directory fix for using model not created with create_mlp.

import matplotlib.pyplot as plt

from EMRI_DET.utilities import norm, norm_inputs, unnorm_inputs, unnorm, get_script_path

from sys import stdout

from pathlib import Path

def model_train_test(data, model, device, n_epochs, n_batches, loss_function, learning_rate, verbose=False, return_losses=False):

    xtrain, ytrain, xtest, ytest = data

    model.to(device)

    name = model.name

    path = get_script_path()

    Path(get_script_path()+f'/../models/{name}/').mkdir(parents=True, exist_ok=True)

    np.save(path+'/../models/'+name+'/xdata_mean_std.npy',np.array([xtrain.mean(axis=0), xtrain.std(axis=0)]))

    np.save(path+'/../models/'+name+'/ydata_mean_std.npy',np.array([ytrain.mean(), ytrain.std()]))

if __name__ == '__main__':

    device = "cuda:0"

    fp = '../schwarz_negY/{}'

    fp = '../schwarz_data/{}'

    #['logM', 'logq', 'a', 'p0', 'e', 'Y0', 'thetaS', 'phiS', 'thetaK', 't', 'SNR']

    train_inds = [0,1,2,4,5,6,7,8,9]

    test_inds = [10]

    train_inds = [0,1,2,3,4,5,6,7,8]#[0,1,2,4,5,6,7,8,9]

    test_inds = [9]

    traindata = pd.read_csv(fp.format('samp_dataframe.csv'))

    xtrain = traindata.iloc[:,train_inds].to_numpy()

    out_features = 1

    layers = 4

    neurons = [256,128,64,32]

    activation = nn.Tanh

    activation = nn.SiLU

    model = create_mlp(input_features=in_features,output_features=out_features,neurons=neurons,layers=layers,activation=activation,device=device, model_name='m1')

    data = [xtrain, ytrain, xtest, ytest]