I'm trying to reproduce some Python MXNet code in Julia 0.6.0, and I'm getting a BoundsError if I try to use a batch size that is smaller than the dimension of the output. If I use a larger batch size in a toy example, things work properly and the network converges to the correct solution, but in my application the output dimension is large so this isn't practical.
Here's a linear regression example that gives this error:
using MXNet
net = mx.Variable(:data)
net = mx.FullyConnected(net, name=:fc0, num_hidden=5)
net = mx.LinearRegressionOutput(net, name=:output)
mod = mx.FeedForward(net, context=mx.cpu(0))
batch_size = 4 # works for batch_size > 4
A = randn(5,100)
train_in = randn(100,1000)
train_out = A*train_in + .1*randn(5,1000)
train_provider = mx.ArrayDataProvider(:data=>train_in,
:output_label=>train_out,
shuffle=true,
batch_size=batch_size)
optimizer = mx.SGD(lr=0.001, momentum=0.9, weight_decay=0.00001)
mx.fit(mod, optimizer, train_provider)
This produces
INFO: Start training on MXNet.mx.Context[CPU0]
INFO: Initializing parameters...
INFO: Creating KVStore...
INFO: TempSpace: Total 0 MB allocated on CPU0
INFO: Start training...
ERROR: LoadError: BoundsError: attempt to access 5×4 Array{Float32,2} at index [Base.Slice(Base.OneTo(5)), 5]
If I increase the batch size to 5 or greater, it works as expected. What am I missing?
