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Julia Flux error: SGD optimiser is undefined

By : Brandon Guzman
Date : August 28 2020, 02:00 PM
should help you out The tutorial uses an outdated version of Flux.
In version v0.10.0 of Flux, Flux has deprecated usage of SGD in favor of Descent which is just a more optimised version of the Standard Gradient Descent algorithm.
code :
# New Way
Flux.train!(loss, params(model), data, optimizer)

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Julia Flux, images with different dimension in neural network

By : James Wetz
Date : March 29 2020, 07:55 AM
will be helpful for those in need You can do this with the packages Images and ImageMagick (you need to install both), and then:
code :
using Images
img = Images.load("test.png");
size(img) # outputs (128, 128)
img2 = imresize(img,(50,50));

Stop tracking Arrays in Flux (Julia)

By : user2861632
Date : March 29 2020, 07:55 AM
should help you out I am currently trying to implement a batch update in Flux for Julia. , Okay, as it turns out, there is a function
code :

How to feed a dictionary to a Flux model in Julia

By : hrn111
Date : March 29 2020, 07:55 AM
wish help you to fix your issue So I have a 20000x4 dataset, where the 4 columns have strings. The first is a description and the other three are categories, the last one being the one I wish to predict. I tokenized every word of the first column and saved it in a dictionary, with his respective Int value, and I changed the other columns to have numerical values. Now I'm having trouble to understand how to feed these data in a Flux model. , Consider this example:
code :
using DataFrames

df = DataFrame()
df.food = rand(["apple", "banana", "orange"], 20)

multiplier(fruit) = (1 + (0.1 * rand())) * (fruit == "apple" ? 95 : 
    fruit == "orange" ? 45 : 105)
foodtoken(f) = (fruit == "apple" ? 0 : fruit == "orange" ? 2 : 3)

df.calories = multiplier.(df.food)
foodtoken(f) = (fruit == "apple" ? 0 : fruit == "orange" ? 2 : 3)

fooddict = Dict(fruit => (fruit == "apple" ? 0 : fruit == "orange" ? 2 : 3)
    for fruit in df.food)
df.token = map(x -> fooddict[x], df.food)

function colvalue(s, dict)
    total = 0
    for (k, v) in dict
        if occursin(k, s)
            total += 10^v

words_to_numbers(dataframe, 1, dict_x)
categories_to_numbers(dataframe, 4, dict_y)

dataframe.descripval = map(x -> colvalue(x, dict_x), dataframe.Description)
dataframe.catval = map(x -> colvalue(x, dict_y), dataframe.Category)


Julia Flux issue: UndefVarError: Tracker not defined

By : user3590968
Date : March 29 2020, 07:55 AM
fixed the issue. Will look into that further I've found 2 solutions to this problem:
Downgrade to Flux v0.9 (which has Flux.Tracker) using Pkg.add(Pkg.PackageSpec(;name="Flux", version="0.9.0")) Edit the NeuralNetDiffEq ODE solver jl files:

Using quantile in Flux (Julia) in loss function

By : user3593346
Date : March 29 2020, 07:55 AM
This might help you Ideally, we'd define a custom adjoint for quantile so that this works out of the box. (Feel free to open an issue to remind us to do this.)
In the mean time there's a quick workaround. It's actually the sorting that causes trouble here so if you do quantile(xs, p, sorted=true) it'll work. Obviously this requires xs to be sorted to get correct results, so you might need to use quantile(sort(xs), ...).
code :
julia> using Zygote: @adjoint

julia> @adjoint function sort(x)
         p = sortperm(x)
         x[p], x̄ -> (x̄[invperm(p)],)

julia> gradient(x -> quantile(sort(x), 0.5, sorted=true), [1, 2, 3, 3])
([0.0, 0.5, 0.5, 0.0],)
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