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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