def learn(dataset: DataSet, n_iter: int = 10000) -> IrisChain:
model = IrisChain()
optimizer = optimizers.Adam()
optimizer.setup(model)
x_train = dataset.train.drop('class', axis=1).values
y_train = to_hot_vector(dataset.train['class']).values
for i in range(n_iter):
model.cleargrads()
x = Variable(x_train)
y = Variable(y_train)
loss = model(x, y)
loss.backward()
optimizer.update()
return model
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