batch-normalization

I wanted to run the Deconvnet on my data, however it seemd it has been written for another version of caffe . Does anyone know how to change batch_params ? The one that is in Deconvnet layers { bottom: 'conv1_1' top: 'conv1_1' name: 'bn1_1' type: BN bn_param { scale_filler { type: 'constant' value: 1 } shift_filler { type: 'constant' value: 0.001 } bn_mode: INFERENCE } } And the one that Caffe provides for cifar10 example: layer { name: "bn1" type: "BatchNorm" bottom: "pool1" top: "bn1" batch_norm_param { use_global_stats: true } param { lr_mult: 0 } param { lr_mult: 0 } param { lr_mult: 0 }

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) "BatchNorm" layer is followed immediately with "Scale" layer: layer { bottom: "res2a_branch1" top: "res2a_branch1" name: "bn2a_branch1" type: "BatchNorm" batch_norm_param { use_global_stats: true } } layer { bottom: "res2a_branch1" top: "res2a_branch1" name: "scale2a_branch1" type: "Scale" scale_param { bias_term: true } } cifar10 example : only "BatchNorm" In the cifar10 example provided with caffe,

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 normalisation in CNN. I read this paper https://arxiv.org/pdf/1502.03167v3.pdf and could understand the BN algorithm applied on a data but in the end they mentioned that a slight modification is required when applied to CNN: For convolutional layers, we additionally want the normalization to obey the convolutional property – so that different elements of the same feature map, at different locations,

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 see where I'm supposed to call it. Below is my code attempting to use it: model = Sequential() keras.layers.normalization.BatchNormalization(epsilon=1e-06, mode=0, momentum=0.9, weights=None) model.add(Dense(64, input_dim=14, init='uniform')) model.add(Activation('tanh')) model.add(Dropout(0.5)) model.add(Dense(64, init='uniform')) model.add(Activation('tanh')) model.add(Dropout(0.5)) model.add(Dense(2,

In tensorflow 1.4, I found two functions that do batch normalization and they look same: tf.layers.batch_normalization ( link ) tf.contrib.layers.batch_norm ( link ) Which function should I use? Which one is more stable? Just to add to the list, there're several more ways to do batch-norm in tensorflow: tf.nn.batch_normalization is a low-level op. The caller is responsible to handle mean and variance tensors themselves. tf.nn.fused_batch_norm is another low-level op, similar to the previous one. The difference is that it's optimized for 4D input tensors, which is the usual case in