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BoundsError if the batch size is smaller than the dimension of the output

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Assessment

Difficulty
2/5
Estimated time
1-3 hours
Newbie friendliness
42/100
Issue type
Bug
Clarity
Clearly specified
Activity status
Stale
Tech stack
julia
Domain
machine-learning

Research direction

Start at src/metric.jl, especially _update_single_output, which the stack trace identifies as the failing entry point during mx.fit. Reproduce the example with batch_size=4 and verify that training completes without a BoundsError when the output dimension exceeds the batch size.

Written by the indexing model from the issue text.

Description

Originally filed here: https://stackoverflow.com/questions/45406537/boundserror-in-julia-mxnet-when-using-small-batch-size (reported by: Robert Crandall)

quote:
'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]

Update from Viacheslav Kovalevskyi:
When I try to reproduce the bug on the master I got:

julia> mx.fit(mod, optimizer, train_provider)
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: BoundsError: attempt to access 5_4 Array{Float32,2} at index [Base.Slice(Base.OneTo(5)), 5]
Stacktrace:
[1] throw_boundserror(::Array{Float32,2}, ::Tuple{Base.Slice{Base.OneTo{Int64}},Int64}) at ./abstractarray.jl:433
[2] checkbounds at ./abstractarray.jl:362 [inlined]
[3] view at ./subarray.jl:113 [inlined]
[4] _update_single_output(::MXNet.mx.Accuracy, ::Array{Float32,2}, ::Array{Float32,2}) at /home/ubuntu/.julia/v0.6/MXNet/src/metric.jl:211
[5] macro expansion at /home/ubuntu/.julia/v0.6/MXNet/src/metric.jl:58 [inlined]
[6] macro expansion at /home/ubuntu/.julia/v0.6/MXNet/src/ndarray.jl:783 [inlined]
[7] _update!(::MXNet.mx.Accuracy, ::Array{MXNet.mx.NDArray,1}, ::Array{MXNet.mx.NDArray,1}, ::Val{false}) at /home/ubuntu/.julia/v0.6/MXNet/src/metric.jl:55
[8] update!(::MXNet.mx.Accuracy, ::Array{MXNet.mx.NDArray,1}, ::Array{MXNet.mx.NDArray,1}) at /home/ubuntu/.julia/v0.6/MXNet/src/metric.jl:34
[9] #fit#8303(::Array{Any,1}, ::Function, ::MXNet.mx.FeedForward, ::MXNet.mx.SGD, ::MXNet.mx.ArrayDataProvider) at /home/ubuntu/.julia/v0.6/MXNet/src/model.jl:524
[10] fit(::MXNet.mx.FeedForward, ::MXNet.mx.SGD, ::MXNet.mx.ArrayDataProvider) at /home/ubuntu/.julia/v0.6/MXNet/src/model.jl:351

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