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()