def __init__(self, name=None, num=None):
super(TSNResNet, self).__init__()
self.convbn = convbn(3, 16)
self.convpools = dygraph.Sequential(convpool(16, 32, pooling=4),
convpool(32, 64, pooling=4),
convpool(64, 128))
self.fcs = dygraph.Sequential(
dygraph.Linear(7 * 7 * 128, 1024, act='relu'),
dygraph.BatchNorm(1024), dygraph.Dropout(0.5),
dygraph.Linear(1024, 101, act='softmax'))
self.seg_num = 32
def __init__(self):
super(HarFcn, self).__init__()
self.cnn1 = dy.Sequential(
dy.Conv2D(num_channels=1,
num_filters=128,
filter_size=3,
stride=1,
padding=1),
dy.BatchNorm(num_channels=128),
dy.Dropout(p=.2),
)
self.cnn2 = dy.Sequential(
dy.Conv2D(num_channels=128,
num_filters=128,
filter_size=3,
stride=1,
padding=1),
dy.BatchNorm(num_channels=128),
dy.Dropout(p=.2),
)
self.cnn3 = dy.Sequential(
dy.Conv2D(num_channels=128,
num_filters=128,
filter_size=3,
stride=1,
padding=1),
dy.BatchNorm(num_channels=128),
dy.Dropout(p=.2),
)
self.cls = dy.Sequential(
dy.Linear(input_dim=384, output_dim=128),
dy.Dropout(p=.2),
dy.Linear(input_dim=128, output_dim=5),
)
def __init__(self):
super(MNIST, self).__init__()
self.cnn = dy.Conv2D(num_channels=3,
num_filters=1,
filter_size=3,
stride=1,
padding=1,
act='relu')
self.cls = dy.Sequential(
dy.Linear(input_dim=784, output_dim=128),
dy.Dropout(p=.2),
dy.Linear(input_dim=128, output_dim=5),
)
def Dropout(p=0.5, inplace=False):
return dygraph.Dropout(p, dropout_implementation='upscale_in_train')