CNN with Julia

CNNPrimer4

# Loss function
# See: https://github.com/FluxML/model-zoo/blob/master/vision/mnist/conv.jl
# `loss()` calculates the crossentropy loss between our prediction `y_hat`
function loss(x, y)
    # Add some noise to the image
    # we reduce the risk of overfitting the train sample by doing so:
    x_aug = x .+ 0.1f0*gpu(randn(eltype(x), size(x)))

    y_hat = model(x_aug)
    return crossentropy(y_hat, y)
end
accuracy(x, y) = mean(onecold(model(x)) .== onecold(y))

# ADAM optimizer
opt = ADAM(0.001);