def model():
with tf.name_scope('MnistCNN'):
seq = tg.Sequential()
seq.add(Conv2D(num_filters=32, kernel_size=(3, 3), stride=(1, 1), padding='SAME'))
seq.add(BatchNormalization())
seq.add(RELU())
seq.add(MaxPooling(poolsize=(2, 2), stride=(2,2), padding='SAME'))
seq.add(LRN())
seq.add(Conv2D(num_filters=64, kernel_size=(3, 3), stride=(1, 1), padding='SAME'))
seq.add(BatchNormalization())
seq.add(RELU())
seq.add(MaxPooling(poolsize=(2, 2), stride=(2,2), padding='SAME'))
seq.add(LRN())
seq.add(Flatten())
seq.add(Linear(128))
seq.add(BatchNormalization())
seq.add(Tanh())
seq.add(Dropout(0.8))
seq.add(Linear(256))
seq.add(BatchNormalization())
seq.add(Tanh())
seq.add(Dropout(0.8))
seq.add(Linear(10))
seq.add(Softmax())
return seq
def test_UNet():
seq = tg.Sequential()
seq.add(UNet(input_shape=(h, w)))
seq.add(MaxPooling(poolsize=(3,3), stride=(1,1), padding='VALID'))
seq.add(Flatten())
seq.add(Linear(this_dim=nclass))
seq.add(Softmax())
train(seq)
def test_DenseNet():
seq = tg.Sequential()
seq.add(DenseNet(ndense=1, growth_rate=1, nlayer1blk=1))
seq.add(MaxPooling(poolsize=(3,3), stride=(1,1), padding='VALID'))
seq.add(Flatten())
seq.add(Linear(this_dim=nclass))
seq.add(Softmax())
train(seq)
def test_ResNetBase():
seq = tg.Sequential()
seq.add(ResNetBase(config=[1,1,1,1]))
seq.add(MaxPooling(poolsize=(1,1), stride=(1,1), padding='VALID'))
seq.add(Flatten())
seq.add(Linear(this_dim=nclass))
seq.add(Softmax())
train(seq)
def model():
with tf.name_scope('MnistCNN'):
seq = tg.Sequential()
seq.add(
Conv2D(input_channels=1,
num_filters=32,
kernel_size=(3, 3),
stride=(1, 1),
padding='SAME'))
h, w = same(in_height=28,
in_width=28,
stride=(1, 1),
kernel_size=(3, 3))
seq.add(BatchNormalization(input_shape=[h, w, 32]))
seq.add(RELU())
seq.add(MaxPooling(poolsize=(2, 2), stride=(2, 2), padding='SAME'))
h, w = same(in_height=h, in_width=w, stride=(2, 2), kernel_size=(2, 2))
seq.add(LRN())
seq.add(
Conv2D(input_channels=32,
num_filters=64,
kernel_size=(3, 3),
stride=(1, 1),
padding='SAME'))
h, w = same(in_height=h, in_width=w, stride=(1, 1), kernel_size=(3, 3))
seq.add(BatchNormalization(input_shape=[h, w, 64]))
seq.add(RELU())
seq.add(MaxPooling(poolsize=(2, 2), stride=(2, 2), padding='SAME'))
h, w = same(in_height=h, in_width=w, stride=(2, 2), kernel_size=(2, 2))
seq.add(LRN())
seq.add(Flatten())
seq.add(Linear(int(h * w * 64), 128))
seq.add(BatchNormalization(input_shape=[128]))
seq.add(Tanh())
seq.add(Dropout(0.8))
seq.add(Linear(128, 256))
seq.add(BatchNormalization(input_shape=[256]))
seq.add(Tanh())
seq.add(Dropout(0.8))
seq.add(Linear(256, 10))
seq.add(Softmax())
return seq
def test_DenseNet():
seq = tg.Sequential()
model = DenseNet(input_channels=c, input_shape=(h, w), ndense=1, growth_rate=1, nlayer1blk=1)
print('output channels:', model.output_channels)
print('output shape:', model.output_shape)
seq.add(model)
seq.add(MaxPooling(poolsize=tuple(model.output_shape), stride=(1,1), padding='VALID'))
seq.add(Flatten())
seq.add(Linear(model.output_channels, nclass))
seq.add(Softmax())
train(seq)
def test_UNet():
seq = tg.Sequential()
