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 = cgan.CGAN(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_y = np.zeros(shape=[model.sample_num, model.y_dim])
sample_y[:, 5] = 1
sample_x, _ = mnist.train.next_batch(model.sample_num)
sample_x = np.reshape(
sample_x,
[-1, model.input_height, model.input_width, model.channel])
samples = s.run(model.g,
feed_dict={
model.x: sample_x,
model.z: sample_z,
model.y: sample_y
})
samples = np.reshape(
samples,
[-1, model.output_height, model.output_width, model.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 = DataSet(ds_path="D:\\DataSet/mnist/").data
# GPU configure
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
with tf.Session(config=config) as s:
# CGAN Model
model = cgan.CGAN(s, batch_size=train_step['batch_size'])
# initializing
s.run(tf.global_variables_initializer())
# Load model & Graph & Weights
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')
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
for global_step in range(saved_global_step, train_step['global_step']):
batch_x, batch_y = mnist.train.next_batch(model.batch_size)
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.c: batch_y,
model.z: batch_z,
model.do_rate: 0.5,
})
# Update G network
_, g_loss = s.run([model.g_op, model.g_loss],
feed_dict={
model.c: batch_y,
model.z: batch_z,
model.do_rate: 0.5,
})
# Logging
if global_step % train_step['logging_interval'] == 0:
batch_x, batch_y = mnist.test.next_batch(model.batch_size)
batch_z = np.random.uniform(
-1., 1.,
[model.batch_size, model.z_dim]).astype(np.float32)
d_loss, g_loss, summary = s.run(
[model.d_loss, model.g_loss, model.merged],
feed_dict={
model.x: batch_x,
model.c: batch_y,
model.z: batch_z,
model.do_rate: 0.5,
})
# Print Loss
print("[+] Step %08d => " % global_step,
" D loss : {:.8f}".format(d_loss),
" G loss : {:.8f}".format(g_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.c: sample_y,
model.z: sample_z,
model.do_rate: 0.0,
})
samples = np.reshape(samples, [-1, 28, 28, 1])
# 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)
# Model save
model.saver.save(s, results['model'], global_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
# MNIST Dataset Load
mnist = DataSet().data
# GPU configure
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
with tf.Session(config=config) as s:
# CGAN Model
model = cgan.CGAN(s)
# initializing
s.run(tf.global_variables_initializer())
sample_x, _ = mnist.train.next_batch(model.sample_num)
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
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_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.c: batch_y,
model.z: batch_z,
})
# Update G network
_, g_loss = s.run([model.g_op, model.g_loss],
feed_dict={
model.c: batch_y,
model.z: batch_z,
})
d_overpowered = d_loss < g_loss / 2
# Logging
if step % train_step['logging_interval'] == 0:
batch_x, batch_y = mnist.test.next_batch(model.batch_size)
batch_z = np.random.uniform(
-1., 1.,
[model.batch_size, model.z_dim]).astype(np.float32)
d_loss, g_loss, summary = s.run(
[model.d_loss, model.g_loss, model.merged],
feed_dict={
model.x: batch_x,
model.c: batch_y,
model.z: batch_z,
})
# Update d_overpowered
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))
# Training G model with sample image and noise
samples = s.run(model.g,
feed_dict={
model.c: sample_y,
model.z: sample_z,
})
samples = np.reshape(samples, [-1, 28, 28, 1])
# 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
# 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:
# GAN Model
model = cgan.CGAN(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.input_height,model.input_width,model.channel])
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.input_height,model.input_width,model.channel])
batch_x = batch_x*2 - 1
batch_z = np.random.uniform(-1., 1., size=[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.z: batch_z, model.y: batch_y,})
# Update G network
_, g_loss = s.run([model.g_op, model.g_loss],feed_dict={model.x: batch_x,model.z: batch_z,model.y: batch_y,})
d_overpowered = d_loss < (g_loss / 2)
if step % train_step['logging_interval'] == 0:
batch_x, _ = mnist.test.next_batch(model.batch_size)
batch_x = np.reshape(batch_x,[-1,model.input_height,model.input_width,model.channel])
batch_x = batch_x*2 - 1
batch_z = np.random.uniform(-1., 1., [model.batch_size, model.z_dim]).astype(np.float32)
d_loss, g_loss, summary = s.run([model.d_loss, model.g_loss, model.merged],
feed_dict={model.x: batch_x,model.z: batch_z,model.y: batch_y,})
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))
# Training G model with sample image and noise
samples = s.run(model.g,feed_dict={model.x: sample_x,model.z: sample_z,model.y: sample_y})
samples = np.reshape(samples, [-1, model.output_height, model.output_width, model.channel])
# 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)) # took about 370s on my machine
# Close tf.Session
s.close()