#own file imports
import helper_functions
import spectroscopy_cnn_model
import load_data
import numpy as np
normalize = sys.argv[1]
output_spectra_length = 300
spectra_file = 'spectra.npz'
experiment_model = 'exp42'
train_loader, test_loader, validation_loader = load_data.load_experiment_data(
spectra_file, 'all', 1, 1, 1)
model = spectroscopy_cnn_model.create_network(output_spectra_length)
model.load_state_dict(
torch.load('results/model_' + experiment_model + '.pth',
map_location='cpu'))
model.eval()
predicted_spectra_training = []
predicted_spectra_testing = []
predicted_spectra_validation = []
with torch.no_grad():
print("Predictions training data")
for data, target in train_loader:
output = model(data)
coulomb_train, coulomb_test, energies_train, energies_test = train_test_split(
coulomb, energies, test_size=1.0 - train_split, random_state=0)
coulomb_test, coulomb_val, energies_test, energies_val = train_test_split(
coulomb_test, energies_test, test_size=val_test_split, random_state=0)
def relative_difference(prediction, label):
dE = 30 / len(prediction) #how many eV's one dE is
numerator = np.sum(dE * np.power((label - prediction), 2))
denominator = np.sum(dE * label)
return 1 - np.sqrt(numerator) / denominator
validation_loader = load_data.load_experiment_data('spectra.npz', 'validation')
model = spectroscopy_cnn_model.create_network(300)
model.load_state_dict(torch.load('results/model_exp17.pth',
map_location='cpu'))
model.eval()
rmse_accuracy = helper_functions.get_model_error(validation_loader, model,
True)
best_prediction = np.argmin(rmse_accuracy)
worst_prediction = np.argmax(rmse_accuracy)
median_prediction = rmse_accuracy.index(
np.percentile(rmse_accuracy, 50, interpolation='nearest'))
index = 1836
index2 = 5670
