def __init__(self):
self.C1 = Layer.ConvolutionalLayer([5, 5, 1, 6], pad="VALID", activation_function="SQUASHING")
self.S2 = Layer.PoolingLayer([2, 2, 6], mode="AVERAGE", activation_function="SQUASHING")
self.C3 = Layer.ConvolutionalCombinationLayer([5, 5, 16], get_combination_map(), pad="VALID", activation_function="SQUASHING")
self.S4 = Layer.PoolingLayer([2, 2, 16], mode="AVERAGE", activation_function="SQUASHING")
self.C5 = Layer.ConvolutionalLayer([5, 5, 16, 120], pad="VALID", activation_function="SQUASHING")
self.F6 = Layer.FullyConnectedLayer([120, 84], activation_function="SQUASHING")
self.RBF = Layer.RBFLayer(RBF_BITMAP.rbf_bitmap())
def __init__(self):
self.C1 = Layer.ConvolutionalLayer([11, 11, 3, 96],
pad="VALID",
stride=4,
activation_function="RELU",
initializer="ALEXNET_bias0")
self.N1 = Layer.LocalResponseNormalization(depth_radius=2,
bias=2,
alpha=1e-4,
beta=0.75)
self.S1 = Layer.PoolingLayer([3, 3, 96],
stride=2,
mode="MAX",
activation_function="LINEAR")
self.C2_1 = Layer.ConvolutionalLayer([5, 5, 48, 128],
pad="SAME",
activation_function="RELU")
self.C2_2 = Layer.ConvolutionalLayer([5, 5, 48, 128],
pad="SAME",
activation_function="RELU")
self.N2 = Layer.LocalResponseNormalization(depth_radius=2,
bias=2,
alpha=1e-4,
beta=0.75)
self.S2 = Layer.PoolingLayer([3, 3, 256],
stride=2,
mode="MAX",
activation_function="LINEAR")
self.C3 = Layer.ConvolutionalLayer([3, 3, 256, 384],
pad="SAME",
activation_function="RELU",
initializer="ALEXNET_bias0")
self.C4_1 = Layer.ConvolutionalLayer([3, 3, 192, 192],
pad="SAME",
activation_function="RELU")
self.C4_2 = Layer.ConvolutionalLayer([3, 3, 192, 192],
pad="SAME",
activation_function="RELU")
self.C5_1 = Layer.ConvolutionalLayer([3, 3, 192, 128],
pad="SAME",
activation_function="RELU")
self.C5_2 = Layer.ConvolutionalLayer([3, 3, 192, 128],
pad="SAME",
activation_function="RELU")
self.S5 = Layer.PoolingLayer([3, 3, 256],
stride=2,
mode="MAX",
activation_function="LINEAR")
self.F6 = Layer.FullyConnectedLayer([9216, 4096],
activation_function="RELU")
self.D6 = Layer.DropOut(keep_prob=0.5)
self.F7 = Layer.FullyConnectedLayer([4096, 4096],
activation_function="RELU")
self.D7 = Layer.DropOut(keep_prob=0.5)
self.F8 = Layer.FullyConnectedLayer([4096, 1000])
self.Output = Layer.Softmax()
