def main():
mnist = input_data.read_data_sets('../data/MNIST_data', one_hot=True)
# GPU configure
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
with tf.Session(config=config) as s:
# GAN Model
model = infogan.InfoGAN(s, is_train=False)
s.run(tf.global_variables_initializer())
saver = tf.train.Saver()
ckpt = tf.train.get_checkpoint_state('./model/')
if ckpt and ckpt.model_checkpoint_path:
ckpt_name = os.path.basename(ckpt.model_checkpoint_path)
saver.restore(s, os.path.join('./model/', ckpt_name))
else:
print("Cannot restore checkpoint!")
return False
sample_z = np.random.uniform(-1., 1., [model.sample_num, model.z_dim]).astype(np.float32)
# Create conditional one-hot vector, with index 5 = 1
sample_c = np.zeros(shape=[model.sample_num, model.n_cat])
sample_c[:, 8] = 1
print(sample_c[10] )
sample_x, _ = mnist.train.next_batch(model.sample_num)
sample_x = np.reshape(sample_x,[-1,model.input_height,model.input_width,model.input_channel])
samples = s.run(model.g,feed_dict={model.x: sample_x,model.z: sample_z,model.c: sample_c})
samples = np.reshape(samples, [-1, model.output_height, model.output_width, model.input_channel])
# Export image generated by model G
sample_image_height = model.sample_size
sample_image_width = model.sample_size
sample_dir = results['output'] + 'test.png'
# Generated image save
iu.save_images(samples,size=[sample_image_height, sample_image_width],image_path=sample_dir)
# Close tf.Session
s.close()
def main():
start_time = time.time() # Clocking start
# MNIST Dataset load
mnist = input_data.read_data_sets('../data/MNIST_data', one_hot=True)
# GPU configure
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
with tf.Session(config=config) as s:
# InfoGAN Model
model = infogan.InfoGAN(s)
# Initializing
s.run(tf.global_variables_initializer())
sample_x, _ = mnist.test.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)
z = np.random.uniform(-1., 1., model.z_dim).astype(np.float32)
sample_z = [z] * model.sample_num
sample_c = np.zeros(
shape=[model.sample_num, model.n_cat + model.n_cont])
samples_cont = np.random.normal(loc=0.0,
scale=1.0,
size=model.sample_num)
samples_cont = np.sort(samples_cont)
k = 0
sc = 2
for j in range(model.sample_num):
sample_c[j][k] = 1
sample_c[j][11] = samples_cont[j] * sc
k += 1
if k == 10:
k = 0
d_overpowered = False
for step in range(train_step['global_step']):
batch_x, _ = mnist.train.next_batch(model.batch_size)
batch_x = np.reshape(batch_x, [-1] + model.image_shape[1:])
batch_x = batch_x * 2 - 1
batch_z = np.random.uniform(
-1., 1., [model.batch_size, model.z_dim]).astype(np.float32)
batch_cat = np.random.multinomial(1,
model.n_cat *
[1.0 / model.n_cat],
size=model.batch_size)
mean = np.zeros(shape=[model.batch_size, model.n_cont])
stddev = np.ones(shape=[model.batch_size, model.n_cont])
epsilon = np.random.normal(loc=0.0,
scale=1.0,
size=[model.batch_size, model.n_cont])
batch_cont = mean + epsilon * stddev
batch_c = np.concatenate((batch_cat, batch_cont), axis=1)
# Update D network
if not d_overpowered:
_, d_loss = s.run([model.d_op, model.d_loss],
feed_dict={
model.x: batch_x,
model.z: batch_z,
model.c: batch_c,
})
# Update G network
_, g_loss = s.run([model.g_op, model.g_loss],
feed_dict={
model.x: batch_x,
model.z: batch_z,
model.c: batch_c,
})
_, q_loss = s.run([model.q_op, model.q_loss],
feed_dict={
model.x: batch_x,
model.z: batch_z,
model.c: batch_c
})
d_overpowered = d_loss < g_loss / 2
# Logging
if step % train_step['logging_interval'] == 0:
batch_x, _ = mnist.test.next_batch(model.batch_size)
batch_x = np.reshape(batch_x, [-1] + model.image_shape[1:])
batch_x = batch_x * 2 - 1
batch_z = np.random.uniform(
-1., 1.,
[model.batch_size, model.z_dim]).astype(np.float32)
batch_cat = np.random.multinomial(1,
model.n_cat *
[1.0 / model.n_cat],
size=model.batch_size)
mean = np.zeros(shape=[model.batch_size, model.n_cont])
stddev = np.ones(shape=[model.batch_size, model.n_cont])
epsilon = np.random.normal(
loc=0.0, scale=1.0, size=[model.batch_size, model.n_cont])
batch_cont = mean + epsilon * stddev
batch_c = np.concatenate((batch_cat, batch_cont), axis=1)
d_loss, g_loss, q_loss, summary = s.run(
[model.d_loss, model.g_loss, model.q_loss, model.merged],
feed_dict={
model.x: batch_x,
model.z: batch_z,
model.c: batch_c,
})
d_overpowered = d_loss < g_loss / 2
# Print loss
print("[+] Step %08d => " % step,
"Dloss: {:.8f}".format(d_loss),
"Gloss: {:.8f}".format(g_loss),
"Qloss: {:.8f}".format(q_loss))
for k in range(0, 10):
sample_feed = [sample_c[k]]
# Training G model with sample image and noise
samples = s.run(model.g,
feed_dict={
model.x: sample_x,
model.z: [sample_z[0]],
model.c: sample_feed
})
# Summary saver
model.writer.add_summary(summary, step)
# Export image generated by model G
sample_image_height = 1
sample_image_width = 1
sample_dir = results['output'] + str(
k) + 'train_{:08d}.png'.format(step)
# Generated image save
iu.save_images(
samples,
size=[sample_image_height, sample_image_width],
image_path=sample_dir)
# Training G model with sample image and noise
samples = s.run(model.g,
feed_dict={
model.x: sample_x,
model.z: sample_z,
model.c: sample_c
})
# 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()