def model(nclass, h, w, c):
with tf.name_scope('Cifar10AllCNN'):
seq = tg.Sequential()
seq.add(Conv2D(input_channels=c, num_filters=96, kernel_size=(3, 3), stride=(1, 1), padding='SAME'))
seq.add(RELU())
seq.add(TFBatchNormalization(name='b1'))
h, w = same(in_height=h, in_width=w, stride=(1,1), kernel_size=(3,3))
seq.add(Conv2D(input_channels=96, num_filters=96, kernel_size=(3, 3), stride=(1, 1), padding='SAME'))
seq.add(RELU())
h, w = same(in_height=h, in_width=w, stride=(1,1), kernel_size=(3,3))
seq.add(Dropout(0.5))
seq.add(Conv2D(input_channels=96, num_filters=96, kernel_size=(3, 3), stride=(2, 2), padding='SAME'))
seq.add(RELU())
seq.add(TFBatchNormalization(name='b3'))
h, w = same(in_height=h, in_width=w, stride=(2,2), kernel_size=(3,3))
seq.add(Conv2D(input_channels=96, num_filters=192, kernel_size=(3, 3), stride=(1, 1), padding='SAME'))
seq.add(RELU())
h, w = same(in_height=h, in_width=w, stride=(1,1), kernel_size=(3,3))
seq.add(Dropout(0.5))
seq.add(Conv2D(input_channels=192, num_filters=192, kernel_size=(3, 3), stride=(1, 1), padding='SAME'))
seq.add(RELU())
seq.add(TFBatchNormalization(name='b5'))
h, w = same(in_height=h, in_width=w, stride=(1,1), kernel_size=(3,3))
seq.add(Conv2D(input_channels=192, num_filters=192, kernel_size=(3, 3), stride=(2, 2), padding='SAME'))
seq.add(RELU())
h, w = same(in_height=h, in_width=w, stride=(2,2), kernel_size=(3,3))
seq.add(Dropout(0.5))
seq.add(Conv2D(input_channels=192, num_filters=192, kernel_size=(3, 3), stride=(1, 1), padding='SAME'))
seq.add(RELU())
seq.add(TFBatchNormalization(name='b7'))
h, w = same(in_height=h, in_width=w, stride=(1,1), kernel_size=(3,3))
seq.add(Conv2D(input_channels=192, num_filters=192, kernel_size=(1, 1), stride=(1, 1), padding='SAME'))
seq.add(RELU())
h, w = same(in_height=h, in_width=w, stride=(1,1), kernel_size=(1,1))
seq.add(Dropout(0.5))
seq.add(Conv2D(input_channels=192, num_filters=nclass, kernel_size=(1, 1), stride=(1, 1), padding='SAME'))
seq.add(RELU())
seq.add(TFBatchNormalization(name='b9'))
h, w = same(in_height=h, in_width=w, stride=(1,1), kernel_size=(1,1))
seq.add(AvgPooling(poolsize=(h, w), stride=(1,1), padding='VALID'))
seq.add(Flatten())
seq.add(Softmax())
return seq
def __init__(self, nclass, h, w, c):
layers = []
identityblk = IdentityBlock(input_channels=c,
input_shape=[h, w],
nlayers=10)
layers.append(identityblk)
layers.append(
Conv2D(input_channels=c,
num_filters=16,
kernel_size=(3, 3),
stride=(1, 1),
padding='SAME'))
layers.append(RELU())
h, w = same(in_height=h, in_width=w, stride=(1, 1), kernel_size=(3, 3))
layers.append(BatchNormalization(input_shape=[h, w, 16]))
denseblk = DenseBlock(input_channels=16,
input_shape=[h, w],
growth_rate=4,
nlayers=4)
layers.append(denseblk)
layers.append(
Conv2D(input_channels=denseblk.output_channels,
num_filters=32,
kernel_size=(3, 3),
stride=(2, 2),
