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?