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Flux.jl
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cossio 2020-06-12 02:09:17 +02:00
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3
NEWS.md
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# v0.11
* Change to `DataLoader`'s constructor [https://github.com/FluxML/Flux.jl/pull/1152]
* Change to `DataLoader`'s constructor [https://github.com/FluxML/Flux.jl/pull/1152]
* Use `DataLoader` with `NamedTuple`s, so that tensors can be accessed by name [https://github.com/FluxML/Flux.jl/pull/1221].
* Error if Dense layers weights and biases are not arrays [https://github.com/FluxML/Flux.jl/pull/1218].
# v0.10.5

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src/data/dataloader.jl
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An object that iterates over mini-batches of `data`, each mini-batch containing `batchsize` observations
(except possibly the last one).
Takes as input a data tensors or a tuple of one or more such tensors.
The last dimension in each tensor is considered to be the observation dimension.
Takes as input a data tensors or a tuple (or `NamedTuple`) of one or more such tensors.
The last dimension in each tensor is considered to be the observation dimension.
If `shuffle=true`, shuffles the observations each time iterations are re-started.
If `partial=false`, drops the last mini-batch if it is smaller than the batchsize.
@ -57,6 +57,13 @@ Usage example:
# train for 10 epochs
using IterTools: ncycle
Flux.train!(loss, ps, ncycle(train_loader, 10), opt)
# can use NamedTuple to name tensors
train_loader = DataLoader((images = Xtrain, labels = Ytrain), batchsize=2, shuffle=true)
for datum in train_loader
@assert size(datum.images) == (10, 2)
@assert size(datum.labels) == (2,)
end
"""
function DataLoader(data; batchsize=1, shuffle=false, partial=true)
batchsize > 0 || throw(ArgumentError("Need positive batchsize"))