lichen11 commented on issue #7968: [R] Transfer Learning using VGG-16 URL: https://github.com/apache/incubator-mxnet/issues/7968#issuecomment-332622479 Hi, I believe I have set up the transfer learning correctly, retraining the last fully connected layer. I also made sure the names match the pretrained vgg model. However, R would always crash. When I use on inception-bn or inception-v3, it works fine. Is there another source to download mxnet vgg weights? Below is my code for vgg transfer learning. The data is using the data from the cat/dog classification problem https://statist-bhfz.github.io/cats_dogs_finetune. vgg <- mx.model.load("vgg19", iteration = 0) symbol <- vgg$symbol internals <- symbol$get.internals() outputs <- internals$outputs drop7 <- internals$get.output(which(outputs == "drop7_output")) fc_final <- mx.symbol.FullyConnected(data = drop7, num.hidden = 2, name = 'fc8') new_soft <- mx.symbol.SoftmaxOutput(data = fc_final, name = 'prob') arg_params_new <- mxnet:::mx.model.init.params( symbol = new_soft, input.shape = c(224, 224, 3, 8), output.shape = (8), initializer = mxnet:::mx.init.uniform(0.1), ctx = mx.gpu(0) )$arg.params fc8_weights_new <- arg_params_new[["fc8_weight"]] fc8_bias_new <- arg_params_new[["fc8_bias"]] arg_params_new <- vgg$arg.params arg_params_new[["fc8_weight"]] <- fc8_weights_new arg_params_new[["fc8_bias"]] <- fc8_bias_new model <- mx.model.FeedForward.create( symbol = new_soft, X = train, eval.data = val, ctx = mx.gpu(0), eval.metric = mx.metric.accuracy, num.round = 1, learning.rate = 0.05, momentum = 0.9, wd = 0.00001, kvstore = "local", array.batch.size = 128, epoch.end.callback = mx.callback.save.checkpoint("vgg"), batch.end.callback = mx.callback.log.train.metric(150), initializer = mx.init.Xavier(factor_type = "in", magnitude = 2.34), optimizer = "sgd", arg.params = arg_params_new, aux.params = vgg$aux.params ) ---------------------------------------------------------------- This is an automated message from the Apache Git Service. To respond to the message, please log on GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: [email protected]
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