# Question about Flux.jl data inputs

**URL:** <https://discourse.julialang.org/t/question-about-flux-jl-data-inputs/19630>\
**Category:** Machine Learning\
**Created:** [January 14, 2019, 5:07pm UTC](https://discourse.julialang.org/t/question-about-flux-jl-data-inputs/19630 "2019-01-14T17:07:44Z")\
**Posts on this page:** 2\
**Page:** 1

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**Author:** ![zjroth](https://avatars.discourse-cdn.com/v4/letter/z/74df32/32.png) [@zjroth](https://discourse.julialang.org/u/zjroth)\
**Post date:** [January 14, 2019, 5:07pm UTC](https://discourse.julialang.org/t/question-about-flux-jl-data-inputs/19630/1 "2019-01-14T17:07:44Z")

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I’m playing around with Flux.jl, and I came across an error that I have some questions about.

With a `Dense` layer, I can provide a matrix input in which each column represents a single input value. Additionally, **the number of columns can change in subsequent calls**. For example,

```julia
m = Dense(3, 2)
m(rand(3, 10))
m(rand(3))
m(rand(3, 2))

```

is perfectly fine.

Similarly, with `RNN` and `LSTM` (and presumably with other recurrent networks), I can provide a matrix input in which each column represents a single input value. However, **the number of columns must remain the same in subsequent calls**. (However, vectors seem to be treated differently.) For example,

```julia
m = RNN(3, 2)
m(rand(3, 10))
m(rand(3))
m(rand(3, 2)) # ERROR: DimensionMismatch

```

fails with a `DimensionMismatch` error.

The error originally occurred because I was using training and test sets in matrix form with different numbers of columns. It seems that the issue can be sidestepped with something like the following

```julia
columns(A::Matrix) = (@view(A[:, j]) for j = 1 : size(A, 2))
m.(columns(rand(3, 10))
m.(columns(rand(3, 2))

```

if the data is in matrix form.

It is unclear to me why the error occurs with recurrent networks. Should I not be using matrices as I described? Should there not be an error with the recurrent networks? Is the code working with the `Dense` network as an artifact of the implementation? Is there a recommended shape for the data? For instance, other deep-learning frameworks seem to insist that data be coerced into a tensor (e.g., with specific dimensions corresponding to things such as batches and timesteps).

I apologize if I’ve missed something simple somewhere, but I’ve been getting some cryptic errors, and this seems to be the root cause of one of them.

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<div class="post-metadata">

**Author:** ![c42f](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/c42f/32/52842_2.png) [@c42f](https://discourse.julialang.org/u/c42f)\
**Post date:** [January 15, 2019, 1:27pm UTC](https://discourse.julialang.org/t/question-about-flux-jl-data-inputs/19630/2 "2019-01-15T13:27:01Z")

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I think the problem is that you are trying to put various batch sizes into the RNN layer one after the other (ie, batches of size 10, 1 and 2), but the `Recur` object returned from `RNN` stores the hidden state (including number of batches used) so the batch size must match all previous batches.

The reason it “works” for a batch size of 1 (ie, vector input) is probably an unhappy accident of the implementation using broadcast.

If you want to try your model with a different batch size, you’ll have to reset the hidden state with `Flux.reset!(m)`.