[GitHub] lichen11 commented on issue #7968: [R] Transfer Learning using VGG-16

[GitBox <gi...@apache.org>]

lichen11 commented on issue #7968: [R] Transfer Learning using VGG-16
URL: https://github.com/apache/incubator-mxnet/issues/7968#issuecomment-355379685
 
 
   Hi, I recently attempted transfer learning on ResNet101. I only retrain the last fully connected layer. 
   
       resnet101<- mx.model.load("Model/ResNet/resnet-101", iteration=0)
       symbol<- resnet101$symbol
       internals<- symbol$get.internals()
       outputs<- internals$outputs
       flatten<- internals$get.output(which(outputs=="flatten0_output"))
       new_fc<- mx.symbol.FullyConnected(data=flatten, num_hidden=2, name="fc1") 
       new_soft <- mx.symbol.SoftmaxOutput(data=new_fc, name='softmax')
       arg_params_new<- mxnet:::mx.model.init.params(
           symbol = new_soft, 
           input.shape = list(data = c(224,224,3,32)), 
           output.shape = NULL,
           initializer = mxnet:::mx.init.uniform(0.1), 
           ctx =mx.gpu(0) 
           )$arg.params
   
       fc1_weights_new<- arg_params_new[["fc1_weight"]]
       fc1_bias_new<- arg_params_new[["fc1_bias"]]
       arg_params_new <- resnet101$arg.params
       arg_params_new[["fc1_weight"]] <- fc1_weights_new 
       arg_params_new[["fc1_bias"]] <- fc1_bias_new 
   
   However, when I initiate training, my R would crash. It first gives the following msg:
   
       Start training with 1 devices
       [19:35:16] src/operator/nn/./cudnn/./cudnn_algoreg-inl.h:107: Running performance tests to find the         best convolution algorithm, this can take a while... (setting env variable MXNET_CUDNN_AUTOTUNE_DEFAULT to 0 to disable)
   
   Then it crashes. I searched online to set MXNET_CUDNN_AUTOTUNE to 0 to disable and updated my mxnet to 1.0.0., since some say this version will resolve the MXNET_CUDNN_AUTOTUNE issue. However, after updating, my R is still crashing when using ResNet or VGG. Meanwhile, transfer learning using Inception does not crash. I am wondering if there is an internal bug in mxnet R to cause this issue. 

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