使用 TensorFlow 的多标签文本分类 [英] Multilabel Text Classification using TensorFlow

问题描述

文本数据被组织为具有 20,000 个元素的向量，例如 [2, 1, 0, 0, 5, ...., 0].第 i 个元素表示第 i 个词在文本中的出现频率.

The text data is organized as vector with 20,000 elements, like [2, 1, 0, 0, 5, ...., 0]. i-th element indicates the frequency of the i-th word in a text.

ground truth 标签数据也表示为具有 4,000 个元素的向量，如 [0, 0, 1, 0, 1, ...., 0].第 i 个元素指示第 i 个标签是否是文本的正标签.文本的标签数量因文本而异.

The ground truth label data is also represented as vector with 4,000 elements, like [0, 0, 1, 0, 1, ...., 0]. i-th element indicates whether the i-th label is a positive label for a text. The number of labels for a text differs depending on texts.

我有一个用于单标签文本分类的代码.

I have a code for single-label text classification.

如何编辑以下用于多标签文本分类的代码?

How can I edit the following code for multilabel text classification?

特别是，我想知道以下几点.

Especially, I would like to know following points.

• 如何使用 TensorFlow 计算准确度.
• 如何设置一个阈值来判断标签是正还是负.例如，如果输出是 [0.80, 0.43, 0.21, 0.01, 0.32] 并且基本事实是 [1, 1, 0, 0, 1]，那么分数超过 0.25 的标签应该被判断为正面.

谢谢.

import tensorflow as tf

# hidden Layer
class HiddenLayer(object):
    def __init__(self, input, n_in, n_out):
        self.input = input

        w_h = tf.Variable(tf.random_normal([n_in, n_out],mean = 0.0,stddev = 0.05))
        b_h = tf.Variable(tf.zeros([n_out]))

        self.w = w_h
        self.b = b_h
        self.params = [self.w, self.b]

    def output(self):
        linarg = tf.matmul(self.input, self.w) + self.b
        self.output = tf.nn.relu(linarg)

        return self.output

# output Layer
class OutputLayer(object):
    def __init__(self, input, n_in, n_out):
        self.input = input

        w_o = tf.Variable(tf.random_normal([n_in, n_out], mean = 0.0, stddev = 0.05))
        b_o = tf.Variable(tf.zeros([n_out]))

        self.w = w_o
        self.b = b_o
        self.params = [self.w, self.b]

    def output(self):
        linarg = tf.matmul(self.input, self.w) + self.b
        self.output = tf.nn.relu(linarg)

        return self.output

# model
def model():
    h_layer = HiddenLayer(input = x, n_in = 20000, n_out = 1000)
    o_layer = OutputLayer(input = h_layer.output(), n_in = 1000, n_out = 4000)

    # loss function
    out = o_layer.output()
    cross_entropy = -tf.reduce_sum(y_*tf.log(out + 1e-9), name='xentropy')    

    # regularization
    l2 = (tf.nn.l2_loss(h_layer.w) + tf.nn.l2_loss(o_layer.w))
    lambda_2 = 0.01

    # compute loss
    loss = cross_entropy + lambda_2 * l2

    # compute accuracy for single label classification task
    correct_pred = tf.equal(tf.argmax(out, 1), tf.argmax(y, 1))
    accuracy = tf.reduce_mean(tf.cast(correct_pred, "float"))

    return loss, accuracy

推荐答案

将relu改为sigmoid的输出层.将交叉熵损失修改为 sigmoid 交叉熵损失的显式数学公式(显式损失在我的案例/张量流版本中有效)

Change relu to sigmoid of output layer. Modify cross entropy loss to explicit mathematical formula of sigmoid cross entropy loss (explicit loss was working in my case/version of tensorflow )

import tensorflow as tf

# hidden Layer
class HiddenLayer(object):
    def __init__(self, input, n_in, n_out):
        self.input = input

        w_h = tf.Variable(tf.random_normal([n_in, n_out],mean = 0.0,stddev = 0.05))
        b_h = tf.Variable(tf.zeros([n_out]))

        self.w = w_h
        self.b = b_h
        self.params = [self.w, self.b]

    def output(self):
        linarg = tf.matmul(self.input, self.w) + self.b
        self.output = tf.nn.relu(linarg)

        return self.output

# output Layer
class OutputLayer(object):
    def __init__(self, input, n_in, n_out):
        self.input = input

        w_o = tf.Variable(tf.random_normal([n_in, n_out], mean = 0.0, stddev = 0.05))
        b_o = tf.Variable(tf.zeros([n_out]))

        self.w = w_o
        self.b = b_o
        self.params = [self.w, self.b]

    def output(self):
        linarg = tf.matmul(self.input, self.w) + self.b
        #changed relu to sigmoid
        self.output = tf.nn.sigmoid(linarg)

        return self.output

# model
def model():
    h_layer = HiddenLayer(input = x, n_in = 20000, n_out = 1000)
    o_layer = OutputLayer(input = h_layer.output(), n_in = 1000, n_out = 4000)

    # loss function
    out = o_layer.output()
    # modified cross entropy to explicit mathematical formula of sigmoid cross entropy loss
    cross_entropy = -tf.reduce_sum( (  (y_*tf.log(out + 1e-9)) + ((1-y_) * tf.log(1 - out + 1e-9)) )  , name='xentropy' )    

    # regularization
    l2 = (tf.nn.l2_loss(h_layer.w) + tf.nn.l2_loss(o_layer.w))
    lambda_2 = 0.01

    # compute loss
    loss = cross_entropy + lambda_2 * l2

    # compute accuracy for single label classification task
    correct_pred = tf.equal(tf.argmax(out, 1), tf.argmax(y, 1))
    accuracy = tf.reduce_mean(tf.cast(correct_pred, "float"))

    return loss, accuracy

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