def create_generator(Xin,
is_training,
Cout=1,
reuse=False,
networktype='ganG'):
'''input : batchsize * latentD
output: batchsize * 28 * 28 * 1'''
with tf.variable_scope(networktype, reuse=reuse):
Xout = dense(Xin,
is_training,
Cout=7 * 7 * 256,
act='reLu',
norm='batchnorm',
name='dense1')
Xout = tf.reshape(Xout, shape=[-1, 7, 7, 256]) # 7
Xout = deconv(Xout,
is_training,
kernel_w=4,
stride=2,
epf=2,
Cout=128,
act='reLu',
norm='batchnorm',
name='deconv1') # 14
Xout = deconv(Xout,
is_training,
kernel_w=4,
stride=2,
epf=2,
Cout=Cout,
act=None,
norm=None,
name='deconv2') # 28
Xout = tf.nn.sigmoid(Xout)
return Xout
def create_encoder(Xin,
is_training,
latentD,
reuse=False,
networktype='cdaeE'):
'''Xin: batchsize * H * W * Cin
output1-2: batchsize * Cout'''
with tf.variable_scope(networktype, reuse=reuse):
Xout = conv(Xin,
is_training,
kernel_w=4,
stride=2,
Cout=64,
pad=1,
act='reLu',
norm='batchnorm',
name='conv1') # 14*14
Xout = conv(Xout,
is_training,
kernel_w=4,
stride=2,
Cout=128,
pad=1,
act='reLu',
norm='batchnorm',
name='conv2') # 7*7
Xout = dense(Xout,
is_training,
Cout=latentD,
act=None,
norm=None,
name='dense_mean')
return Xout
def create_decoder(Xin,
is_training,
latentD,
Cout=1,
reuse=False,
networktype='cdaeD'):
with tf.variable_scope(networktype, reuse=reuse):
Xout = dense(Xin,
is_training,
Cout=7 * 7 * 256,
act='reLu',
norm='batchnorm',
name='dense1')
Xout = tf.reshape(Xout, shape=[-1, 7, 7, 256]) # 7
Xout = deconv(Xout,
is_training,
kernel_w=4,
stride=2,
Cout=256,
epf=2,
act='reLu',
norm='batchnorm',
name='deconv1') # 14
Xout = deconv(Xout,
is_training,
kernel_w=4,
stride=2,
Cout=Cout,
epf=2,
act=None,
norm=None,
name='deconv2') # 28
Xout = tf.nn.sigmoid(Xout)
return Xout
def create_gan_G(z,
is_training,
Cout=1,
trainable=True,
reuse=False,
networktype='ganG'):
'''input : batchsize * latentDim
output: batchsize * 28 * 28 * 1'''
with tf.variable_scope(networktype, reuse=reuse):
Gout = dense(z,
is_training,
Cout=7 * 7 * 256,
trainable=trainable,
act='reLu',
norm='batchnorm',
name='dense1')
Gout = tf.reshape(Gout, shape=[-1, 7, 7, 256]) # 7
Gout = deconv(Gout,
is_training,
kernel_w=4,
stride=2,
epf=2,
Cout=128,
trainable=trainable,
act='reLu',
norm='batchnorm',
name='deconv1') # 14
Gout = deconv(Gout,
is_training,
kernel_w=4,
stride=2,
epf=2,
Cout=Cout,
trainable=trainable,
act=None,
norm=None,
name='deconv2') # 28
Gout = tf.nn.sigmoid(Gout)
return Gout
def create_discriminator(Xin, is_training, reuse=False, networktype='ganD'):
with tf.variable_scope(networktype, reuse=reuse):
Xout = dense(Xin,
is_training,
Cout=7 * 7 * 256,
act='reLu',
norm='batchnorm',
name='dense1')
Xout = tf.reshape(Xout, shape=[-1, 7, 7, 256]) # 7
Xout = conv(Xout,
is_training,
kernel_w=3,
stride=1,
pad=1,
Cout=128,
act='lrelu',
norm='batchnorm',
name='conv1') # 7
Xout = conv(Xout,
is_training,
kernel_w=3,
stride=1,
pad=1,
Cout=256,
act='lrelu',
norm='batchnorm',
name='conv2') # 7
Xout = conv(Xout,
is_training,
kernel_w=3,
stride=1,
pad=None,
Cout=1,
act=None,
norm='batchnorm',
name='conv3') # 5
Xout = tf.nn.sigmoid(Xout)
return Xout