# Data-formatting in and out of Flux ML model

**URL:** <https://discourse.julialang.org/t/data-formatting-in-and-out-of-flux-ml-model/65383>\
**Category:** Machine Learning\
**Created:** [July 27, 2021, 3:41pm UTC](https://discourse.julialang.org/t/data-formatting-in-and-out-of-flux-ml-model/65383 "2021-07-27T15:41:13Z")\
**Posts on this page:** 2\
**Page:** 1

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**Author:** ![amit1](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/amit1/32/25318_2.png) [@amit1](https://discourse.julialang.org/u/amit1)\
**Post date:** [July 27, 2021, 3:41pm UTC](https://discourse.julialang.org/t/data-formatting-in-and-out-of-flux-ml-model/65383/1 "2021-07-27T15:41:14Z")

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Hi, I am still learning the ropes of Flux framework. I have experience of tensorflow and bit of pytorch, but I am interested in Flux, mostly because of the flexibility and uniformity (no more to\_numpy and vice versa!).

I wrote a very simple example of a 2D function, but I am stuck querying one point at a time. as opposed to `model.predict(x)` usability of TF.

Given below is the snippet I wrote, can anyone please provide comments on more efficient ways of organizing the code, or perhaps better way of writing the functions (specially loss function is bit of hack right now i feel). Thank you in advance

```julia
using Flux

function f(x,y)
	z = x.^2 + y.^3
	return z
end

x = collect(-2.:0.01:2.)
y = collect(-2.:0.01:2.)
z = f(x,y)
xy = hcat(x,y)

nn = Chain(Dense(2,10,tanh),
			Dense(10,10,tanh),
			Dense(10,1)
			)
# Two loss functions, one for call back, as I could not get the
# same function general enough. 

function loss_cb(x,y)
	l = 0.0
	for i = 1:size(x)[1]
		l += (nn(x[i,:])[1] .- y[i]).^2
	end
	return l
end
	
function loss(x,y)
	l = 0.0
	for i = 1:length(x)
		l += (nn(x[i])[1] - y[i]).^2
	end
	return l
end

cb() = @show(loss_cb(xy,z))

# arrange data in individual tuples, else dataloader gives error saying two
# subsequent queries return different length of array. 802 vs 401.  
dataset = Vector(undef,401)
for i = 1:401
	dataset[i] = xy[i,:]
end

in_data = Flux.Data.DataLoader((dataset,z),batchsize=401)
opt = ADAM(0.1)

Flux.@epochs 200 Flux.train!(loss,Flux.params(nn),in_data,opt,cb=cb)

# how to get predictions on all of the query points at once?
prediction = nn(dataset[1])

```

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<div class="post-metadata">

**Author:** ![ToucheSir](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/touchesir/32/14411_2.png) [@ToucheSir](https://discourse.julialang.org/u/ToucheSir)\
**Post date:** [July 28, 2021, 4:15pm UTC](https://discourse.julialang.org/t/data-formatting-in-and-out-of-flux-ml-model/65383/2 "2021-07-28T16:15:29Z")

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Welcome! I think you’ll find that the APIs in Flux are a lot closer to what you’re familar with in Tensorflow (or more specifically, Keras). I would suggest working through the tutorials on [Flux – Tutorials](https://fluxml.ai/tutorials.html) and the intro docs at [Overview · Flux](https://fluxml.ai/Flux.jl/stable/models/overview/) to get a better idea of how the library works. I know for a fact those will be able to answer all of the comments you left in the code snippet above 🙂