How to use the model:
model = tensorflow.keras.models.load_model(filename+'.h5')
model.predict(scaled input data)

Input/Output
------------
Input and output variables:
['clw' 'cli' 'ta' 'rh' 'pa_z' 'pa_zz' 'rh_z' 'rh_zz' 'cl_area']
The (order of) input variables:
['clw' 'cli' 'ta' 'rh' 'pa_z' 'pa_zz' 'rh_z' 'rh_zz']

Scaling
-------
Standard Scaler mean values:
[ 2.25493498e-05  3.38180032e-06  2.57065512e+02  6.02512234e-01
 -7.51544542e+00 -1.06989723e-04 -4.76200071e-05 -1.32246548e-07]
Standard Scaler standard deviation:
[5.69702638e-05 1.01308124e-05 3.00533874e+01 3.32494615e-01
 3.53136052e+00 1.13215264e-02 2.58065098e-04 2.49552692e-06]
=> Apply this standard scaling to (only) the input data before processing.

Preprocessed data
-----------------
/home/b/b309170/my_work/icon-ml_data/cloud_cover_parameterization/neighborhood_based_SR_DYAMOND/cloud_cover_input_train_11.npy
/home/b/b309170/my_work/icon-ml_data/cloud_cover_parameterization/neighborhood_based_SR_DYAMOND/cloud_cover_input_valid_11.npy
/home/b/b309170/my_work/icon-ml_data/cloud_cover_parameterization/neighborhood_based_SR_DYAMOND/cloud_cover_output_train_11.npy
/home/b/b309170/my_work/icon-ml_data/cloud_cover_parameterization/neighborhood_based_SR_DYAMOND/cloud_cover_output_valid_11.npy
/home/b/b309170/my_work/icon-ml_data/cloud_cover_parameterization/neighborhood_based_SR_DYAMOND/cloud_cover_input_test_11.npy
/home/b/b309170/my_work/icon-ml_data/cloud_cover_parameterization/neighborhood_based_SR_DYAMOND/cloud_cover_output_test_11.npy

Model
-----
Results from the 2-th fold
Training epochs: 25
Weights restored from epoch: 24

Unbounded training loss: 44.8297
Unbounded validation loss: 44.0879
Bounded training loss: 39.8364
Bounded validation loss: 39.0962
