def main():
tf.set_random_seed(1234)
np.random.seed(1234)
# Load CIFAR
data_path = os.path.join(conf.data_dir, "cifar10",
"cifar-10-python.tar.gz")
x_train, t_train, x_test, t_test = dataset.load_cifar10(data_path,
normalize=True,
one_hot=True)
# Define model parameters
z_dim = 40
# Build the computation graph
is_training = tf.placeholder(tf.bool, shape=[], name="is_training")
x = tf.placeholder(tf.float32, shape=[None, 32, 32, 3], name="x")
optimizer = tf.train.AdamOptimizer(learning_rate=0.0002, beta1=0.5)
def build_tower_graph(x, id_):
tower_x = x[id_ * tf.shape(x)[0] // FLAGS.num_gpus:(id_ + 1) *
tf.shape(x)[0] // FLAGS.num_gpus]
n = tf.shape(tower_x)[0]
x_gen = generator(n, z_dim, is_training)
x_class_logits = discriminator(tower_x, is_training)
x_gen_class_logits = discriminator(x_gen, is_training)
gen_loss = tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(
labels=tf.ones_like(x_gen_class_logits),
logits=x_gen_class_logits))
gen_var_list = tf.trainable_variables(scope="gen")
gen_grads = optimizer.compute_gradients(gen_loss,
var_list=gen_var_list)
disc_loss = (tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(
labels=tf.ones_like(x_class_logits), logits=x_class_logits)) +
tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(
labels=tf.zeros_like(x_gen_class_logits),
logits=x_gen_class_logits))) / 2.
disc_var_list = tf.trainable_variables(scope="disc")
disc_grads = optimizer.compute_gradients(disc_loss,
var_list=disc_var_list)
grads = disc_grads + gen_grads
return grads, gen_loss, disc_loss
tower_losses = []
tower_grads = []
for i in range(FLAGS.num_gpus):
with tf.device("/gpu:%d" % i):
with tf.name_scope("tower_%d" % i):
grads, gen_loss, disc_loss = build_tower_graph(x, i)
tower_losses.append([gen_loss, disc_loss])
tower_grads.append(grads)
gen_loss, disc_loss = multi_gpu.average_losses(tower_losses)
grads = multi_gpu.average_gradients(tower_grads)
update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
with tf.control_dependencies(update_ops):
infer_op = optimizer.apply_gradients(grads)
# Generate images
eval_x_gen = generator(100, z_dim, False)
# Define training/evaluation parameters
epochs = 1000
batch_size = 32 * FLAGS.num_gpus
iters = x_train.shape[0] // batch_size
print_freq = 100
save_freq = 100
# Run the inference
with multi_gpu.create_session() as sess:
sess.run(tf.global_variables_initializer())
for epoch in range(1, epochs + 1):
np.random.shuffle(x_train)
gen_losses, disc_losses = [], []
time_train = -time.time()
for t in range(iters):
iter = t + 1
x_batch = x_train[t * batch_size:(t + 1) * batch_size]
_, g_loss, d_loss = sess.run([infer_op, gen_loss, disc_loss],
feed_dict={
x: x_batch,
is_training: True
})
gen_losses.append(g_loss)
disc_losses.append(d_loss)
if iter % print_freq == 0:
print("Epoch={} Iter={} ({:.3f}s/iter): "
"Gen loss = {} Disc loss = {}".format(
epoch, iter,
(time.time() + time_train) / print_freq,
np.mean(gen_losses), np.mean(disc_losses)))
gen_losses = []
disc_losses = []
if iter % save_freq == 0:
images = sess.run(eval_x_gen)
name = "results/dcgan/dcgan.epoch.{}.iter.{}.png".format(
epoch, iter)
save_image_collections(images, name, scale_each=True)
if iter % print_freq == 0:
time_train = -time.time()
var_list=disc_var_list)
gen_grads = optimizer.compute_gradients(gen_loss,
var_list=gen_var_list)
grads = disc_grads + gen_grads
return grads, gen_loss, disc_loss
tower_losses = []
tower_grads = []
for i in range(FLAGS.num_gpus):
with tf.device('/gpu:%d' % i):
with tf.name_scope('tower_%d' % i):
grads, gen_loss, disc_loss = build_tower_graph(x, i)
tower_losses.append([gen_loss, disc_loss])
tower_grads.append(grads)
gen_loss, disc_loss = multi_gpu.average_losses(tower_losses)
grads = multi_gpu.average_gradients(tower_grads)
infer = optimizer.apply_gradients(grads)
