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MLP문제 푸는 경우,
임의의 A1 B1 C1 세개의 값을 알고 있다고 했을 때, 이것을 1이라고 가정한다면, A2 B2 C2 1 .... .... A100 A100 A100 36 으로 분류하는 데이터를 가지고 있습니다.
총 36개로 분류 하고싶은 데이터가 있는데, 본 경우에는, MLP input output을 어떻게 설정 해줘야 하나요?
class MLPModel(nn.Module): def init(self): super(MLPModel, self).init() self.linear1 = nn.Linear(in_features=3, out_features=200) self.linear2 = nn.Linear(in_features=200, out_features=1) self.relu = nn.ReLU()
def forward(self, x): x = self.linear1(x) x = self.relu(x) x = self.linear2(x) return x
1,2,3 의 예시만 들었지만, 답은 36개의 분류를 하고싶습니다. test_X = torch.tensor([[ 6.229247765, 6.164917938, 42.46821876], [ 6.318359321, 6.434637158, 41.73123234], [ 5.919044792, 5.935551197, 40.34761768], [ 5.932705898, 5.893793528, 40.39065692], [ 6.215735091, 2.769788416, 18.15682191], [ 6.356852727, 2.859113491, 18.87773254], [ 6.455439504, 2.926123325, 19.15900978], [ 6.380173194, 2.896036131, 18.18237821], [ 6.514274085, 1.431469088, 9.764890137], [ 6.582618314, 1.467246223, 9.654912427], [ 6.308791584, 1.402158543, 9.573039555], [ 6.505234242, 1.451120875, 9.285698667]]) test_Y = torch.tensor([ [0.0],[0.0],[0.0],[0.0],[1.0],[1.0],[1.0],[1.0],[2.0],[2.0],[2.0],[2.0] ])
The text was updated successfully, but these errors were encountered:
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MLP문제 푸는 경우,
임의의 A1 B1 C1 세개의 값을 알고 있다고 했을 때, 이것을 1이라고 가정한다면,
A2 B2 C2 1
....
....
A100 A100 A100 36 으로 분류하는 데이터를 가지고 있습니다.
class MLPModel(nn.Module):
def init(self):
super(MLPModel, self).init()
self.linear1 = nn.Linear(in_features=3, out_features=200)
self.linear2 = nn.Linear(in_features=200, out_features=1)
self.relu = nn.ReLU()
1,2,3 의 예시만 들었지만, 답은 36개의 분류를 하고싶습니다.
test_X = torch.tensor([[ 6.229247765, 6.164917938, 42.46821876],
[ 6.318359321, 6.434637158, 41.73123234],
[ 5.919044792, 5.935551197, 40.34761768],
[ 5.932705898, 5.893793528, 40.39065692],
[ 6.215735091, 2.769788416, 18.15682191],
[ 6.356852727, 2.859113491, 18.87773254],
[ 6.455439504, 2.926123325, 19.15900978],
[ 6.380173194, 2.896036131, 18.18237821],
[ 6.514274085, 1.431469088, 9.764890137],
[ 6.582618314, 1.467246223, 9.654912427],
[ 6.308791584, 1.402158543, 9.573039555],
[ 6.505234242, 1.451120875, 9.285698667]])
test_Y = torch.tensor([ [0.0],[0.0],[0.0],[0.0],[1.0],[1.0],[1.0],[1.0],[2.0],[2.0],[2.0],[2.0] ])
The text was updated successfully, but these errors were encountered: