我正在使用此处的代码（此处为论文）创建 GAN。我正在尝试将其应用到一个新领域，从他们在 MNIST 上的应用切换到 3D 大脑 MRI 图像。我的问题在于 GAN 本身的定义。
例如，他们用于定义生成模型的代码（获取维度 z_dim 的噪声并从 MNIST 分布生成图像，因此为 28x28）是这样的，我的评论基于我认为它是如何工作的：
def generate(self, z):
# start with noise in compact space
assert z.shape[1] == self.z_dim
# Fully connected layer that for some reason expands to latent * 64
output = tflib.ops.linear.Linear('Generator.Input', self.z_dim,
self.latent_dim * 64, z)
output = tf.nn.relu(output)
# Reshape the latent dimension into 4x4 MNIST
output = tf.reshape(output, [-1, self.latent_dim * 4, 4, 4])
# Reduce the latent dimension to get 8x8 MNIST
output = tflib.ops.deconv2d.Deconv2D('Generator.2', self.latent_dim * 4,
self.latent_dim * 2, 5, output)
output = tf.nn.relu(output) # 8 x 8
# To be able to get 28x28 later?
output = output[:, :, :7, :7] # 7 x 7
# Reduce more to get 14x14
output = tflib.ops.deconv2d.Deconv2D('Generator.3', self.latent_dim * 2,
self.latent_dim, 5, output)
output = tf.nn.relu(output) # 14 x 14
output = tflib.ops.deconv2d.Deconv2D('Generator.Output',
self.latent_dim, 1, 5, output)
output = tf.nn.sigmoid(output) # 28 x 28
if self.gen_params is None:
self.gen_params = tflib.params_with_name('Generator')
return tf.reshape(output, [-1, self.x_dim])
这是我使用 niftynet 卷积层的代码，其中 z_dim 和 latent_dim 与之前的 64 相同，并且我添加了打印语句的结果：
def generate(self, z):
assert z.shape[1] == self.z_dim
generator_input = FullyConnectedLayer(self.latent_dim * 64,
acti_func='relu',
#with_bn = True,
name='Generator.Input')
output = generator_input(z, is_training=True)
print(output.shape) # (?, 4096)
#output = tflib.ops.linear.Linear('Generator.Input', self.z_dim,
# self.latent_dim * 64, z)
#output = tf.nn.relu(output)
output = tf.reshape(output, [-1, self.latent_dim * 4, 1, 18, 18]) # 4 x 4
print(output.shape) # (?, 256, 1, 18, 18)
generator_2 = DeconvolutionalLayer(self.latent_dim*2,
kernel_size=5,
stride=2,
acti_func='relu',
name='Generator.2')
output = generator_2(output, is_training=True)
#output = tflib.ops.deconv2d.Deconv2D('Generator.2', self.latent_dim * 4,
# self.latent_dim * 2, 5, output)
#output = tf.nn.relu(output) # 8 x 8
print(output.shape) # (?, 512, 2, 36, 128)
#output = output[:, :, :-1, :-1] # 7 x 7
generator_3 = DeconvolutionalLayer(self.latent_dim,
kernel_size=5,
stride=2,
acti_func='relu',
name='Generator.3')
output = generator_3(output, is_training=True)
#output = tflib.ops.deconv2d.Deconv2D('Generator.3', self.latent_dim * 2,
# self.latent_dim, 5, output)
#output = tf.nn.relu(output) # 14 x 14
print(output.shape) # (?, 1024, 4, 72, 64)
generator_out = DeconvolutionalLayer(1,
kernel_size=5,
stride=2,
acti_func='sigmoid',
name='Generator.Output')
output = generator_out(output, is_training=True)
#output = tflib.ops.deconv2d.Deconv2D('Generator.Output',
# self.latent_dim, 1, 5, output)
#output = tf.nn.sigmoid(output) # 28 x 28
if self.gen_params is None:
self.gen_params = tflib.params_with_name('Generator')
print(output.shape) # (?, 2048, 8, 144, 1)
print("Should be %s" % str(self.x_dim)) # [1, 19, 144, 144, 4]
return tf.reshape(output, self.x_dim)
我不太确定如何才能将 19 放在那里。目前我收到此错误。
ValueError：维度大小必须能被 2359296 整除，但对于输入形状为 [?,2048,8,144,1], [5] 且输入张量计算为部分形状的“Reshape_1”（操作：“Reshape”）为 1575936：输入1 = [1,19,144,144,4]。
我对构建神经网络也比较陌生，我也有几个问题。当我们在 z 空间中已经有了一个紧凑的表示时，潜在空间的意义何在？如何确定“输出维度”的大小，即层构造函数中的第二个参数？
我也一直在寻找 CNN 的成功实施，并在这里寻求灵感。谢谢！
主要编辑：
