caffe

Is it possible to change the input source of ImageData layer or a MemoryData layer on the fly? I am trying to shuffle the data every epoch but I have both image and some other non-image features that I want to concatenate at a later stage in the network. I could not find a reliable way to shuffle both the image and my other data in a way that preserves the alignment of the two. So, I am thinking of re-generating the imagelist.txt as well as nonimage data (in Memory) every epoch and attach the new file to the ImageData layer and initialize MemoryDataLayer with the new data. How can I make sure

I'm building one but, and when I use the custom train function provided on lenet example with a batch size bigger than 110 my accuracy gets bigger than 1 (100%). If I use batch size 32, I get 30 percent of accuracy. Batch size equal 64 my net accuracy is 64. And batch size equal to 128, the accuracy is 1.2. My images are 32x32. Train dataset: 56 images of Neutral faces. 60 images of Surprise faces. Test dataset: 15 images of Neutral faces. 15 images of Surprise faces. This is my code: def train(solver): niter = 200 test_interval = 25 train_loss = zeros(niter) test_acc = zeros(int(np.ceil(niter

Here are two LMDB databases. Is there any way to merge these two databases and feed it to network using caffe? Simply write a script using the python lmdb interface. Something like: import lmdb env = lmdb.open("path/to/lmdbFile") txn = env.begin(write=True) database1 = txn.cursor("db1Name") database2 = txn.cursor("db2Name") env.open_db(key="newDBName", txn=txn) newDatabase = txt.cursor("newDBName") for (key, value) in database1: newDatabase.put(key, value) for (key, value) in database2: newDatabase.put(key, value) or you could just as simply add one to the other by: for (key, value) in

If I define multiple loss layers in a network, will there be multiple back propagation happening from those ends to the beginning of the network? I mean, do they even work that way? Suppose I have something like this: Layer1{ } Layer2{ } ... Layer_n{ } Layer_cls1{ bottom:layer_n top:cls1 } Layer_cls_loss1{ type:some_loss bottom:cls1 top:loss1 } Layer_n1{ bottom:layer_n .. } Layer_n2{ } ... layer_n3{ } Layer_cls2{ bottom:layer_n3 top:cls2 } Layer_cls_loss2{ type:some_loss bottom:cls2 top:loss2 } layer_n4{ bottom:layer_n3 .. } ... layer_cls3End{ top:cls_end bottom:... } loss{ bottom:cls_end top

I'm somewhat new to caffe, and I'm getting some strange behavior. I'm trying to use fine tuning on the bvlc_reference_caffenet to accomplish an OCR task. I've taken their pretrained net, changed the last FC layer to the number of output classes that I have, and retrained. After a few thousand iterations I'm getting loss rates of ~.001, and an accuracy over 90 percent when the network tests. That said, when I try to run my network on data by myself, I get awful results, not exceeding 7 or 8 percent. The code I'm using to run the net is: [imports] net = caffe.Classifier('bvlc_reference_caffenet

I'm trying to make my classification process a bit faster. I thought of increasing the first input_dim in my deploy.prototxt but that does not seem to work. It's even a little bit slower than classifying each image one by one. deploy.prototxt input: "data" input_dim: 128 input_dim: 1 input_dim: 120 input_dim: 160 ... net description ... python net initialization net=caffe.Net( 'deploy.prototxt', 'model.caffemodel', caffe.TEST) net.blobs['data'].reshape(128, 1, 120, 160) transformer = caffe.io.Transformer({'data':net.blobs['data'].data.shape}) #transformer settings python classification images=