[GitHub] train-test-laura opened a new issue #7364: MxnetR chunk-wise neural nets

[gi...@git.apache.org]

train-test-laura opened a new issue #7364: MxnetR chunk-wise neural nets
URL: https://github.com/apache/incubator-mxnet/issues/7364
 
 
   Dear all,
   
   I just now started using the mxnet R package in Windows. 
   
   I have a pretty big dataset which I need to process in chunks due to memory limits. Therefore, I split my dataset and wanted to run a feed-forward  neural net on chunk 1, and use the parameters (weights and bias) for chunk 2 , which in turn can be used for chunk 3, and so on. However, despite some examples [](https://statist-bhfz.github.io/cats_dogs_finetune) I cannot get that to work.
   
   My model looks like that:
   `## set up neural network
   data <- mx.symbol.Variable("data")
   fc1 <- mx.symbol.FullyConnected(data, name="fc1", num_hidden=150)
   act1 <- mx.symbol.Activation(fc1, name="relu1", act_type="relu")
   fc2 <- mx.symbol.FullyConnected(act1, name="fc2", num_hidden=100)
   softmax <- mx.symbol.SoftmaxOutput(fc2, name="sm")
   
   prefix='Checkpoints/chkpt'
   ## train!
   system.time(model <- mx.model.FeedForward.create(softmax, X=train.x, y=train.y,
                                                    ctx=devices, num.round=20, array.batch.size=200,
                                                    learning.rate=0.09, momentum=0.9,  eval.metric=mx.metric.accuracy,
                                                    optimizer='sgd', 
                                                    epoch.end.callback=mx.callback.save.checkpoint(prefix),
                                                    batch.end.callback=mx.callback.log.speedometer(200, frequency = 200)))
   
   `
   and now I tried to follow other posts and used 
   net001<- mx.model.load(prefix, iteration=20)
   
   symbol<- net001$symbol
   internals<- symbol$get.internals()
   outputs<- internals$outputs
   
   arg_params_batch1<- net001$arg.params
   fc1_weights_batch1<- arg_params_batch1[["fc1_weight"]]
   fc2_weights_batch1<- arg_params_batch1i[["fc2_weight"]]
   
   But now I am unsure how to use the weights and bias for the next batch of mx.model.FeedForward.create. 
   Any hint would be greatly appreciated.
   Thank you,
   Best, Laura
 
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