原文讲解
RCNN出处：论文Recurrent Convolutional Neural Networks for Text Classification
讲解可以参考TextRCNN 阅读笔记
网络结构
textRCNN网络结构.png
- Word Representation Learning. RCNN uses a recurrent structure, which is a bi-directional recurrent neural network, to capture the contexts. Then, combine the word and its context to present the word. And apply a linear transformation together with the
tanhactivation fucntion to the representation. - Text Representation Learning. When all of the representations of words are calculated, it applys a element-wise max-pooling layer in order to capture the most important information throughout the entire text. Finally, do the linear transformation and apply the softmax function.
本文实现
RCNN结构
定义网络结构
from tensorflow.keras import Input ,Model
from tensorflow.keras import backend as K
from tensorflow.layers import Embedding , Dense , SimpleRNN , Lambda , Concatenate , Conv1D , GlobalMaxPooling1D
class RCNN(object):
def __init__(self,maxlen , max_features , embedding_dims , class_num = 5 , last_activation = 'softmax'):
self.maxlen = maxlen
self.max_features = max_features
self.embedding_dims = embedding_dims
self.class_num = class_num
self.last_activation = last_activation
def get_model(self):
input_current = Input((self.maxlen,))
input_left = Input((self.maxlen,))
input_right = Input((self.maxlen,))
embedder = Embedding(self.max_features , self.embedding_dims, max_length = self.maxlen)
embedding_current = embedder(input_current)
embedding_left = embedder(input_left)
embedding_right = embedder(input_right)
x_left = SimpleRNN(128,return_sequence = True)(embedding_left)
x_right = SimpleRNN(128,return_sequence = True , go_backwards = True)(embedding_right)
x_right = Lambda(lambda x: K.reverse(x , axis = 1))(x_right)
x = Concatenate(axis = 2) ([x_left , embedding_current , x_right])
x = Conv1D(64,kernel_size = 1 , activation = 'tanh')(x)
x = GlobalMaxPooling1D(x)
output = Dense(self.class_num, activation = self.last_activation)(x)
model = Model(inputs = [input_current , input_left , input_right] , outputs = output)
return model
from tensorflow.keras.preprocessing import sequence
import random
from sklearn.model_selection import train_test_split
from tensorflow.keras.callbacks import EarlyStopping
from tensorflow.keras.utils import to_categorical
from utils import *
# 路径等配置
data_dir = "./processed_data"
vocab_file = "./vocab/vocab.txt"
vocab_size = 40000
# 神经网络配置
max_features = 40001
maxlen = 400
batch_size = 32
embedding_dims = 50
epochs = 10
print('数据预处理与加载数据...')
# 如果不存在词汇表，重建
if not os.path.exists(vocab_file):
build_vocab(data_dir, vocab_file, vocab_size)
# 获得 词汇/类别 与id映射字典
categories, cat_to_id = read_category()
words, word_to_id = read_vocab(vocab_file)
# 全部数据
x, y = read_files(data_dir)
data = list(zip(x,y))
del x,y
# 乱序
random.shuffle(data)
# 切分训练集和测试集
train_data, test_data = train_test_split(data)
# 对文本的词id和类别id进行编码
x_train = encode_sentences([content[0] for content in train_data], word_to_id)
y_train = to_categorical(encode_cate([content[1] for content in train_data], cat_to_id))
x_test = encode_sentences([content[0] for content in test_data], word_to_id)
y_test = to_categorical(encode_cate([content[1] for content in test_data], cat_to_id))
print('对序列做padding，保证是 samples*timestep 的维度')
x_train = sequence.pad_sequences(x_train, maxlen=maxlen)
x_test = sequence.pad_sequences(x_test, maxlen=maxlen)
print('x_train shape:', x_train.shape)
print('x_test shape:', x_test.shape)
print('为模型准备输入数据...')
x_train_current = x_train
x_train_left = np.hstack([np.expand_dims(x_train[:, 0], axis=1), x_train[:, 0:-1]])
x_train_right = np.hstack([x_train[:, 1:], np.expand_dims(x_train[:, -1], axis=1)])
x_test_current = x_test
x_test_left = np.hstack([np.expand_dims(x_test[:, 0], axis=1), x_test[:, 0:-1]])
x_test_right = np.hstack([x_test[:, 1:], np.expand_dims(x_test[:, -1], axis=1)])
print('x_train_current 维度:', x_train_current.shape)
print('x_train_left 维度:', x_train_left.shape)
print('x_train_right 维度:', x_train_right.shape)
print('x_test_current 维度:', x_test_current.shape)
print('x_test_left 维度:', x_test_left.shape)
print('x_test_right 维度:', x_test_right.shape)
print('构建模型...')
model = RCNN(maxlen, max_features, embedding_dims).get_model()
model.compile('adam', 'categorical_crossentropy', metrics=['accuracy'])
print('Train...')
early_stopping = EarlyStopping(monitor='val_accuracy', patience=2, mode='max')
history = model.fit([x_train_current, x_train_left, x_train_right], y_train,
batch_size=batch_size,
epochs=epochs,
callbacks=[early_stopping],
validation_data=([x_test_current, x_test_left, x_test_right], y_test))
print('Test...')
result = model.predict([x_test_current, x_test_left, x_test_right])
import matplotlib.pyplot as plt
plt.switch_backend('agg')
%matplotlib inline
fig1 = plt.figure()
plt.plot(history.history['loss'],'r',linewidth=3.0)
plt.plot(history.history['val_loss'],'b',linewidth=3.0)
plt.legend(['Training loss', 'Validation Loss'],fontsize=18)
plt.xlabel('Epochs ',fontsize=16)
plt.ylabel('Loss',fontsize=16)
plt.title('Loss Curves :CNN',fontsize=16)
fig1.savefig('loss_cnn.png')
plt.show()
fig2=plt.figure()
plt.plot(history.history['accuracy'],'r',linewidth=3.0)
plt.plot(history.history['val_accuracy'],'b',linewidth=3.0)
plt.legend(['Training Accuracy', 'Validation Accuracy'],fontsize=18)
plt.xlabel('Epochs ',fontsize=16)
plt.ylabel('Accuracy',fontsize=16)
plt.title('Accuracy Curves : CNN',fontsize=16)
fig2.savefig('accuracy_cnn.png')
plt.show()
from tensorflow.keras.utils import plot_model
plot_model(model, show_shapes=True, show_layer_names=True)