def __init__(self):
super().__init__()
self.conv1 = create_conv(1, 3, 2, 1, -2)
self.conv2 = create_conv(1, 3, 2, 2, -2)
self.conv3 = create_conv(1, 3, 2, 3, -2)
self.relu = nn.ReLU()
self.conv4 = create_conv(3, 1, 3, 10, 0)
self.conv5 = create_conv(3, 1, 3, -10, 0)
def __init__(self):
super().__init__()
self.conv1 = create_conv(1, 8, 3, 1, -2, padding=1)
self.conv2 = create_conv(8, 8, 3, 2, -2, padding=1)
self.conv3 = create_conv(8, 8, 3, 3, -2, padding=1)
self.conv4 = create_conv(8, 1, 3, 10, 0, padding=1)
self.conv5 = create_conv(8, 1, 3, -10, 0, padding=1)
self.linear = nn.Linear(64, 10)
self.relu = nn.ReLU()
def __init__(self):
super().__init__()
self.conv1 = create_conv(1, 16, 2, 0, 1)
for i in range(16):
self.conv1.weight.data[i] += i
self.relu = nn.ReLU()
self.conv2 = create_conv(16, 32, 3, 20, 0)
for i in range(32):
self.conv2.weight.data[i] += i
self.bn = nn.BatchNorm2d(32)
self.conv3 = create_conv(32, 1, 5, 5, 1)
def __init__(self):
super().__init__()
# Usual conv
self.conv1 = create_conv(1, 3, 2, 9, -2)
self.relu = nn.ReLU()
# Depthwise conv
self.conv2 = nn.Conv2d(3, 3, 1, groups=3)
# Downsample conv
self.conv3 = create_conv(3, 8, 3, -10, 0, stride=2)
# Group conv
self.conv4 = nn.Conv2d(8, 4, 1, groups=4)
def __init__(self):
super().__init__()
self.conv1 = create_conv(1, 16, 2, 1, -2)
for i in range(16):
self.conv1.weight.data[i] += i
self.conv2 = create_conv(16, 32, 2, 2, -2)
self.conv3 = create_conv(16, 32, 2, 2, -2)
for i in range(32):
self.conv2.weight.data[i] += i
self.conv3.weight.data[i] += i
self.relu = nn.ReLU()
self.conv4 = create_conv(32, 16, 3, 10, 0)
for i in range(16):
self.conv4.weight.data[i] += i
def __init__(self):
super().__init__()
self.conv1 = create_conv(1, 1, 2, -1, -2)
self.fc = nn.Linear(3, 6)
def __init__(self):
super().__init__()
self.conv1 = create_conv(1, 2, 2, -1, -2)
self.conv2 = create_conv(1, 2, 2, -1, -2)
def __init__(self):
super().__init__()
self.conv1 = create_conv(1, 2, 2, 9, -2)
self.conv2 = create_conv(2, 1, 3, -10, 0)
def __init__(self):
super().__init__()
self.features = []
self.conv1 = create_conv(1, 2, 2, -1, -2)
self.conv2 = create_conv(1, 2, 2, -1, -2)
self.relu = nn.ReLU()