【动手学深度学习】softmax回归在实现过程中，无法在VSCode中绘制动图的解决办法（含源代码）
一、问题提出
李沐老师《动手学深度学习》这本书中，3.6节中涉及到softmax回归的从零开始实现，其中有一个绘制动图的程序，展示的效果很好。
我们看一下相应的代码：
class Animator:
def __init__(self, xlabel = None, ylabel = None, legend = None, xlim = None, ylim = None, xscale = 'linear', yscale = 'linear',\
fmts = ('-', 'm-', 'g-', 'r:'), nrows = 1, ncols = 1, figsize = (10, 6)):
if legend is None:
legend = []
d2l.use_svg_display()
self.fig, self.axes = d2l.plt.subplots(nrows, ncols, figsize = figsize)
if nrows * ncols == 1:
self.axes = [self.axes, ]
self.config_axes = lambda : d2l.set_axes(self.axes[0], xlabel, ylabel, xlim, ylim, xscale, yscale, legend)
self.X, self.Y, self.fmts = None, None, fmts
def add(self, x, y):
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()
display.display(self.fig)
display.clear_output(wait=True)
def train_ch3(net, train_iter, test_iter, loss, num_epochs, updater):
animator = Animator(xlabel='epoch', xlim=[1, num_epochs], ylim=[0.3, 0.9], legend=['train_loss', 'train_acc', 'test_acc'])
for epoch in range(num_epochs):
train_metrics = train_epoch_ch3(net, train_iter, loss, updater)
test_acc = evaluate_accuracy(net, test_iter)
animator.add(epoch + 1, train_metrics + (test_acc, ))
train_loss, train_acc = train_metrics
assert train_loss < 0.5, train_loss
assert train_loss <= 1 and train_acc > 0.7, train_loss
assert train_loss <= 1 and test_acc > 0.7, test_acc
lr = 0.1
def updater(batch_size):
return d2l.sgd([w, b], lr, batch_size)
num_epochs = 10
train_ch3(net, train_iter, test_iter, cross_entropy, num_epochs, updater)
但是源代码是在Jupyter中编写的，VSCode中无法正常显示。
<Figure size 1500x900 with 1 Axes>
<Figure size 1500x900 with 1 Axes>
<Figure size 1500x900 with 1 Axes>
<Figure size 1500x900 with 1 Axes>
<Figure size 1500x900 with 1 Axes>
<Figure size 1500x900 with 1 Axes>
<Figure size 1500x900 with 1 Axes>
<Figure size 1500x900 with 1 Axes>
<Figure size 1500x900 with 1 Axes>
<Figure size 1500x900 with 1 Axes>
<Figure size 1500x900 with 1 Axes>
<Figure size 1500x900 with 1 Axes>
<Figure size 1500x900 with 1 Axes>
<Figure size 1500x900 with 1 Axes>
<Figure size 1500x900 with 1 Axes>
二、尝试解决办法
2.1 尝试解决办法1：
需要在train_ch3(net,train_iter,test_iter,loss,num_epochs,updater)函数中添加d2l.plt.show()，将会显示最终结果：
这里无法显示动图，笔者尝试在循环中添加此函数，仍无法显示动图，并且只显示第一轮训练的结果，另外，如果显示的窗口不关掉，训练将会被中止。
这种方法行不通。
2.2 最终的解决方法
修改 d2l.Animator 类的部分代码：
- 在 Animator 类中的 add() 方法中添加两行代码：
plt.draw()
plt.pause(0.001)
- 在 Animator 添加 show() 方法：
def show(self):
display.display(self.fig)
三、最终的运行动图展示
此时我们的运行结果如下：
四、源代码
class Animator:
def __init__(self, xlabel = None, ylabel = None, legend = None, xlim = None, ylim = None, xscale = 'linear', yscale = 'linear',\
fmts = ('-', 'm-', 'g-', 'r:'), nrows = 1, ncols = 1, figsize = (10, 6)):
if legend is None:
legend = []
d2l.use_svg_display()
self.fig, self.axes = d2l.plt.subplots(nrows, ncols, figsize = figsize)
if nrows * ncols == 1:
self.axes = [self.axes, ]
self.config_axes = lambda : d2l.set_axes(self.axes[0], xlabel, ylabel, xlim, ylim, xscale, yscale, legend)
self.X, self.Y, self.fmts = None, None, fmts
def add(self, x, y):
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()
plt.draw()
plt.pause(0.001)
display.display(self.fig)
display.clear_output(wait=True)
def show(self):
display.display(self.fig)
def train_ch3(net, train_iter, test_iter, loss, num_epochs, updater):
animator = Animator(xlabel='epoch', xlim=[1, num_epochs], ylim=[0.3, 0.9], legend=['train_loss', 'train_acc', 'test_acc'])
for epoch in range(num_epochs):
train_metrics = train_epoch_ch3(net, train_iter, loss, updater)
test_acc = evaluate_accuracy(net, test_iter)
animator.add(epoch + 1, train_metrics + (test_acc, ))
train_loss, train_acc = train_metrics
assert train_loss < 0.5, train_loss
assert train_loss <= 1 and train_acc > 0.7, train_loss
assert train_loss <= 1 and test_acc > 0.7, test_acc
# animator.show()
lr = 0.1
def updater(batch_size):
return d2l.sgd([w, b], lr, batch_size)
num_epochs = 10
train_ch3(net, train_iter, test_iter, cross_entropy, num_epochs, updater)