# Shape of data for sequence learning in Flux.jl?

**URL:** https://discourse.julialang.org/t/shape-of-data-for-sequence-learning-in-flux-jl/20811
**Category:** Machine Learning
**Tags:** first-steps
**Created:** [February 15, 2019, 6:48am UTC](https://discourse.julialang.org/t/shape-of-data-for-sequence-learning-in-flux-jl/20811 "2019-02-15T06:48:22Z")
**Posts on this page:** 2
**Page:** 1

<div class="post-metadata">

### Author: ![clemej](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/clemej/32/7098_2.png) [@clemej](https://discourse.julialang.org/u/clemej)
#### Post date: [February 15, 2019, 6:48am UTC](https://discourse.julialang.org/t/shape-of-data-for-sequence-learning-in-flux-jl/20811/1 "2019-02-15T06:48:22Z")

</div>

I’m teaching myself Julia and porting some Python/Keras code over to FluxML, and I’m having trouble shaping the data in Julia.

The python keras code is (basically):

```
model = Sequential()
model.add(LSTM(9, input_shape=(128,1), return_sequences=True))
model.add(TimeDistributed(Dense(8)))
model.compile(loss='mse', optimizer='nadam')

model.fit(X, Y, epochs=256, .....)

```

Where X is a sequence of 128 single values (input to the network one at a time), and the output is the resulting 8 outputs at each of the 128 values. So in numpy terms (for 1024 training samples)

```
X.shape
(1024, 128, 1)
Y.shape
(1024, 128, 8)

```

Trying to replicate this with Julia, I have the data set up in the same way (using 64 training examples and a seqlen of 32 instead of 128) using nested arrays (is this my issue?), but the following code:

```
m = Chain(
          Dense(1, 9, NNlib.sigmoid),
          LSTM(9, 9),
          Dense(9, 8))

mynewloss(xs, ys) = Flux.mse.(m(xs), ys)
opt = Flux.ADAM(params(m))
evalcb = () -> @show loss(x_validate, y_validate)

Flux.train!(mynewloss, zip(x_train, y_train), opt, cb = Flux.throttle(evalcb, 30))

```

produces this error:

```
DimensionMismatch("second dimension of A, 1, does not match length of x, 32")

```

more info:

```
summary(x_train)
"64-element Array{Array{Float32,1},1}"
summary(x_train[1])
"32-element Array{Float32,1}"
summary(x_train[1][1])
"Float32"

summary(y_train)
"64-element Array{Array{Float32,2},1}"
summary(y_train[1])
"32×8 Array{Float32,2}"

```

Any advice?

---

<div class="post-metadata">

### Author: ![jling](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/jling/32/212909_2.png) [@jling](https://discourse.julialang.org/u/jling)
#### Post date: [March 26, 2019, 5:43pm UTC](https://discourse.julialang.org/t/shape-of-data-for-sequence-learning-in-flux-jl/20811/2 "2019-03-26T17:43:12Z")

</div>

> [@clemej](#):
>
> Dense(1, 9, NNlib.sigmoid)

this means each train sample has 1 number as input, so you’re training against scalar input