# # reset ADAM variables
# sess.run(tf.initialize_variables(sigma_opt_vars))
# sigma_iter = 0
# that_change = sigma_opt_thresh * 2
# old_that = 0
# while that_change > sigma_opt_thresh and sigma_iter < sigma_opt_iter:
# new_sigma, that_np, _ = sess.run([sigma, that, sigma_solver],
# feed_dict={eval_real_PH: eval_eval_real, eval_sample_PH: eval_eval_sample})
# that_change = np.abs(that_np - old_that)
# old_that = that_np
# sigma_iter += 1
# opt_sigma = sess.run(sigma)
# try:
# mmd2, that_np = sess.run(mix_rbf_mmd2_and_ratio(eval_test_real, eval_test_sample, biased=False, sigmas=sigma))
# except ValueError:
# mmd2 = 'NA'
# that = 'NA'
#
# MMD[epoch, ] = mmd2
# -- save model parameters -- #
model.dump_parameters(sub_id + '_' + str(seq_length) + '_' + str(epoch),
sess)
np.save('./experiments/plots/gs/' + identifier + '_' + 'MMD.npy', MMD)
end = time() - begin
print('Training terminated | Training time=%d s' % (end))
print("Training terminated | training time = %ds " % (time() - begin))
# print(new_sigma,that_np)
that_change = np.abs(that_np - old_that)
old_that = that_np
sigma_iter += 1
opt_sigma = sess.run(sigma)
mmd2, that_np = sess.run(
mix_rbf_mmd2_and_ratio(eval_test_real,
eval_test_sample,
biased=False,
sigmas=sigma))
## save parameters
if mmd2 < best_mmd2_so_far and epoch > 10:
best_epoch = epoch
best_mmd2_so_far = mmd2
model.dump_parameters(identifier + '_' + str(epoch), sess)
pdf_sample = 'NA'
pdf_real = 'NA'
else:
# report nothing this epoch
mmd2 = 'NA'
that = 'NA'
pdf_sample = 'NA'
pdf_real = 'NA'
t = time() - t0
try:
print('%d\t%.2f\t%.4f\t%.4f\t%.5f\t' %
(epoch, t, D_loss_curr, G_loss_curr, mmd2))
except TypeError: # pdf are missing (format as strings)
# pdf_real = 'NA'
#
# MMD[epoch, ] = mmd2
#
# ## print
#
# t = time() - t0
# print('epoch\ttime\tD_loss\tG_loss\tmmd2\tthat\tpdf_sample\tpdf_real')
# try:
# print('%d\t%.2f\t%.4f\t%.4f\t%.5f\t%.0f\t%.2f\t%.2f' % (
# epoch, t, D_loss_curr, G_loss_curr, mmd2, that_np, pdf_sample, pdf_real))
# except TypeError: # pdf are missing (format as strings)
# print('%d\t%.2f\t%.4f\t%.4f\t%s\t%s\t %s\t %s' % (
# epoch, t, D_loss_curr, G_loss_curr, mmd2, that_np, pdf_sample, pdf_real))
#
#-- save model parameters -- #
model.dump_parameters(
settings['identifier'] + '_' + str(settings['seq_length']) + '_' +
str(epoch), sess)
# model_parameters = dict()
# for v in tf.trainable_variables():
# model_parameters[v.name] = sess.run(v)
# print('Saved {} parameters'.format(len(model_parameters)))
# np.save('./experiments/plots/gs/' + settings['identifier'] + '_' + settings['seq_length'] + '_' + 'MMD.npy', MMD)
end = time() - begin
# print('Training terminated | Training time=%ds' %(end) )
print("Training terminated | training time = %ds " % (time() - begin))
