x = torch.from_numpy(x).reshape((1, 3, 32, 32)).float()
with torch.no_grad():
x = net(x)
return entropy_loss(x, torch.zeros(1, dtype=torch.long))
fig = plt.figure(figsize=(10, 10))
ax = fig.add_subplot(111, projection='3d')
sphere = Sphere()
# f_points = sphere.fibonnaci_points(10000).swapaxes(0, 1)
# tf_points = transformer.transform(f_points, base_point)
#
# print(tf_points[:, 0].min(), tf_points[:, 0].max())
# print(tf_points[:, 1].min(), tf_points[:, 1].max())
# print(tf_points[:, 2].min(), tf_points[:, 2].max())
sphere.plot_heatmap(ax=ax, n_points=10000, scalar_function=loss_f)
correct_points = points[labels == 0]
correct_labels = labels[labels == 0]
wrong_points = points[labels != 0]
wrong_labels = labels[labels != 0]
plot_points = np.concatenate((correct_points[:100], wrong_points[:100]))
plot_labels = np.concatenate((correct_labels[:100], wrong_labels[:100]))
visualization.plot(plot_points,
ax=ax,
space='S2',
c=plot_labels == 0,
s=80,
alpha=0.5)
plt.show()