我取得了一些进展，并让 tensorflow 运行代码。但是，即使批量大小为 1，当我尝试运行训练操作时也会遇到内存不足错误。我计算出一张图像的大小为 19 * 144 * 144 * 4 * 32（每像素位数）= ~50 MB，因此导致内存错误的不是数据。由于我基本上只是调整了 GAN 参数直到它起作用，我的问题可能就在那里。下面是整个文件。
class MnistWganInv(object):
def __init__(self, x_dim=784, z_dim=64, latent_dim=64, batch_size=80,
c_gp_x=10., lamda=0.1, output_path='./'):
self.x_dim = [-1] + x_dim[1:]
self.z_dim = z_dim
self.latent_dim = latent_dim
self.batch_size = batch_size
self.c_gp_x = c_gp_x
self.lamda = lamda
self.output_path = output_path
self.gen_params = self.dis_params = self.inv_params = None
self.z = tf.placeholder(tf.float32, shape=[None, self.z_dim])
self.x_p = self.generate(self.z)
self.x = tf.placeholder(tf.float32, shape=x_dim)
self.z_p = self.invert(self.x)
self.dis_x = self.discriminate(self.x)
self.dis_x_p = self.discriminate(self.x_p)
self.rec_x = self.generate(self.z_p)
self.rec_z = self.invert(self.x_p)
self.gen_cost = -tf.reduce_mean(self.dis_x_p)
self.inv_cost = tf.reduce_mean(tf.square(self.x - self.rec_x))
self.inv_cost += self.lamda * tf.reduce_mean(tf.square(self.z - self.rec_z))
self.dis_cost = tf.reduce_mean(self.dis_x_p) - tf.reduce_mean(self.dis_x)
alpha = tf.random_uniform(shape=[self.batch_size, 1], minval=0., maxval=1.)
difference = self.x_p - self.x
interpolate = self.x + alpha * difference
gradient = tf.gradients(self.discriminate(interpolate), [interpolate])[0]
slope = tf.sqrt(tf.reduce_sum(tf.square(gradient), axis=1))
gradient_penalty = tf.reduce_mean((slope - 1.) ** 2)
self.dis_cost += self.c_gp_x * gradient_penalty
self.gen_params = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope='Generator')
self.inv_params = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope='Inverter')
self.dis_params = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope='Discriminator')
self.gen_train_op = tf.train.AdamOptimizer(
learning_rate=1e-4, beta1=0.9, beta2=0.999).minimize(
self.gen_cost, var_list=self.gen_params)
self.inv_train_op = tf.train.AdamOptimizer(
learning_rate=1e-4, beta1=0.9, beta2=0.999).minimize(
self.inv_cost, var_list=self.inv_params)
self.dis_train_op = tf.train.AdamOptimizer(
learning_rate=1e-4, beta1=0.9, beta2=0.999).minimize(
self.dis_cost, var_list=self.dis_params)
def generate(self, z):
print(z.shape)
assert z.shape[1] == self.z_dim
with tf.name_scope('Generator.Input') as scope:
generator_input = FullyConnectedLayer(self.latent_dim * 4 * 3 * 18 * 18,
acti_func='relu',
#with_bn = True,
name='Generator.Input')(z, is_training=True)
print(generator_input.shape)
#output = tflib.ops.linear.Linear('Generator.Input', self.z_dim,
# self.latent_dim * 64, z)
#output = tf.nn.relu(output)
generator_input = tf.reshape(generator_input, [-1, 3, 18, 18, self.latent_dim * 4]) # 4 x 4
print(generator_input.shape)
with tf.name_scope('Generator.2') as scope:
generator_2 = DeconvolutionalLayer(self.latent_dim*2,
kernel_size=5,
stride=2,
acti_func='relu',
name='Generator.2')(generator_input, is_training=True)
#output = tflib.ops.deconv2d.Deconv2D('Generator.2', self.latent_dim * 4,
# self.latent_dim * 2, 5, output)
#output = tf.nn.relu(output) # 8 x 8
print(generator_2.shape)
with tf.name_scope('Generator.3') as scope:
generator_3 = DeconvolutionalLayer(self.latent_dim,
kernel_size=5,
stride=2,
acti_func='relu',
name='Generator.3')(generator_2, is_training=True)
#output = tflib.ops.deconv2d.Deconv2D('Generator.3', self.latent_dim * 2,
