Question

我尝试将悬停效果应用于具有类button的所有dialog-btn代码。我已经尝试.dialog-btn:hover{background-color:gold}，但这不起作用。我也尝试过类似问题的其他建议，但仍然没有运气。有人可以澄清一下我是怎么做到的吗？

以下两个例子都不起作用。

＆＃13;

button.dialog-btn:hover {
  background-color: gold;
}

＆＃13;

<div class="dialog-btns">
  <button class="dialog-btn" id="yes">Ref Match</button>
  <button class="dialog-btn" id="about">About</button>
</div>

＆＃13;

.dialog-btn:hover {
  background-color: gold;
}

＆＃13;

<div class="dialog-btns">
  <button class="dialog-btn" id="yes">Ref Match</button>
  <button class="dialog-btn" id="about">About</button>
</div>

＆＃13;

编辑2：

 #yes{
    background-color:green;
 }
 #about{
    background-color:purple;
 }

上面的代码似乎覆盖了上面的.dialog-btn:hover代码。那是为什么？

Answer 1

阅读你的评论，我想如果你将鼠标悬停在parrent元素上，你想要在所有按钮上都有金色。

如果是这种情况，你可以这样做

import numpy as np
import random
import copy

def gradcheck_naive(f, x):
    """ Gradient check for a function f.

    Arguments:
    f -- a function that takes a single argument (x) and outputs the
         cost (fx) and its gradients grad
    x -- the point (numpy array) to check the gradient at
    """
    rndstate = random.getstate()
    random.setstate(rndstate)
    fx, grad = f(x) # Evaluate function value at original point
    #fx=cost
    #grad=gradient
    h = 1e-4        
    # Iterate over all indexes in x
    it = np.nditer(x, flags=['multi_index'], op_flags=['readwrite'])
    while not it.finished:
        ix = it.multi_index #multi-index number

        random.setstate(rndstate)
        xp = copy.deepcopy(x)
        xp[ix] += h
        fxp, gradp = f(xp) 

        random.setstate(rndstate)
        xn = copy.deepcopy(x)
        xn[ix] -= h
        fxn, gradn = f(xn) 
        numgrad = (fxp-fxn) / (2*h)

        # Compare gradients
        reldiff = abs(numgrad - grad[ix]) / max(1, abs(numgrad), abs(grad[ix]))
        if reldiff > 1e-5:
            print ("Gradient check failed.")
            print ("First gradient error found at index %s" % str(ix))
            print ("Your gradient: %f \t Numerical gradient: %f" % (
                grad[ix], numgrad))
            return

        it.iternext() # Step to next dimension

    print ("Gradient check passed!")

#sanity check with 1D function
exp_f = lambda x: (np.sum(np.exp(x)), np.exp(x))
gradcheck_naive(exp_f, np.random.randn(4,5)) #this works fine

#sanity check with matrices
#forward pass
W = np.random.randn(5,10)
x = np.random.randn(10,3)
D = W.dot(x)

#backpropagation pass
gradx = W

func_f = lambda x: (np.sum(W.dot(x)), gradx)
gradcheck_naive(func_f, np.random.randn(10,3)) #this does not work (grad check fails)

应用：将样式悬停在具有相同类的所有元素上

1 个答案: