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Added an example of Conv to Flux.jl/src/layers/conv.jl, and clarified what WHCN means

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Rohith Pentaparthy 2019-02-23 14:31:27 -06:00
parent ebf50f4e1c
commit 1b1dff1266
1 changed files with 11 additions and 2 deletions
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src/layers/conv.jl
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@ -12,8 +12,17 @@ expand(N, i::Integer) = ntuple(_ -> i, N)
Standard convolutional layer. `size` should be a tuple like `(2, 2)`. Standard convolutional layer. `size` should be a tuple like `(2, 2)`.
`in` and `out` specify the number of input and output channels respectively. `in` and `out` specify the number of input and output channels respectively.
Data should be stored in WHCN order. In other words, a 100×100 RGB image would Example: Applying Conv layer to a 1-channel input using a 2x2 window size,
be a `100×100×3×1` array, and a batch of 50 would be a `100×100×3×50` array. giving us a 16-channel output. Output is activated with ReLU.
size = (2,2)
in = 1
out = 16
Conv((2, 2), 1=>16, relu)
Data should be stored in WHCN order (width, height, # channels, # batches).
In other words, a 100×100 RGB image would be a `100×100×3×1` array,
and a batch of 50 would be a `100×100×3×50` array.
Takes the keyword arguments `pad`, `stride` and `dilation`. Takes the keyword arguments `pad`, `stride` and `dilation`.
""" """