[GitHub] thirdwing commented on issue #6629: Not enough information to get shape

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

thirdwing commented on issue #6629: Not enough information to get shape
URL: https://github.com/apache/incubator-mxnet/issues/6629#issuecomment-321399282
 
 
   With the latest version of mxnet from github, the code works well:
   
   ```r
   library(mxnet)
   
   train.x = matrix(data = rexp(200, rate = 10), nrow = 120, ncol = 6380)
   train.y = matrix(data = rexp(6380, rate = 10), nrow = 120, ncol = 319)
   
   # Reshape testing data
   train.array <- train.x
   dim(train.array) <- c(319, 20, 120)
   dim(train.y) <- c(319, 120)
   
   data <- mx.symbol.Variable("data")
   
   # Define the first fully connected layer
   fc1 <- mx.symbol.FullyConnected(data, num_hidden = 100)
   act.fun <- mx.symbol.Activation(fc1, act_type = "relu") # create a hidden layer with Rectified Linear Unit as its activation function.
   output <- mx.symbol.FullyConnected(act.fun, num_hidden = 319)
   
   # Customize loss function
   label <- mx.symbol.Variable("label")
   
   output_mean <- mx.symbol.mean(output)
   label_mean <- mx.symbol.mean(label)
   
   output_delta <- mx.symbol.broadcast_sub(output, output_mean)
   label_delta <- mx.symbol.broadcast_sub(label, label_mean)
   
   output_sqr <- mx.symbol.square(output_delta)
   label_sqr <- mx.symbol.square(label_delta)
   
   output_sd <- mx.symbol.sqrt(mx.symbol.sum(output_delta))
   label_sd <- mx.symbol.sqrt(mx.symbol.sum(label_delta))
   
   numerator <- mx.symbol.sum(output_delta * label_delta)
   denominator <- output_sd * label_sd
   
   lro <- mx.symbol.MakeLoss(numerator / denominator)
   
   # Generate a new model
   model <- mx.model.FeedForward.create(symbol = lro,
                                        X = train.array,
                                        y = train.y,
                                        num.round = 5000,
                                        array.batch.size = 1,
                                        optimizer = "adam",
                                        learning.rate = 0.0003,
                                        eval.metric = mx.metric.rmse,
                                        epoch.end.callback = mx.callback.log.train.metric(20))
   ```
   ```
   Start training with 1 devices
   [1] Train-rmse=NaN
   .......
   ```
   
   The output of `Makeloss` is the gradient, so the `mx.metric.rmse` produced NaN.
   
   If you are using the prebuilt pkg, please wait for the update. I will update it soon.
 
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