keras text classification

最新推荐文章于 2024-11-29 14:25:30 发布

原创最新推荐文章于 2024-11-29 14:25:30 发布 · 1.1k 阅读

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本文详细介绍文本分类任务的各种方法和技术，包括卷积神经网络(CNN)、注意力机制的循环神经网络(Attentional RNN)及层级注意力网络(Hierarchical Attention Network)，并提供基于Keras的实际案例与调参技巧。同时探讨了多标签分类的处理方式。

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1 Text Classification, Part I - Convolutional Networks

2 Text Classification, Part 2 - sentence level Attentional RNN

3 Text Classification, Part 3 - Hierarchical attention network

4 100行深度学习文本分类

5 CNN中文文本分类

6 DNN文本分类实践

7 用8千万评论训练的char-rnn做情感分析

8 词向量/CNN电影评论情感分类

9 浅层词级CNN vs. 深层字符级CNN 文本分类对比研究

10 一篇文章带你看懂 Keras 文本分类实现

11 How does Keras handle multilabel classification?

12 keras 调参，优化，一些设置等

13 Python/Keras - accessing ModelCheckpoint callback

14 深度学习框架Keras使用心得

15 指定GPU并且限制GPU用量

import os
import tensorflow as tf
import keras.backend.tensorflow_backend as KTF

# 指定第一块GPU可用 
os.environ["CUDA_VISIBLE_DEVICES"] = "0"

config = tf.ConfigProto()  
config.gpu_options.allow_growth=True   #不全部占满显存, 按需分配
sess = tf.Session(config=config)

KTF.set_session(sess)

16 Neural network for multi label classification with large number of classes outputs only zero

If you use the tensorflow backend in Keras, you can use the loss function like this (Keras 2.1.1):

import tensorflow as tf
import keras.backend.tensorflow_backend as tfb

POS_WEIGHT = 10  # multiplier for positive targets, needs to be tuned

def weighted_binary_crossentropy(target, output):
    """
    Weighted binary crossentropy between an output tensor 
    and a target tensor. POS_WEIGHT is used as a multiplier 
    for the positive targets.

    Combination of the following functions:
    * keras.losses.binary_crossentropy
    * keras.backend.tensorflow_backend.binary_crossentropy
    * tf.nn.weighted_cross_entropy_with_logits
    """
    # transform back to logits
    _epsilon = tfb._to_tensor(tfb.epsilon(), output.dtype.base_dtype)
    output = tf.clip_by_value(output, _epsilon, 1 - _epsilon)
    output = tf.log(output / (1 - output))
    # compute weighted loss
    loss = tf.nn.weighted_cross_entropy_with_logits(targets=target,
                                                    logits=output,
                                                    pos_weight=POS_WEIGHT)
    return tf.reduce_mean(loss, axis=-1)
Then in your model:

model.compile(loss=weighted_binary_crossentropy, ...)