logits=D_logit_fake, labels=tf.ones_like(D_logit_fake)))
D_solver = tf.train.GradientDescentOptimizer(learning_rate=lr).minimize(
D_loss, var_list=discriminator_vars)
G_solver = tf.train.AdamOptimizer().minimize(G_loss,
var_list=generator_vars)
sess = tf.Session()
sess.run(tf.global_variables_initializer())
# plot the ouput from the same seed
vis_z = sample_Z(batch_size, seq_length, latent_dim, use_time=use_time)
X_mb_vis, Y_mb_vis = get_batch(train_seqs, train_targets, batch_size, 0)
vis_sample = sess.run(G_sample, feed_dict={Z: vis_z, CG: Y_mb_vis})
plotting.vis_eICU_patients_downsampled(vis_sample,
seq_length,
identifier=identifier,
idx=0)
# visualise some real samples
vis_real = np.float32(
vali_seqs[np.random.choice(len(vali_seqs), size=batch_size), :, :])
plotting.vis_eICU_patients_downsampled(vis_real,
seq_length,
identifier=identifier + '_real',
idx=0)
trace = open('./experiments/traces/' + identifier + '.trace.txt', 'w')
trace.write('epoch D_loss G_loss time\n')
print('epoch\tD_loss\tG_loss\ttime\n')
t0 = time.time()
samps = vis_real
if multivariate_mnist:
plotting.save_mnist_plot_sample(samps.reshape(-1, seq_length**2, 1),
0,
identifier + '_real',
n_samples=6,
labels=labs)
else:
plotting.save_mnist_plot_sample(samps,
0,
identifier + '_real',
n_samples=6,
labels=labs)
elif 'eICU' in data:
plotting.vis_eICU_patients_downsampled(vis_real,
resample_rate_in_min,
identifier=identifier + '_real',
idx=0)
else:
plotting.save_plot_sample(vis_real,
0,
identifier + '_real',
n_samples=6,
num_epochs=num_epochs)
trace = open('./experiments/traces/' + identifier + '.trace.txt', 'w')
trace.write('epoch time D_loss G_loss mmd2 that ll real_ll\n')
# --- train --- #
train_vars = [
'batch_size', 'D_rounds', 'G_rounds', 'use_time', 'seq_length',
'latent_dim', 'num_generated_features', 'cond_dim', 'max_val', 'WGAN_clip',