I keep getting undefreferror whenever I call function size. Here is my code. The error is at the first `if`

in function `forward`

. Full error message is:

```
ERROR: LoadError: UndefRefError: access to undefined reference
in #forward#55(::Array{Any,1}, ::Function, ::NN.CrossEntropyLoss, ::Array{Float64,2}, ::Array{Int64,2}) at /Users/haonanchen/Documents/CS/NN.jl/src/layers/CrossEntropyLoss.jl:37
in forward(::NN.CrossEntropyLoss, ::Array{Float64,2}, ::Array{Int64,2}) at /Users/haonanchen/Documents/CS/NN.jl/src/layers/CrossEntropyLoss.jl:36
...
```

```
type CrossEntropyLoss <: LossCriteria
x :: Array{Float64}
y :: Array{Float64}
classes :: Int64
function CrossEntropyLoss()
return new(Float64[], Float64[], 0)
end
end
function init(l::CrossEntropyLoss, p::Union{Layer,Void}, config::Dict{String,Any}; kwargs...)
if p==nothing || typeof(p)<:InputLayer
error("Loss functions cannot be the first layer; makes no sense")
end
out_size = getOutputSize(p)
l.classes = out_size[2]
l.x = Array{Float64}(out_size)
l.y = Array{Float64}(out_size)
end
function convert_to_one_hot(old_label::Array{Int, 2})
m = size(old_label)[1]
new_label::Array{Int,2}
new_label=zeros(Int64,size(old_label,2)[1],l.classes)
for i=1:m
new_label[i][old_label[i]+1] = 1;
end
end
"""
Invaraint for this to work:
Label needs to be either a vector specifying each item's class or a matrix made up with multiple one hot vectors.
In the former case, labels need to be in the range of 0:classes-1.
"""
function forward(l::CrossEntropyLoss, Y::Array{Float64,2}, label::Array{Int, 2}; kwargs...)
if size(label)[2] == 1
# convert one-dim label to one hot vectors
label = convert_to_one_hot(label)
end
# from now on label is guaranteed to be of one-hot
@assert size(Y) = size(label)
loss = zeros(m)
m,n = size(Y)
for i=1:m
log_sum = 0;
for j=1:n
p = Y[i,j]
q = label[i,j]
log_sum+=q*log(q/p)
end
loss = log_sum/n
loss[i]=loss
end
x = Y;
y = loss;
# generate prediction
pred = zeros(m)
for i=1:m
pred[i] = findmax(Y[i,:])[2]-1
end
return loss, pred
end
"""
for each row x, let x_i be j^th element, loss(x)=log(q_i/x_i)/n+...(other elements)
thus d(loss_j)/dx_j=1/n*x_j/q = x_j/(q_j*n)
where j is the num of classes,
"""
function backward(l::CrossEntropyLoss, label::Array{Int, 2};kwargs...)
dldx = zeros(classes)
m,n=size(l.x)
for i=1:m
for j=:1:n
dlidx=l.x[i,j]/(label[i,j]*classes)
dldlx[j]+=dlidx
end
end
return dldx
end
```