# DATA. MNIST batches are fed at training time.
mnist = input_data.read_data_sets(DATA_DIR, one_hot=True)
x_ph = tf.placeholder(tf.float32, [M, 784])
z_ph = tf.placeholder(tf.float32, [M, d])
# MODEL
with tf.variable_scope("Gen"):
xf = gen_data(z_ph, hidden_units)
zf = gen_latent(x_ph, hidden_units)
# INFERENCE:
optimizer = tf.train.AdamOptimizer()
optimizer_d = tf.train.AdamOptimizer()
inference = ed.BiGANInference(
latent_vars={zf: z_ph}, data={xf: x_ph},
discriminator=discriminative_network)
inference.initialize(
optimizer=optimizer, optimizer_d=optimizer_d, n_iter=100000, n_print=3000)
sess = ed.get_session()
init_op = tf.global_variables_initializer()
sess.run(init_op)
idx = np.random.randint(M, size=16)
i = 0
for t in range(inference.n_iter):
if t % inference.n_print == 1:
samples = sess.run(xf, feed_dict={z_ph: z_batch})
def main(_):
ed.set_seed(42)
# DATA. MNIST batches are fed at training time.
(x_train, _), (x_test, _) = mnist(FLAGS.data_dir)
x_train_generator = generator(x_train, FLAGS.M)
x_ph = tf.placeholder(tf.float32, [FLAGS.M, 784])
z_ph = tf.placeholder(tf.float32, [FLAGS.M, FLAGS.d])
# MODEL
with tf.variable_scope("Gen"):
xf = gen_data(z_ph, FLAGS.hidden_units)
zf = gen_latent(x_ph, FLAGS.hidden_units)
# INFERENCE:
optimizer = tf.train.AdamOptimizer()
optimizer_d = tf.train.AdamOptimizer()
inference = ed.BiGANInference(latent_vars={zf: z_ph},
data={xf: x_ph},
discriminator=discriminative_network)
inference.initialize(optimizer=optimizer,
optimizer_d=optimizer_d,
n_iter=100000,
n_print=3000)
sess = ed.get_session()
init_op = tf.global_variables_initializer()
sess.run(init_op)
idx = np.random.randint(FLAGS.M, size=16)
i = 0
for t in range(inference.n_iter):
if t % inference.n_print == 1:
samples = sess.run(xf, feed_dict={z_ph: z_batch})
samples = samples[idx, ]
fig = plot(samples)
plt.savefig(os.path.join(FLAGS.out_dir, '{}{}.png').format(
'Generated',
str(i).zfill(3)),
bbox_inches='tight')
plt.close(fig)
fig = plot(x_batch[idx, ])
plt.savefig(os.path.join(FLAGS.out_dir, '{}{}.png').format(
'Base',
str(i).zfill(3)),
bbox_inches='tight')
plt.close(fig)
zsam = sess.run(zf, feed_dict={x_ph: x_batch})
reconstructions = sess.run(xf, feed_dict={z_ph: zsam})
reconstructions = reconstructions[idx, ]
fig = plot(reconstructions)
plt.savefig(os.path.join(FLAGS.out_dir, '{}{}.png').format(
'Reconstruct',
str(i).zfill(3)),
bbox_inches='tight')
plt.close(fig)
i += 1
x_batch = next(x_train_generator)
z_batch = np.random.normal(0, 1, [FLAGS.M, FLAGS.d])
info_dict = inference.update(feed_dict={x_ph: x_batch, z_ph: z_batch})
inference.print_progress(info_dict)