How to implement multivariate time series forecasting in keras?
Congratulations, you have learned how to implement multivariate multi-step time series forecasting using TF 2.0 / Keras. This is my first attempt at writing a blog. So please share your opinion in the comments section below.
Is it possible to train keras on multiple GPUs?
However, one of my biggest hangups with Keras is that it can be a pain to perform multi-GPU training. Between the boilerplate code and configuring TensorFlow it can be a bit of a process… …but not anymore. With the latest commit and release of Keras (v2.0.9) it’s now extremely easy to train deep neural networks using multiple GPUs.
How does the time distributed densely work in keras?
The output received from the decoder with respect to each time step is mixed. The time distributed densely will apply a fully connected dense layer on each time step and separates the output for each timestep. The time distributed densely is a wrapper that allows applying a layer to every temporal slice of an input.
Which is the best library for multi GPU training?
TensorFlow was a possibility, but it could take a lot of boilerplate code and tweaking to get your network to train using multiple GPUs. I preferred using the mxnet backend (or even the mxnet library outright) to Keras when performing multi-GPU training, but that introduced even more configurations to handle.
Can you keep the previous state in keras?
States between different batches are not usually kept or relevant. stateful option in keras isn’t something I would recommend unless you are sure about what you are doing. stateful retains the state between batches but doesn’t backprop between batches. So it uses the previous state but it can’t learn to make the previous state useful.
Can a stepwise forecast be used for longer sequences?
The stepwise forecast approach can be used for short sequences but because any biases are compounded using this approach, it is not good for longer sequences.