def main():
start_time = time.time() # Clocking start
# MNIST DataSet load
mnist = DataSet().data
# GPU configure
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
with tf.Session(config=config) as s:
# ACGAN Model
model = acgan.ACGAN(s)
# Initializing
s.run(tf.global_variables_initializer())
sample_x, sample_y = mnist.train.next_batch(model.sample_num)
sample_x = np.reshape(sample_x, [-1] + model.image_shape[1:])
sample_z = np.random.uniform(
-1., 1., [model.sample_num, model.z_dim]).astype(np.float32)
d_overpowered = False
for step in range(train_step['global_step']):
batch_x, batch_y = mnist.train.next_batch(model.batch_size)
batch_x = np.reshape(batch_x, [-1] + model.image_shape[1:])
batch_z = np.random.uniform(
-1., 1., [model.batch_size, model.z_dim]).astype(np.float32)
# Update D network
if not d_overpowered:
_, d_loss = s.run([model.d_op, model.d_loss],
feed_dict={
model.x: batch_x,
model.y: batch_y,
model.z: batch_z,
})
# Update G/C network
_, g_loss, _, c_loss = s.run(
[model.g_op, model.g_loss, model.c_op, model.c_loss],
feed_dict={
model.x: batch_x,
model.y: batch_y,
model.z: batch_z,
})
d_overpowered = d_loss < g_loss / 2
if step % train_step['logging_interval'] == 0:
batch_x, batch_y = mnist.test.next_batch(model.batch_size)
batch_x = np.reshape(batch_x, [-1] + model.image_shape[1:])
batch_z = np.random.uniform(
-1., 1.,
[model.batch_size, model.z_dim]).astype(np.float32)
d_loss, g_loss, c_loss, summary = s.run(
[model.d_loss, model.g_loss, model.c_loss, model.merged],
feed_dict={
model.x: batch_x,
model.y: batch_y,
model.z: batch_z,
})
d_overpowered = d_loss < g_loss / 2
# Print loss
print("[+] Step %08d => " % step,
" D loss : {:.8f}".format(d_loss),
" G loss : {:.8f}".format(g_loss),
" C loss : {:.8f}".format(c_loss))
# Training G model with sample image and noise
samples = s.run(model.g,
feed_dict={
model.x: sample_x,
model.y: sample_y,
model.z: sample_z,
})
# Summary saver
model.writer.add_summary(summary, step)
# Export image generated by model G
sample_image_height = model.sample_size
sample_image_width = model.sample_size
sample_dir = results['output'] + 'train_{:08d}.png'.format(
step)
# Generated image save
iu.save_images(samples,
size=[sample_image_height, sample_image_width],
image_path=sample_dir)
# Model save
model.saver.save(s, results['model'], global_step=step)
end_time = time.time() - start_time # Clocking end
# Elapsed time
print("[+] Elapsed time {:.8f}s".format(end_time))
# Close tf.Session
s.close()
def main():
start_time = time.time() # Clocking start
# Loading Cifar-10 DataSet
ds = DataSet(height=32,
width=32,
channel=3,
ds_path="D:/DataSet/cifar/cifar-10-batches-py/",
ds_name='cifar-10')
ds_iter = DataIterator(
x=iu.transform(ds.train_images, '127'),
y=ds.train_labels,
batch_size=train_step['batch_size'],
label_off=False
) # using label # maybe someday, i'll change this param's name
# Generated image save
test_images = iu.transform(ds.test_images[:100], inv_type='127')
iu.save_images(test_images,
size=[10, 10],
image_path=results['output'] + 'sample.png',
inv_type='127')
# GPU configure
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
with tf.Session(config=config) as s:
# ACGAN Model
model = acgan.ACGAN(s,
batch_size=train_step['batch_size'],
n_classes=ds.n_classes)
# Initializing
s.run(tf.global_variables_initializer())
sample_y = np.zeros(shape=[model.sample_num, model.n_classes])
for i in range(10):
sample_y[10 * i:10 * (i + 1), i] = 1
saved_global_step = 0
ckpt = tf.train.get_checkpoint_state('./model/')
if ckpt and ckpt.model_checkpoint_path:
# Restores from checkpoint
model.saver.restore(s, ckpt.model_checkpoint_path)
saved_global_step = int(
ckpt.model_checkpoint_path.split('/')[-1].split('-')[-1])
print("[+] global step : %d" % saved_global_step,
" successfully loaded")
else:
print('[-] No checkpoint file found')
global_step = saved_global_step
start_epoch = global_step // (len(ds.train_images) // model.batch_size
) # recover n_epoch
ds_iter.pointer = saved_global_step % (
len(ds.train_images) // model.batch_size) # recover n_iter
for epoch in range(start_epoch, train_step['epochs']):
for batch_x, batch_y in ds_iter.iterate():
batch_z = np.random.uniform(
-1., 1.,
[model.batch_size, model.z_dim]).astype(np.float32)
# Update D network
_, d_loss = s.run([model.d_op, model.d_loss],
feed_dict={
model.x: batch_x,
model.y: batch_y,
model.z: batch_z,
})
# Update G/C networks
_, g_loss, _, c_loss = s.run(
[model.g_op, model.g_loss, model.c_op, model.c_loss],
feed_dict={
model.x: batch_x,
model.y: batch_y,
model.z: batch_z,
})
if global_step % train_step['logging_interval'] == 0:
batch_z = np.random.uniform(
-1., 1.,
[model.batch_size, model.z_dim]).astype(np.float32)
d_loss, g_loss, c_loss, summary = s.run([
model.d_loss, model.g_loss, model.c_loss, model.merged
],
feed_dict={
model.x:
batch_x,
model.y:
batch_y,
model.z:
batch_z,
})
# Print loss
print(
"[+] Epoch %04d Step %08d => " % (epoch, global_step),
" D loss : {:.8f}".format(d_loss),
" G loss : {:.8f}".format(g_loss),
" C loss : {:.8f}".format(c_loss))
# Training G model with sample image and noise
sample_z = np.random.uniform(
-1., 1.,
[model.sample_num, model.z_dim]).astype(np.float32)
samples = s.run(model.g,
feed_dict={
model.y: sample_y,
model.z: sample_z,
})
# Summary saver
model.writer.add_summary(summary, global_step)
# Export image generated by model G
sample_image_height = model.sample_size
sample_image_width = model.sample_size
sample_dir = results['output'] + 'train_{:08d}.png'.format(
global_step)
# Generated image save
iu.save_images(
samples,
size=[sample_image_height, sample_image_width],
image_path=sample_dir,
inv_type='127')
# Model save
model.saver.save(s, results['model'], global_step)
global_step += 1
end_time = time.time() - start_time # Clocking end
# Elapsed time
print("[+] Elapsed time {:.8f}s".format(end_time))
# Close tf.Session
s.close()