谁能举一个小例子来解释 tf.random.categorical 的参数? [英] can anyone give a tiny example to explain the params of tf.random.categorical?

问题描述

tensorflow 的网站给出了这个例子

tensorflow's site gives this example

tf.random.categorical(tf.log([[10., 10.]]), 5)

产生一个形状为 [1, 5] 的张量，其中每个值为 0 或 1 的概率相等"

produces a tensor that "has shape [1, 5], where each value is either 0 or 1 with equal probability"

我已经知道，基本的demo的含义tf.log([[10., 10.]]).

I have already known, the basic demo, the meaning of tf.log([[10., 10.]]).

我想知道 [batch_size, num_classes] 是做什么的，谁能举一个小例子来解释参数?

what I want to know is what does [batch_size, num_classes] do, can anyone give a tiny example to explain the params?

推荐答案

如您所见，tf.random.categorical 有两个参数:

As you note, tf.random.categorical takes two parameters:

• logits，一个形状为 [batch_size, num_classes]
的二维浮点张量
• num_samples，一个整数标量.

• logits, a 2D float tensor with shape [batch_size, num_classes]
• num_samples, an integer scalar.

输出是一个形状为 [batch_size, num_samples] 的二维整数张量.

The output is a 2D integer tensor with shape [batch_size, num_samples].

logits 张量的每一行" (logits[0, :], logits[1, :], ...) 表示不同分类分布的事件概率.不过，该函数并不期望实际概率值，而是期望未归一化的对数概率；所以实际概率将是 softmax(logits[0, :]), softmax(logits[1, :]) 等.这样做的好处是你可以基本上给出任何实际值作为输入(例如神经网络的输出)，它们将是有效的.此外，使用对数来使用特定的概率值或比例也很简单.例如，[log(0.1), log(0.3), log(0.6)] 和 [log(1), log(3), log(6)] 表示相同的概率，其中第二类的可能性是第一类的三倍，但只有第三类的一半.

Each "row" of the logits tensor (logits[0, :], logits[1, :], ...) represents the event probabilities of a different categorical distribution. The function does not expect actual probability values, though, but unnormalized log-probabilities; so the actual probabilities would be softmax(logits[0, :]), softmax(logits[1, :]), etc. The benefit of this is that you can give basically any real values as input (e.g. the output of a neural network) and they will be valid. Also, it's trivial to use specific probability values, or proportions, using logarithms. For example, both [log(0.1), log(0.3), log(0.6)] and [log(1), log(3), log(6)] represent the same probability, where the second class is three times as likely as the first one but only half as likely as the third one.

对于(非标准化对数)概率的每一行，您从分布中获得 num_samples 个样本.每个样本都是一个介于 0 和 num_classes - 1 之间的整数，根据给定的概率绘制.因此，结果是形状为 [batch_size, num_samples] 的二维张量，每个分布的采样整数.

For each row of (unnormalized log-)probabilities, you get num_samples samples from the distribution. Each sample is an integer between 0 and num_classes - 1, drawn according to the given probabilities. So, the result is the 2D tensor with shape [batch_size, num_samples] with the sampled integers for each distribution.

函数的一个小例子.

import tensorflow as tf

with tf.Graph().as_default(), tf.Session() as sess:
    tf.random.set_random_seed(123)
    logits = tf.log([[1., 1., 1., 1.],
                     [0., 1., 2., 3.]])
    num_samples = 30
    cat = tf.random.categorical(logits, num_samples)
    print(sess.run(cat))
    # [[3 3 1 1 0 3 3 0 2 3 1 3 3 3 1 1 0 2 2 0 3 1 3 0 1 1 0 1 3 3]
    #  [2 2 3 3 2 3 3 3 2 2 3 3 2 2 2 1 3 3 3 2 3 2 2 1 3 3 3 3 3 2]]

在这种情况下，结果是一个两行 30 列的数组.第一行中的值是从分类分布中采样的，其中每个类 ([0, 1, 2, 3]) 具有相同的概率.在第二行中，3 类是最有可能的，0 类没有被采样的概率.

In this case, the result is an array with two rows and 30 columns. The values in the first row are sampled from a categorical distribution where every class ([0, 1, 2, 3]) has the same probability. In the second row, the class 3 is the most likely one, and class 0 has just no probability of being sampled.

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