model = UNet(input_channels=c, input_shape=(h, w))
print('output channels:', model.output_channels)
print('output shape:', model.output_shape)
out_dim = np.prod(model.output_shape) * model.output_channels
seq.add(model)
seq.add(MaxPooling(poolsize=tuple(model.output_shape), stride=(1,1), padding='VALID'))
seq.add(Flatten())
seq.add(Linear(model.output_channels, nclass))
seq.add(Softmax())
train(seq)
def test_ResNetBase():
seq = tg.Sequential()
model = ResNetBase(input_channels=c, input_shape=(h, w), config=[1,1,1,1])
print('output channels:', model.output_channels)
print('output shape:', model.output_shape)
seq.add(model)
seq.add(MaxPooling(poolsize=tuple(model.output_shape), stride=(1,1), padding='VALID'))
outshape = valid_nd(model.output_shape, kernel_size=model.output_shape, stride=(1,1))
print(outshape)
out_dim = model.output_channels
seq.add(Flatten())
seq.add(Linear(int(out_dim), nclass))
seq.add(Softmax())
train(seq)
def __init__(self, nclass, h, w, c):
layers = []
model = UNet(input_channels=c, input_shape=(h, w))
layers.append(model)
layers.append(
MaxPooling(poolsize=tuple(model.output_shape),
stride=(1, 1),
padding='VALID'))
layers.append(Flatten())
layers.append(Linear(model.output_channels, nclass))
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 classifier(X_ph, X_gen_ph, h, w):
with tf.variable_scope('Classifier'):
X_sn = tg.StartNode(input_vars=[X_ph])
X_gen_sn = tg.StartNode(input_vars=[X_gen_ph])
h1, w1 = same(in_height=h,
in_width=w,
stride=(1, 1),
kernel_size=(3, 3))
h2, w2 = same(in_height=h1,
in_width=w1,
stride=(2, 2),
kernel_size=(2, 2))
h3, w3 = same(in_height=h2,
in_width=w2,
stride=(1, 1),
kernel_size=(3, 3))
h4, w4 = same(in_height=h3,
in_width=w3,
stride=(2, 2),
kernel_size=(2, 2))
print('---', h, w)
X_hn = tg.HiddenNode(prev=[X_sn],
layers=[
Conv2D(input_channels=1,
num_filters=32,
kernel_size=(3, 3),
stride=(1, 1),
padding='SAME'),
BatchNormalization(input_shape=[h1, w1, 32]),
RELU(),
MaxPooling(poolsize=(2, 2),
stride=(2, 2),
padding='SAME'),
LRN(),
Conv2D(input_channels=32,
num_filters=64,
kernel_size=(3, 3),
stride=(1, 1),
padding='SAME'),
BatchNormalization(input_shape=[h3, w3, 64]),
RELU(),
MaxPooling(poolsize=(2, 2),
stride=(2, 2),
padding='SAME'),
Flatten(),
])
X_gen_hn = tg.HiddenNode(
prev=[X_gen_sn],
layers=[
Conv2D(input_channels=1,
num_filters=32,
kernel_size=(3, 3),
stride=(1, 1),
padding='SAME'),
BatchNormalization(input_shape=[h1, w1, 32]),
RELU(),
MaxPooling(poolsize=(2, 2), stride=(2, 2), padding='SAME'),
LRN(),
Conv2D(input_channels=32,
num_filters=64,
kernel_size=(3, 3),
stride=(1, 1),
padding='SAME'),
BatchNormalization(input_shape=[h3, w3, 64]),
RELU(),
MaxPooling(poolsize=(2, 2), stride=(2, 2), padding='SAME'),
Flatten(),
])
print('===', h4 * w4 * 64 * 2)
merge_hn = tg.HiddenNode(prev=[X_hn, X_gen_hn],
input_merge_mode=Concat(),
layers=[
Linear(h4 * w4 * 64 * 2, 100),
RELU(),
BatchNormalization(input_shape=[100]),
Linear(100, 1),
Sigmoid()
])
en = tg.EndNode(prev=[merge_hn])
graph = tg.Graph(start=[X_sn, X_gen_sn], end=[en])
y_train, = graph.train_fprop()
y_test, = graph.test_fprop()
return y_train, y_test