Get training hyperparameters from a trained keras model
Configuration - model.get_config()
Optimizer config - model.optimizer.get_config()
Training Config  model.history.params (this will be empty, if model is saved and reloaded)
Loss Fuction - model.loss
I am trying to figure out some of the hyperparamters used for training some old keras models I have.  They were saved as .h5 files.  When using model.summary(), I get the model architecture, but no additional metadata about the model.
When I open this .h5 file in notepad++, most of the file is not human readable, but there are bits that I can understand, for instance;

{"loss_weights": null, "metrics": ["accuracy"], "sample_weight_mode":
  null, "optimizer_config": {"config": {"decay": 0.0, "momentum":
  0.8999999761581421, "nesterov": false, "lr": 9.999999747378752e-05}, "class_name": "SGD"}, "loss": "binary_crossentropy"}

which is not present in the output printed by model.summary().
Is there a way to make these files human readable or to get a more expanded summary that includes version information and training parameters?