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')