caffe - BatchNorm and Reshuffle train images after each epoch -


the recommended way of using batchnorm reshuffle training imageset between each epoch, given image not fall in mini-batch same images on each pass.

how achieve caffe?

if use imagedata layer input, set "shuffle" true.

for example, if have:

layer {   name: "data"   type: "imagedata"   top: "data"   top: "label"   transform_param {     mirror: false     crop_size: 227     mean_file: "data/ilsvrc12/imagenet_mean.binaryproto"   }   image_data_param {     source: "examples/_temp/file_list.txt"     batch_size: 50     new_height: 256     new_width: 256   } }

just add:

layer {   name: "data"   type: "imagedata"   top: "data"   top: "label"   transform_param {     mirror: false     crop_size: 227     mean_file: "data/ilsvrc12/imagenet_mean.binaryproto"   }   image_data_param {     source: "examples/_temp/file_list.txt"     batch_size: 50     new_height: 256     new_width: 256     shuffle: true   } }

for documentation, see:

you can find source code here:

of particular interest code within function load_batch re-shuffles data @ end of each epoch:

lines_id_++; if (lines_id_ >= lines_size) {   // have reached end. restart first.   dlog(info) << "restarting data prefetching start.";   lines_id_ = 0;   if (this->layer_param_.image_data_param().shuffle()) {     shuffleimages();   } }

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