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' 'cl_area']
The (order of) input variables:
['clw' 'cli' 'ta' 'rh']

Scaling
-------
Standard Scaler mean values:
[2.25493498e-05 3.38180032e-06 2.57065512e+02 6.02512234e-01]
Standard Scaler standard deviation:
[5.69702638e-05 1.01308124e-05 3.00533874e+01 3.32494615e-01]
=> 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: 23

Unbounded training loss: 94.0126
Unbounded validation loss: 93.4670
Bounded training loss: 90.1142
Bounded validation loss: 89.5694
