distargs=(), loc=0, scale=1, line='45')
plt.grid()
fig.savefig('logs/out/{}/real.png'.format(saved_file))
plt.close()
if it % 1000 == 0:
sequences_generator = []
for _ in range(int(1000 / BATCH_SIZE)):
sequences_gen = sess.run(fake_data, feed_dict={
Z: fake_batch[0], fake_seqlen: fake_batch[1]})
shape_gen = sequences_gen.shape
sequences_gen = np.reshape(
sequences_gen, (shape_gen[0], shape_gen[1]))
if D_DIFF:
sequences_gen = np.cumsum(sequences_gen, axis=1)
sequences_gen = sequence_filter(
sequences_gen, fake_batch[1]) # remove padding tokens
sequences_generator += sequences_gen
ts_gen, intensity_gen = get_intensity(sequences_generator, T, n_t)
deviation = np.linalg.norm(
intensity_gen - intensity_real) / np.linalg.norm(intensity_real)
print(
'Iter: {}; D loss: {:.4}; G_loss: {:.4}; data:{}; deviation: {}'.format(
it, D_loss_curr, G_loss_curr, DATA, deviation))
plt.plot(ts_real, intensity_real, label='real')
plt.plot(ts_gen, intensity_gen, label='generated')
plt.legend(loc=1)
plt.xlabel('time')
plt.ylabel('intensity')
plt.savefig('logs/out/{}/{}_{}.png'
'Iter: {}; Data: {}; D loss: {:.4}; neglik:{}; reg:{} Para:{}; w:{}'
.format(it, DATA, D_loss_curr, neglike, regular, para_max_,
decay_w_))
if np.max(np.abs(last_value - column_para_)) < 1e-4 and np.abs(
D_loss_curr -
last_loss) < 1: #np.max(np.abs(last_value-column_para_))<1e-2
stop_indicator = True
last_value = column_para_
last_loss = D_loss_curr
if it % 1000 == 0 and it > 10000:
sequences_generator = []
for _ in range(100):
sequences_gen = sess.run(fake_data)
sequences_generator.append(sequences_gen)
sequences_generator = sequence_filter(sequences_generator, None, T)
ts_gen, intensity_gen = get_intensity(sequences_generator, T, n_t)
deviation = np.linalg.norm(
intensity_gen - intensity_real) / np.linalg.norm(intensity_real)
plt.plot(ts_real, intensity_real, label='real')
plt.plot(ts_gen, intensity_gen, label='generated')
plt.legend(loc=1)
plt.xlabel('time')
plt.ylabel('intensity')
plt.savefig('out/{}/{}_{}.png'.format(saved_file,
str(it).zfill(3), deviation),
bbox_inches='tight')
plt.close()
if not REAL_DATA and DATA != "rmtpp":