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, 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__()
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()
def __init__(self):
super().__init__()
self.conv1 = create_conv(1, 2, 2, -1, -2)
self.conv2 = create_conv(1, 2, 2, -1, -2)
def get_magnitude_test_model(input_shape):
inputs = tf.keras.Input(shape=input_shape)
x = create_conv(1, 2, 2, 9., -2.)(inputs)
outputs = create_conv(2, 1, 3, -10., 0.)(x)
return tf.keras.Model(inputs=inputs, outputs=outputs)