l1=L.Linear(4, 6),
l2=L.Linear(6, 3),
)
def __call__(self, x, y):
return F.mean_squared_error(self.fwd(x), y)
def fwd(self, x):
h1 = F.sigmoid(self.l1(x))
h2 = self.l2(h1)
return h2
if __name__ == "__main__":
# irisのデータセット
iris = datasets.load_iris()
X = iris.data.astype(np.float32)
Y = iris.target
N = Y.size
Y2 = np.zeros(3 * N).reshape(N, 3).astype(np.float32)
for i in range(N):
Y2[i, Y[i]] = 1.0
index = np.arange(N)
xtrain = X[index[index % 2 != 0], :]
ytrain = Y2[index[index % 2 != 0], :]
xtest = X[index[index % 2 == 0], :]
yans = Y[index[index % 2 == 0]]
train = tuple_dataset.TupleDataset(xtrain, ytrain)
# モデルの生成
model = IrisChain()
def getData():
# アヤメの品種データを読み込む
iris = datasets.load_iris()
return model_selection.train_test_split(iris.data.astype(np.float32),
iris.target)