padding='SAME'))
layers.append(RELU())
h, w = same(in_height=h, in_width=w, stride=(2, 2), kernel_size=(3, 3))
layers.append(Dropout(0.5))
layers.append(
Conv2D(input_channels=32,
num_filters=nclass,
kernel_size=(1, 1),
stride=(1, 1),
padding='SAME'))
layers.append(RELU())
h, w = same(in_height=h, in_width=w, stride=(1, 1), kernel_size=(1, 1))
layers.append(BatchNormalization(input_shape=[h, w, nclass]))
layers.append(
AvgPooling(poolsize=(h, w), stride=(1, 1), padding='VALID'))
layers.append(Flatten())
layers.append(Softmax())
self.startnode = tg.StartNode(input_vars=[None])
model_hn = tg.HiddenNode(prev=[self.startnode], layers=layers)
self.endnode = tg.EndNode(prev=[model_hn])
def __init__(self, nclass, h, w, c):
layers = []
layers.append(
Conv2D(input_channels=c,
num_filters=96,
kernel_size=(3, 3),
stride=(1, 1),
padding='SAME'))
layers.append(RELU())
h, w = same(in_height=h, in_width=w, stride=(1, 1), kernel_size=(3, 3))
layers.append(BatchNormalization(input_shape=[h, w, 96]))
layers.append(
Conv2D(input_channels=96,
num_filters=96,
kernel_size=(3, 3),
stride=(1, 1),
padding='SAME'))
layers.append(RELU())
h, w = same(in_height=h, in_width=w, stride=(1, 1), kernel_size=(3, 3))
layers.append(Dropout(0.5))
layers.append(
Conv2D(input_channels=96,
num_filters=96,
kernel_size=(3, 3),
stride=(2, 2),
padding='SAME'))
layers.append(RELU())
h, w = same(in_height=h, in_width=w, stride=(2, 2), kernel_size=(3, 3))
layers.append(BatchNormalization(input_shape=[h, w, 96]))
layers.append(
Conv2D(input_channels=96,
num_filters=192,
kernel_size=(3, 3),
stride=(1, 1),
padding='SAME'))
layers.append(RELU())
h, w = same(in_height=h, in_width=w, stride=(1, 1), kernel_size=(3, 3))
layers.append(Dropout(0.5))
layers.append(
Conv2D(input_channels=192,
num_filters=192,
kernel_size=(3, 3),
stride=(1, 1),
padding='SAME'))
layers.append(RELU())
h, w = same(in_height=h, in_width=w, stride=(1, 1), kernel_size=(3, 3))
layers.append(BatchNormalization(input_shape=[h, w, 192]))
layers.append(
Conv2D(input_channels=192,
num_filters=192,
kernel_size=(3, 3),
stride=(2, 2),
padding='SAME'))
layers.append(RELU())
h, w = same(in_height=h, in_width=w, stride=(2, 2), kernel_size=(3, 3))
layers.append(Dropout(0.5))
layers.append(
Conv2D(input_channels=192,
num_filters=192,
kernel_size=(3, 3),
stride=(1, 1),
padding='SAME'))
layers.append(RELU())
h, w = same(in_height=h, in_width=w, stride=(1, 1), kernel_size=(3, 3))
layers.append(BatchNormalization(input_shape=[h, w, 192]))
layers.append(
Conv2D(input_channels=192,
num_filters=192,
kernel_size=(1, 1),
stride=(1, 1),
padding='SAME'))
layers.append(RELU())
h, w = same(in_height=h, in_width=w, stride=(1, 1), kernel_size=(1, 1))
layers.append(Dropout(0.5))
layers.append(
Conv2D(input_channels=192,
num_filters=nclass,
kernel_size=(1, 1),
stride=(1, 1),
padding='SAME'))
layers.append(RELU())
h, w = same(in_height=h, in_width=w, stride=(1, 1), kernel_size=(1, 1))
layers.append(BatchNormalization(input_shape=[h, w, nclass]))
layers.append(
AvgPooling(poolsize=(h, w), stride=(1, 1), padding='VALID'))
layers.append(Flatten())
layers.append(Softmax())
self.startnode = tg.StartNode(input_vars=[None])
model_hn = tg.HiddenNode(prev=[self.startnode], layers=layers)
self.endnode = tg.EndNode(prev=[model_hn])
def discriminator_allconv(self):
if not self.generator_called:
raise Exception(
'self.generator() has to be called first before self.discriminator()'
)
scope = 'Discriminator'
with self.tf_graph.as_default():
with tf.name_scope(scope):
# h1, w1 = valid(self.h, self.w, kernel_size=(5,5), stride=(1,1))
# h2, w2 = valid(h1, w1, kernel_size=(5,5), stride=(2,2))
# h3, w3 = valid(h2, w2, kernel_size=(5,5), stride=(2,2))
# flat_dim = int(h3*w3*32)
dis_real_sn = tg.StartNode(input_vars=[self.real_ph])
# fake_ph = tf.placeholder('float32', [None, self.h, self.w, 1], name='fake')
# fake_sn = tg.StartNode(input_vars=[fake_ph])
h, w = same(in_height=self.h,
in_width=self.w,
stride=(1, 1),
kernel_size=(3, 3))
h, w = same(in_height=h,
in_width=w,
stride=(1, 1),
kernel_size=(3, 3))
h, w = same(in_height=h,
in_width=w,
stride=(2, 2),
kernel_size=(3, 3))
h, w = same(in_height=h,
in_width=w,
stride=(1, 1),
kernel_size=(3, 3))
h, w = same(in_height=h,
in_width=w,
stride=(1, 1),
kernel_size=(3, 3))
h, w = same(in_height=h,
in_width=w,
stride=(2, 2),
kernel_size=(3, 3))
h, w = same(in_height=h,
in_width=w,
stride=(1, 1),
kernel_size=(1, 1))
h, w = same(in_height=h,
in_width=w,
stride=(1, 1),
kernel_size=(3, 3))
h, w = same(in_height=h,
in_width=w,
stride=(1, 1),
kernel_size=(1, 1))
print('h, w', h, w)
print('===============')
# h, w = valid(in_height=h, in_width=w, stride=(1,1), kernel_size=(h,w))
disc_hn = tg.HiddenNode(
prev=[dis_real_sn, self.gen_hn],
layers=[
Dropout(0.2),
# TFBatchNormalization(name='b0'),
Conv2D(input_channels=self.c,
num_filters=96,
kernel_size=(3, 3),
stride=(1, 1),
padding='SAME'),
LeakyRELU(),
TFBatchNormalization(name='b1'),
# Dropout(0.5),
Conv2D(input_channels=96,
num_filters=96,
kernel_size=(3, 3),
stride=(1, 1),
padding='SAME'),
LeakyRELU(),
# TFBatchNormalization(name='b2'),
Dropout(0.5),
Conv2D(input_channels=96,
num_filters=96,
kernel_size=(3, 3),
stride=(2, 2),
padding='SAME'),
LeakyRELU(),
TFBatchNormalization(name='b3'),
# Dropout(0.5),
Conv2D(input_channels=96,
num_filters=192,
kernel_size=(3, 3),
stride=(1, 1),
padding='SAME'),
LeakyRELU(),
# TFBatchNormalization(name='b4'),
Dropout(0.5),
Conv2D(input_channels=192,
num_filters=192,
kernel_size=(3, 3),
stride=(1, 1),
padding='SAME'),
LeakyRELU(),
TFBatchNormalization(name='b5'),
# Dropout(0.5),
Conv2D(input_channels=192,
num_filters=192,
kernel_size=(3, 3),
stride=(2, 2),
padding='SAME'),