# self.latent_dim, 5, output)
#output = tf.nn.relu(output) # 14 x 14
print(generator_3.shape)
with tf.name_scope('Generator.Output') as scope:
generator_out = DeconvolutionalLayer(4,
kernel_size=5,
stride=2,
acti_func='sigmoid',
name='Generator.Output')(generator_3, is_training=True)
#output = tflib.ops.deconv2d.Deconv2D('Generator.Output',
# self.latent_dim, 1, 5, output)
#output = tf.nn.sigmoid(output) # 28 x 28
if self.gen_params is None:
self.gen_params = tflib.params_with_name('Generator')
print(generator_out.shape)
generator_out = generator_out[:, :19, :, :, :]
print(generator_out.shape)
print("Should be %s" % str(self.x_dim))
return tf.reshape(generator_out, self.x_dim)
def discriminate(self, x):
input = tf.reshape(x, self.x_dim) # 28 x 28
with tf.name_scope('Discriminator.Input') as scope:
discriminator_input = ConvolutionalLayer(self.latent_dim,
kernel_size=5,
stride=2,
acti_func='leakyrelu',
name='Discriminator.Input')(input, is_training=True)
#output = tflib.ops.conv2d.Conv2D(
# 'Discriminator.Input', 1, self.latent_dim, 5, output, stride=2)
#output = tf.nn.leaky_relu(output) # 14 x 14
with tf.name_scope('Discriminator.2') as scope:
discriminator_2 = ConvolutionalLayer(self.latent_dim*2,
kernel_size=5,
stride=2,
acti_func='leakyrelu',
name='Discriminator.2')(discriminator_input, is_training=True)
#output = tflib.ops.conv2d.Conv2D(
# 'Discriminator.2', self.latent_dim, self.latent_dim * 2, 5,
# output, stride=2)
#output = tf.nn.leaky_relu(output) # 7 x 7
with tf.name_scope('Discriminator.3') as scope:
discriminator_3 = ConvolutionalLayer(self.latent_dim*4,
kernel_size=5,
stride=2,
acti_func='leakyrelu',
name='Discriminator.3')(discriminator_2, is_training=True)
#output = tflib.ops.conv2d.Conv2D(
# 'Discriminator.3', self.latent_dim * 2, self.latent_dim * 4, 5,
# output, stride=2)
#output = tf.nn.leaky_relu(output) # 4 x 4
discriminator_3 = tf.reshape(discriminator_3, [-1, self.latent_dim * 48])
with tf.name_scope('Discriminator.Output') as scope:
discriminator_out = FullyConnectedLayer(1,
name='Discriminator.Output')(discriminator_3, is_training=True)
#output = tflib.ops.linear.Linear(
# 'Discriminator.Output', self.latent_dim * 64, 1, output)
discriminator_out = tf.reshape(discriminator_out, [-1])
if self.dis_params is None:
self.dis_params = tflib.params_with_name('Discriminator')
return discriminator_out
def invert(self, x):
output = tf.reshape(x, self.x_dim) # 28 x 28
with tf.name_scope('Inverter.Input') as scope:
inverter_input = ConvolutionalLayer(self.latent_dim,
kernel_size=5,
stride=2,
#padding='VALID',
#w_initializer=self.initializers['w'],
#w_regularizer=self.regularizers['w'],
#b_initializer=self.initializers['b'],
#b_regularizer=self.regularizers['b'],
acti_func='leakyrelu',
#with_bn = True,
name='Inverter.Input')
#output = tflib.ops.conv2d.Conv2D(
# 'Inverter.Input', 1, self.latent_dim, 5, output, stride=2)
#output = tf.nn.leaky_relu(output) # 14 x 14
output = inverter_input(output, is_training=True)
with tf.name_scope('Inverter.2') as scope:
inverter_2 = ConvolutionalLayer(self.latent_dim*2,
kernel_size=5,
stride=2,
acti_func='leakyrelu',
name='Inverter.2')
output = inverter_2(output, is_training=True)
#output = tflib.ops.conv2d.Conv2D(
# 'Inverter.2', self.latent_dim, self.latent_dim * 2, 5, output,
# stride=2)
#output = tf.nn.leaky_relu(output) # 7 x 7
with tf.name_scope('Inverter.3') as scope:
