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dive_into_deep_learning's Issues

提个问题,ch02的04

为什么optimizer在每个batch之后不需要把梯度清零,并且可以得到正确结果
我写了个类似的代码,但是不加optimizer.zero_gard()会导致正确率越训越低直到10%

如何处理d2l中的jupyter魔术命令?

你好,我想在pycharm中使用d2l包(pytorch版)进行开发,但是由于目前直接pip下来的是适用于jupyter的,有一些魔术命令无法运行,有什么解决办法吗?十分感谢,例如:d2l/torch中有from matplotlib_inline import backend_inline这个命令,想请教下是如何处理的

提出另一种实现pycharm的方法,可直接使用原课程代码

尝试更改了 Animator 累加器的 add 方法实现使用 Matplotlib 输出图表
代码如下

    def add(self, x, y):
        # Add multiple data points into the figure
        if not hasattr(y, "__len__"):
            y = [y]
        n = len(y)
        if not hasattr(x, "__len__"):
            x = [x] * n
        if not self.X:
            self.X = [[] for _ in range(n)]
        if not self.Y:
            self.Y = [[] for _ in range(n)]
        for i, (a, b) in enumerate(zip(x, y)):
            if a is not None and b is not None:
                self.X[i].append(a)
                self.Y[i].append(b)
        self.axes[0].cla()
        for x, y, fmt in zip(self.X, self.Y, self.fmts):
            self.axes[0].plot(x, y, fmt)
        self.config_axes()
        d2l.plt.pause(0.01)

主要添加了 d2l.plt.pause(0.01) 来显示图片,我在权重衰减从零实现中测试正常

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