conv1 = Conv2D(n_filters, (k, k), padding=padding)(inputs) conv1 = BatchNormalization(scale=False, axis=3)(conv1) conv1 = Activation('relu')(conv1) ...

I am newbie in convolutional neural networks and just have idea about feature maps and how convolution is done on images to extract features. I would be glad to know some details on applying batch ...

I am a little confused about how should I use/insert "BatchNorm" layer in my models. I see several different approaches, for instance: ResNets: "BatchNorm"+"Scale" (no parameter sharing) "BatchNo...

torch.nn has classes BatchNorm1d, BatchNorm2d, BatchNorm3d, but it doesn't have a fully connected BatchNorm class? What is the standard way of doing normal Batch Norm in PyTorch?

When I implement batch normalization in python from scrach, I am confused. Please see A paper demonstrates some figures about normalization methods, I think it may be not correct. The description ...

For a CNN architecture I want to use SpatialDropout2D layer instead of Dropout layer. Additionaly I want to use BatchNormalization. So far I had always set the BatchNormalization directly after a C...