inverter_3 = ConvolutionalLayer(self.latent_dim*4,
kernel_size=5,
stride=2,
acti_func='leakyrelu',
name='Inverter.3')
output = inverter_3(output, is_training=True)
#output = tflib.ops.conv2d.Conv2D(
# 'Inverter.3', self.latent_dim * 2, self.latent_dim * 4, 5,
# output, stride=2)
#output = tf.nn.leaky_relu(output) # 4 x 4
output = tf.reshape(output, [-1, self.latent_dim * 48])
with tf.name_scope('Inverter.4') as scope:
inverter_4 = FullyConnectedLayer(self.latent_dim*8,
acti_func='leakyrelu',
#with_bn = True,
name='Inverter.4')
output = inverter_4(output, is_training=True)
#output = tflib.ops.linear.Linear(
# 'Inverter.4', self.latent_dim * 64, self.latent_dim * 8, output)
#output = tf.nn.leaky_relu(output)
with tf.name_scope('Inverter.Output') as scope:
inverter_output = FullyConnectedLayer(self.z_dim,
acti_func='leakyrelu',
#with_bn = True,
name='Inverter.Output')
output = inverter_output(output, is_training=True)
#output = tflib.ops.linear.Linear(
# 'Inverter.Output', self.latent_dim * 8, self.z_dim, output)
output = tf.reshape(output, [-1, self.z_dim])
if self.inv_params is None:
self.inv_params = tflib.params_with_name('Inverter')
return output
def train_gen(self, sess, x, z):
_gen_cost, _ = sess.run([self.gen_cost, self.gen_train_op],
feed_dict={self.x: x, self.z: z})
return _gen_cost
def train_dis(self, sess, x, z):
_dis_cost, _ = sess.run([self.dis_cost, self.dis_train_op],
feed_dict={self.x: x, self.z: z})
return _dis_cost
def train_inv(self, sess, x, z):
_inv_cost, _ = sess.run([self.inv_cost, self.inv_train_op],
feed_dict={self.x: x, self.z: z})
return _inv_cost
def generate_from_noise(self, sess, noise, frame):
samples = sess.run(self.x_p, feed_dict={self.z: noise})
for i in range(batch_size):
save_array_as_nifty_volume(samples[i], "examples/img_{0:}.nii.gz".format(n*batch_size + i))
#tflib.save_images.save_images(
# samples.reshape((-1, 28, 28)),
# os.path.join(self.output_path, 'examples/samples_{}.png'.format(frame)))
return samples
def reconstruct_images(self, sess, images, frame):
reconstructions = sess.run(self.rec_x, feed_dict={self.x: images})
comparison = np.zeros((images.shape[0] * 2, images.shape[1]),
dtype=np.float32)
for i in range(images.shape[0]):
comparison[2 * i] = images[i]
comparison[2 * i + 1] = reconstructions[i]
for i in range(batch_size):
save_array_as_nifty_volume(comparison[i], "examples/img_{0:}.nii.gz".format(n*batch_size + i))
#tflib.save_images.save_images(
# comparison.reshape((-1, 28, 28)),
# os.path.join(self.output_path, 'examples/recs_{}.png'.format(frame)))
return comparison
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--z_dim', type=int, default=64, help='dimension of z')
parser.add_argument('--latent_dim', type=int, default=64,
help='latent dimension')
parser.add_argument('--iterations', type=int, default=100000,
help='training steps')
parser.add_argument('--dis_iter', type=int, default=5,
help='discriminator steps')
parser.add_argument('--c_gp_x', type=float, default=10.,
help='coefficient for gradient penalty x')
parser.add_argument('--lamda', type=float, default=.1,
help='coefficient for divergence of z')
parser.add_argument('--output_path', type=str, default='./',
help='output path')
parser.add_argument('-config')
args = parser.parse_args()
config = parse_config(args.config)
config_data = config['data']
print("Loading data...")
