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
error = relative_difference(spectra_predicted[index], energies_val[index])
error2 = relative_difference(spectra_predicted[index2], energies_val[index2])
print("LOSS", error)
print("LOSS2", error2)
print("Best index = {}\nWorst index = {}\nMedian index = {}".format(
import torch
import pkg_resources
pkg_resources.require("torch==1.0.0")
import load_data
import helper_functions
import spectroscopy_cnn_model
validation_loader = load_data.load_experiment_data('spectra_normalized.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, rmse_rmse, rmse_mae, rmse_r2 = helper_functions.get_model_error(
validation_loader, model, normalized=True)
model.load_state_dict(torch.load('results/model_exp41.pth',
map_location='cpu'))
model.eval()
smooth_accuracy, smooth_rmse, smooth_mae, smooth_r2 = helper_functions.get_model_error(
validation_loader, model, normalized=True)
model.load_state_dict(torch.load('results/model_exp42.pth',
map_location='cpu'))
model.eval()
logcosh_accuracy, logcosh_rmse, logcosh_mae, logcosh_r2 = helper_functions.get_model_error(
validation_loader, model, normalized=True)
model.load_state_dict(torch.load('results/model_exp39.pth',
map_location='cpu'))