d2l函数解析

在学习李沐老师的课时，需要下载d2l库，但有的同学没有下载，所以在此我进行库中一些函数的总结，持续更新！

def use_svg_display():  #@save
    """使用svg格式在Jupyter中显示绘图"""
    backend_inline.set_matplotlib_formats('svg')

def set_figsize(figsize=(3.5, 2.5)):  #@save
    """设置matplotlib的图表大小"""
    use_svg_display()
    plt.rcParams['figure.figsize'] = figsize
    
def set_axes(axes, xlabel, ylabel, xlim, ylim, xscale, yscale, legend):
    """设置matplotlib的轴"""
    axes.set_xlabel(xlabel)
    axes.set_ylabel(ylabel)
    axes.set_xscale(xscale)
    axes.set_yscale(yscale)
    axes.set_xlim(xlim)
    axes.set_ylim(ylim)
    if legend:
        axes.legend(legend)
    axes.grid()

def plot(X, Y=None, xlabel=None, ylabel=None, legend=None, xlim=None,
         ylim=None, xscale='linear', yscale='linear',
         fmts=('-', 'm--', 'g-.', 'r:'), figsize=(3.5, 2.5), axes=None):
    """绘制数据点"""
    if legend is None:
        legend = []

    set_figsize(figsize)
    axes = axes if axes else plt.gca()

    # 如果X有一个轴，输出True
    def has_one_axis(X):
        return (hasattr(X, "ndim") and X.ndim == 1 or isinstance(X, list)
                and not hasattr(X[0], "__len__"))

    if has_one_axis(X):
        X = [X]
    if Y is None:
        X, Y = [[]] * len(X), X
    elif has_one_axis(Y):
        Y = [Y]
    if len(X) != len(Y):
        X = X * len(Y)
    axes.cla()
    for x, y, fmt in zip(X, Y, fmts):
        if len(x):
            axes.plot(x, y, fmt)
        else:
            axes.plot(y, fmt)
    set_axes(axes, xlabel, ylabel, xlim, ylim, xscale, yscale, legend)

class Accumulator:
    def __init__(self, n):
        self.data=[0.0]*n
    
    def add(self, *args):
        self.data=[a+float(b) for a, b in zip(self.data, args)]
        
    def reset(self):
        self.data=[0.0]*len(self.data)
        
    def __getitem__(self, idx):
        return self.data[idx]

def evaluate_loss(net, data_iter, loss):
    metric=Accumulator(2)
    for x, y in data_iter:
        out=net(x)
        y=y.reshape(out.shape)
        l=loss(out, y)
        metric.add(l.sum(), l.numel())
    return metric[0]/metric[1]

def load_array(data_arrays, batch_size, is_train=True):
    """Construct a PyTorch data iterator.

    Defined in :numref:`sec_linear_concise`"""
    dataset = TensorDataset(*data_arrays)
    return DataLoader(dataset, batch_size, shuffle=is_train)