Python实现绘制多种激活函数曲线详解

pip install numpy matplotlib sympy

"python.linting.flake8Enabled": true, // 规范检查工具
"python.formatting.provider": "yapf", // 格式化工具
"python.linting.flake8Args": ["--max-line-length=248"], // 设置单行最长字符限制
"python.linting.pylintEnabled": false, // 关闭pylint工具

from sympy import symbols, evalf, diff
# 我们先要定义自变量是什么，这边按需求来，这是文档的例子有两个变量
x, y = symbols('x y')
# 然后我们写出函数表达式
expr = x + 2*y
# 输出看一下是什么东西
expr # x + 2*y
# 接着就要用我们定义的函数了
expr.evalf(subs={x: 10, y: 20}) # 50.000000
# 再对我们的函数求导
diff(expr, x, 1) # 对x进行求导得出结果 1，这也是表达式

def sigmoid():
    """
    定义sigmoid函数
    """
    x = symbols('x')
    return 1. / (1 + exp(-x))

def tanh():
    """
    定义tanh函数
    """
    x = symbols('x')
    return (exp(x) - exp(-x)) / (exp(x) + exp(-x))

def relu():
    """
    定义ReLU函数
    """
    x = symbols('x')
    return Piecewise((0, x < 0), (x, x >= 0))

def leakyRelu():
    """
    定义Leaky ReLu函数
    """
    x = symbols('x')
    return Piecewise((0.1 * x, x < 0), (x, x >= 0))

def softMax(x: np.ndarray):
    """
    定义SoftMax函数\n
    """
    exp_x = np.exp(x)
    print(exp_x, np.sum(exp_x))
    return exp_x / np.sum(exp_x)

def softmax_derivative(x):
    """
    定义SoftMax导数函数\n
    x - 输入x向量
    """
    s = softMax(x)
    return s * (1 - s)

def derivate(formula, len, variate):
    """
    定义函数求导
      formula：函数公式
      len：求导次数
      variate：自变量
    """
    return diff(formula, variate, len)

x = np.linspace(0, 2, 100)  # Sample data.

# Note that even in the OO-style, we use `.pyplot.figure` to create the Figure.
fig, ax = plt.subplots(figsize=(5, 2.7), layout='constrained')
ax.plot(x, x, label='linear')  # Plot some data on the axes.
ax.plot(x, x**2, label='quadratic')  # Plot more data on the axes...
ax.plot(x, x**3, label='cubic')  # ... and some more.
ax.set_xlabel('x label')  # Add an x-label to the axes.
ax.set_ylabel('y label')  # Add a y-label to the axes.
ax.set_title("Simple Plot")  # Add a title to the axes.
ax.legend()  # Add a legend.

plt.xlabel('x label') // 两种方式加label，一种为ax.set_xlabel（面向对象），一种就是这种（面向函数）
plt.ylabel('y label')

plt.subplot(2, 2, 1, adjustable='box') # 1行1列
plt.subplot(2, 2, 2, adjustable='box') # 1行2列

do = input( 'input function expression what you want draw(sigmoid, tanh, relu, leakyRelu, softMax)\n' )

 try:
        plt.xlabel('x label')
        plt.ylabel('y label')
        plt.title(do)
        if (do == 'softMax'):
            plt.plot(num, softMax(num), label='Softmax')
            plt.plot(num, softmax_derivative(num), label='Softmax Derivative')
        else:
            plt.plot(
                num,
                [eval(f'{do}()').evalf(subs={symbols("x"): i}) for i in num])
            plt.plot(num, [
                derivate(eval(f'{do}()'), 1, 'x').evalf(subs={symbols('x'): i})
                for i in num
            ])

        plt.tight_layout()
        plt.show()
    except TypeError:
        print(
            'input function expression is wrong or the funciton is not configured'
        )