keras utils标准化的目的是什么? [英] What is the purpose of keras utils normalize?

问题描述

我想在将训练集传递给我的NN之前对其进行归一化，所以我没有手动进行(减去均值并除以std)，而是尝试了keras.utils.normalize()，我对得到的结果感到惊讶. >

运行此:

r = np.random.rand(3000) * 1000
nr = normalize(r)
print(np.mean(r))
print(np.mean(nr))
print(np.std(r))
print(np.std(nr))
print(np.min(r))
print(np.min(nr))
print(np.max(r))
print(np.max(nr))

导致的结果:

495.60440066771866
0.015737914577213984
291.4440194021
0.009254802974329002
0.20755517410064872
6.590913227674956e-06
999.7631481267636
0.03174747238214018

不幸的是，文档不能解释幕后发生的事情.您能解释一下它的作用吗?如果我应该使用keras.utils.normalize而不是我应该手动进行的操作?

解决方案

这不是您期望的标准化类型.实际上，它使用 np.linalg.norm() 可以使用以下方法标准化给定数据Lp规范:

def normalize(x, axis=-1, order=2):
    """Normalizes a Numpy array.
    # Arguments
        x: Numpy array to normalize.
        axis: axis along which to normalize.
        order: Normalization order (e.g. 2 for L2 norm).
    # Returns
        A normalized copy of the array.
    """
    l2 = np.atleast_1d(np.linalg.norm(x, order, axis))
    l2[l2 == 0] = 1
    return x / np.expand_dims(l2, axis)

例如，在默认情况下，它将使用L2归一化对数据进行归一化(即元素的平方和等于1).

您可以使用此功能，或者如果您不想手动进行均值和标准差归一化，则可以使用 StandardScaler() .html"rel =" noreferrer> MinMaxScaler() .

I'd like to normalize my training set before passing it to my NN so instead of doing it manually (subtract mean and divide by std), I tried keras.utils.normalize() and I am amazed about the results I got.

Running this:

r = np.random.rand(3000) * 1000
nr = normalize(r)
print(np.mean(r))
print(np.mean(nr))
print(np.std(r))
print(np.std(nr))
print(np.min(r))
print(np.min(nr))
print(np.max(r))
print(np.max(nr))

​ ​Results in that:

495.60440066771866
0.015737914577213984
291.4440194021
0.009254802974329002
0.20755517410064872
6.590913227674956e-06
999.7631481267636
0.03174747238214018

Unfortunately, the docs don't explain what's happening under the hood. Can you please explain what it does and if I should use keras.utils.normalize instead of what I would have done manually?

解决方案

It is not the kind of normalization you expect. Actually, it uses np.linalg.norm() under the hood to normalize the given data using Lp-norms:

def normalize(x, axis=-1, order=2):
    """Normalizes a Numpy array.
    # Arguments
        x: Numpy array to normalize.
        axis: axis along which to normalize.
        order: Normalization order (e.g. 2 for L2 norm).
    # Returns
        A normalized copy of the array.
    """
    l2 = np.atleast_1d(np.linalg.norm(x, order, axis))
    l2[l2 == 0] = 1
    return x / np.expand_dims(l2, axis)

For example, in the default case, it would normalize the data using L2-normalization (i.e. the sum of squared of elements would be equal to one).

You can either use this function, or if you don't want to do mean and std normalization manually, you can use StandardScaler() from sklearn or even MinMaxScaler().

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