Caffe – aborted training

Issue I am trying to train from scratch a caffe model (in docker). pwd: root@982adaaca24f:~/sharedfolder/caffe/docker/image/happyNet# relevant files path: models/ Custom_Model/ deploy.prototxt solver.prototxt train.prototxt datasets/ training_set_lmdb/ data.mdb (5,01 GB) lock.mdb validation_set_lmdb/ data.mdb (163,8 GB) lock.mdb for that I’m running: #~/caffe/build/tools/caffe train

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how to plot training error and validation error vs number of epochs?

Issue how to plot training error and validation error vs number of epochs? train_data = generate_arrays_for_training(indexPat, filesPath, end=75) validation_data=generate_arrays_for_training(indexPat, filesPath, start=75) model.fit_generator(generate_arrays_for_training(indexPat, filesPath, end=75), #end=75),#It take the first 75% validation_data=generate_arrays_for_training(indexPat, filesPath, start=75),#start=75), #It take the last 25% #steps_per_epoch=10000, epochs=10) steps_per_epoch=int((len(filesPath)-int(len(filesPath)/100*25))),#*25),

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I try to implement vggnet, but it does not be trained well

Issue I am new at CNN. I try to train vggnet. class Net(nn.Module) : def __init__(self) : super(Net, self).__init__() self.conv = nn.Sequential ( #1 ##### nn.Conv2d(3,64,3, padding=1), nn.ReLU(inplace=True), nn.Conv2d(64,64,3, padding=1),nn.ReLU(inplace=True), nn.MaxPool2d(2,2), #2 ##### nn.Conv2d(64,128,3, padding=1), nn.ReLU(inplace=True), nn.Conv2d(128,128,3, padding=1),nn.ReLU(inplace=True), nn.MaxPool2d(2,2), #####

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