iris.py 文件源码

def learn_by_mini_batch(dataset: DataSet, batch_size: int = 25, n_iter: int = 5000) -> IrisChain:
    n = len(dataset.train)

    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 j in range(n_iter):
        shuffled = np.random.permutation(n)
        for i in range(0, n, batch_size):
            indices = shuffled[i:i+batch_size]
            x = Variable(x_train[indices])
            y = Variable(y_train[indices])
            model.cleargrads()
            loss = model(x, y)
            loss.backward()
            optimizer.update()

    return model