def update_disc_plot(ax: plt.Axes, d_x, d_g_z, update_interval):
clear_line(ax, 'dx')
clear_line(ax, 'dgz')
x = np.arange(1, len(d_x) + 1)
ax.plot(x, d_x, color='#308862', label='D(x)', gid='dx')
ax.plot(x, d_g_z, color='#B23F62', label='D(G(z))', gid='dgz', alpha=0.9)
ax.legend(loc='upper right', framealpha=0.75, bbox_to_anchor=(1.008, 1.136), ncol=5)
ax.set_xlim(left=1, right=len(x) + 0.01)
ax.set_ylim(0, 1.0)
ax.set_yticks(np.arange(0, 1, 0.1))
ticks = ax.get_xticks()
ax.set_xticklabels([f'{t:.0f}' for t in ticks * update_interval])
def update_data_plot(ax, D, fake_data, device):
# draw decision discriminator boundary
clear_line(ax, 'decision')
plot_decision_boundary(ax, D, device)
#
clear_patch(ax, 'g_hist')
ax.hist(fake_data,
gid='g_hist',
bins=100,
density=True,
histtype='stepfilled',
label='generated',
facecolor='moccasin',
edgecolor='sandybrown',
linewidth=2,
alpha=0.85)
ax.legend(loc='upper right', framealpha=0.75)
def update_loss_plot(ax: plt.Axes, d_loss, d_fake_loss, g_loss, update_interval, separate=False):
clear_line(ax, 'd_loss')
clear_line(ax, 'g_loss')
x = np.arange(1, len(d_loss) + 1)
if separate:
ax.plot(x, np.add(d_loss, d_fake_loss), color='dodgerblue', label='D Loss', gid='d_loss')
clear_line(ax, 'd_real_loss')
ax.plot(x, d_loss, color='lightseagreen', label='D Loss(Real)', gid='d_real_loss')
clear_line(ax, 'd_fake_loss')
ax.plot(x, d_fake_loss, color='mediumpurple', label='D Loss(Fake)', gid='d_fake_loss')
else:
ax.plot(x, d_loss, color='dodgerblue', label='D Loss', gid='d_loss')
ax.plot(x, g_loss, color='coral', label='G Loss', gid='g_loss', alpha=0.9)
ax.legend(loc='upper right', framealpha=0.75, bbox_to_anchor=(1.008, 1.136), ncol=5)
ax.set_xlim(left=1, right=len(x) + 0.01)
ticks = ax.get_xticks()
ax.set_xticklabels([f'{t:.0f}' for t in ticks * update_interval])