_, eval_x_gen = generator(None, gen_size, n_z, is_training)
gen_var_list = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES,
scope='generator')
saver = tf.train.Saver(max_to_keep=10, var_list=gen_var_list)
# Run the inference
with multi_gpu.create_session() as sess:
sess.run(tf.global_variables_initializer())
for epoch in range(1, epochs + 1):
if epoch % anneal_lr_freq == 0:
learning_rate *= anneal_lr_rate
np.random.shuffle(x_train)
gen_losses, disc_losses = [], []
def main():
tf.set_random_seed(1234)
np.random.seed(1234)
# Load MINST
data_path = os.path.join(conf.data_dir, 'mnist.pkl.gz')
x_train, t_train, x_valid, t_valid, x_test, t_test = \
dataset.load_mnist_realval(data_path)
n_xl = 28
n_channels = 1
x_train = np.vstack([x_train, x_valid]).astype(np.float32).reshape(
(-1, n_xl, n_xl, n_channels))
# Define model parameters
n_z = 40
# Define training/evaluation parameters
epochs = 1000
batch_size = 64 * FLAGS.num_gpus
gen_size = 100
iters = x_train.shape[0] // batch_size
print_freq = 100
save_freq = 100
# Build the computation graph
is_training = tf.placeholder(tf.bool, shape=[], name='is_training')
x = tf.placeholder(tf.float32,
shape=(None, n_xl, n_xl, n_channels),
name='x')
optimizer = tf.train.RMSPropOptimizer(learning_rate=0.0002, decay=0.5)
def build_tower_graph(x, id_):
tower_x = x[id_ * tf.shape(x)[0] // FLAGS.num_gpus:(id_ + 1) *
tf.shape(x)[0] // FLAGS.num_gpus]
n = tf.shape(tower_x)[0]
gen, x_gen = generator(None, n, n_z, is_training)
x_critic = discriminator(tower_x, is_training)
x_gen_critic = discriminator(x_gen, is_training)
gen_var_list = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES,
scope='generator')
disc_var_list = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES,
scope='discriminator')
disc_loss = -tf.reduce_mean(x_critic - x_gen_critic)
gen_loss = -tf.reduce_mean(x_gen_critic)
disc_grads = optimizer.compute_gradients(disc_loss,
var_list=disc_var_list)
gen_grads = optimizer.compute_gradients(gen_loss,
var_list=gen_var_list)
grads = disc_grads + gen_grads
return grads, gen_loss, disc_loss
tower_losses = []
tower_grads = []
for i in range(FLAGS.num_gpus):
with tf.device('/gpu:%d' % i):
with tf.name_scope('tower_%d' % i):
grads, gen_loss, disc_loss = build_tower_graph(x, i)
tower_losses.append([gen_loss, disc_loss])
tower_grads.append(grads)
gen_loss, disc_loss = multi_gpu.average_losses(tower_losses)
w_distance = -disc_loss
grads = multi_gpu.average_gradients(tower_grads)
update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
with tf.control_dependencies(update_ops):
infer_op = optimizer.apply_gradients(grads)
# Clip weights of the critic to ensure 1-Lipschitz
disc_var_list = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES,
scope='discriminator')
with tf.control_dependencies([infer_op]):
clip_op = tf.group(*[
var.assign(tf.clip_by_value(var, -0.01, 0.01))
for var in disc_var_list
])
# Generate images
_, eval_x_gen = generator(None, gen_size, n_z, False)
# Run the inference
with multi_gpu.create_session() as sess:
sess.run(tf.global_variables_initializer())
for epoch in range(1, epochs + 1):
np.random.shuffle(x_train)
w_losses = []
time_train = -time.time()
for t in range(iters):
iter = t + 1
x_batch = x_train[t * batch_size:(t + 1) * batch_size]
_, _, w_loss = sess.run([infer_op, clip_op, w_distance],
feed_dict={
x: x_batch,
is_training: True
})
w_losses.append(w_loss)
if iter % print_freq == 0:
print('Epoch={} Iter={} ({:.3f}s/iter): '
'wasserstein distance = {}'.format(
epoch, iter,
(time.time() + time_train) / print_freq,
np.mean(w_losses)))
w_losses = []
if iter % save_freq == 0:
images = sess.run(eval_x_gen)
name = "results/wgan/wgan.epoch.{}.iter.{}.png".format(
epoch, iter)
save_image_collections(images, name, scale_each=True)
if iter % print_freq == 0:
time_train = -time.time()