LeakyRELU(),
# TFBatchNormalization(name='b6'),
Dropout(0.5),
Conv2D(input_channels=192,
num_filters=192,
kernel_size=(3, 3),
stride=(1, 1),
padding='SAME'),
LeakyRELU(),
TFBatchNormalization(name='b7'),
# Dropout(0.5),
Conv2D(input_channels=192,
num_filters=192,
kernel_size=(1, 1),
stride=(1, 1),
padding='SAME'),
LeakyRELU(),
# TFBatchNormalization(name='b8'),
Dropout(0.5),
Conv2D(input_channels=192,
num_filters=self.nclass,
kernel_size=(1, 1),
stride=(1, 1),
padding='SAME'),
LeakyRELU(),
TFBatchNormalization(name='b9'),
# Dropout(0.5),
AvgPooling(poolsize=(h, w),
stride=(1, 1),
padding='VALID'),
Flatten(),
])
print('h,w', h, w)
print('==============')
class_hn = tg.HiddenNode(
prev=[disc_hn],
layers=[
Linear(self.nclass, self.nclass),
# Softmax()
])
judge_hn = tg.HiddenNode(
prev=[disc_hn],
layers=[
Linear(self.nclass, 1),
# Sigmoid()
])
real_class_en = tg.EndNode(prev=[class_hn])
real_judge_en = tg.EndNode(prev=[judge_hn])
fake_class_en = tg.EndNode(prev=[class_hn])
fake_judge_en = tg.EndNode(prev=[judge_hn])
graph = tg.Graph(start=[dis_real_sn],
end=[real_class_en, real_judge_en])
real_train = graph.train_fprop()
real_valid = graph.test_fprop()
graph = tg.Graph(start=[self.noise_sn, self.gen_real_sn],
end=[fake_class_en, fake_judge_en])
fake_train = graph.train_fprop()
fake_valid = graph.test_fprop()
dis_var_list = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES,
scope=scope)
return self.real_ph, real_train, real_valid, fake_train, fake_valid, dis_var_list
def model(nclass, h, w, c):
with tf.name_scope('Cifar10AllCNN'):
seq = tg.Sequential()
seq.add(
Conv2D(num_filters=96,
kernel_size=(3, 3),
stride=(1, 1),
padding='SAME'))
seq.add(RELU())
seq.add(BatchNormalization())
seq.add(
Conv2D(num_filters=96,
kernel_size=(3, 3),
stride=(1, 1),
padding='SAME'))
seq.add(RELU())
seq.add(Dropout(0.5))
seq.add(
Conv2D(num_filters=96,
kernel_size=(3, 3),
stride=(2, 2),
padding='SAME'))
seq.add(RELU())
seq.add(BatchNormalization())
seq.add(
Conv2D(num_filters=192,
kernel_size=(3, 3),
stride=(1, 1),
padding='SAME'))
seq.add(RELU())
seq.add(Dropout(0.5))
seq.add(
Conv2D(num_filters=192,
kernel_size=(3, 3),
stride=(1, 1),
padding='SAME'))
seq.add(RELU())
seq.add(BatchNormalization())
seq.add(
Conv2D(num_filters=192,
kernel_size=(3, 3),
stride=(2, 2),
padding='SAME'))
seq.add(RELU())
seq.add(Dropout(0.5))
seq.add(
Conv2D(num_filters=192,
kernel_size=(3, 3),
stride=(1, 1),
padding='SAME'))
seq.add(RELU())
seq.add(BatchNormalization())
seq.add(
Conv2D(num_filters=192,
kernel_size=(1, 1),
stride=(1, 1),
padding='SAME'))
seq.add(RELU())
seq.add(Dropout(0.5))
seq.add(
Conv2D(num_filters=nclass,
kernel_size=(1, 1),
stride=(1, 1),
padding='SAME'))
seq.add(RELU())
seq.add(BatchNormalization())
seq.add(AvgPooling(poolsize=(8, 8), stride=(1, 1), padding='VALID'))
seq.add(Flatten())
seq.add(Softmax())
return seq