What representation of data should I choose, pandas, numpy or tensors to train neural network model
It's hard to say for sure without knowing more about the size of your dataset, the architecture of your model, and the libraries you are using.
Changing from pandas.Dataframe to numpy.array is very unlikely to make your training faster.  Pandas dataframes are already backed by numpy arrays.
Depending on what neural network library you are using and what hardware you have available, changing from Dataframes to tensors may help.  Libraries like Tensorflow and PyTorch allow you to move computation to a GPU/TPU, which may speed up training.
The benefit of using tensor data structures from DL libraries is that they can be moved to a GPU/TPU.  They aren't inherently faster.  So if you don't have a GPU, or if you are using scikit-learn to train the model, then there's no point switching from pandas to tensors