I would like to understand how an RNN, specifically an LSTM is working with multiple input dimensions using Keras and Tensorflow. I mean the input shape is (batch\_size, timesteps, input\_dim) where input\_dim > 1.  

I think the below images illustrate quite well the concept of LSTM if the input\_dim = 1.  

Does this mean if input\_dim > 1 then x is not a single value anymore but an array? But if it's like this then the weights are also become arrays, same shape as x + the context?

[![LSTM structure](https://i.stack.imgur.com/YW2bI.png)](https://i.stack.imgur.com/YW2bI.png)

[![enter image description here](https://i.stack.imgur.com/2x8NZ.png)](https://i.stack.imgur.com/2x8NZ.png)