# dataset iterator
dataloader = DataLoader(config_data)
dataloader.load_data()
batch_size = config_data['batch_size']
full_data_shape = [batch_size] + config_data['data_shape']
#train_gen, dev_gen, test_gen = tflib.mnist.load(args.batch_size, args.batch_size)
def inf_train_gen():
while True:
train_pair = dataloader.get_subimage_batch()
tempx = train_pair['images']
tempw = train_pair['weights']
tempy = train_pair['labels']
yield tempx, tempw, tempy
#_, _, test_data = tflib.mnist.load_data()
#fixed_images = test_data[0][:32]
#del test_data
tf.set_random_seed(326)
np.random.seed(326)
fixed_noise = np.random.randn(64, args.z_dim)
print("Initializing GAN...")
mnistWganInv = MnistWganInv(
x_dim=full_data_shape, z_dim=args.z_dim, latent_dim=args.latent_dim,
batch_size=batch_size, c_gp_x=args.c_gp_x, lamda=args.lamda,
output_path=args.output_path)
saver = tf.train.Saver(max_to_keep=1000)
with tf.Session() as session:
session.run(tf.global_variables_initializer())
images = noise = gen_cost = dis_cost = inv_cost = None
dis_cost_lst, inv_cost_lst = [], []
print("Starting training...")
for iteration in range(args.iterations):
for i in range(args.dis_iter):
noise = np.random.randn(batch_size, args.z_dim)
images, images_w, images_y = next(inf_train_gen())
dis_cost_lst += [mnistWganInv.train_dis(session, images, noise)]
inv_cost_lst += [mnistWganInv.train_inv(session, images, noise)]
gen_cost = mnistWganInv.train_gen(session, images, noise)
dis_cost = np.mean(dis_cost_lst)
inv_cost = np.mean(inv_cost_lst)
tflib.plot.plot('train gen cost', gen_cost)
tflib.plot.plot('train dis cost', dis_cost)
tflib.plot.plot('train inv cost', inv_cost)
if iteration % 100 == 99:
mnistWganInv.generate_from_noise(session, fixed_noise, iteration)
mnistWganInv.reconstruct_images(session, fixed_images, iteration)
if iteration % 1000 == 999:
save_path = saver.save(session, os.path.join(
args.output_path, 'models/model'), global_step=iteration)
if iteration % 1000 == 999:
dev_dis_cost_lst, dev_inv_cost_lst = [], []
for dev_images, _ in dev_gen():
noise = np.random.randn(batch_size, args.z_dim)
dev_dis_cost, dev_inv_cost = session.run(
[mnistWganInv.dis_cost, mnistWganInv.inv_cost],
feed_dict={mnistWganInv.x: dev_images,
mnistWganInv.z: noise})
dev_dis_cost_lst += [dev_dis_cost]
dev_inv_cost_lst += [dev_inv_cost]
tflib.plot.plot('dev dis cost', np.mean(dev_dis_cost_lst))
tflib.plot.plot('dev inv cost', np.mean(dev_inv_cost_lst))
if iteration < 5 or iteration % 100 == 99:
tflib.plot.flush(os.path.join(args.output_path, 'models'))
tflib.plot.tick()