构建textcnn分类模型

好的，下面是构建textcnn分类模型的步骤：

1. 导入必要的库和数据集：

import tensorflow as tf
from tensorflow.keras.preprocessing.text import Tokenizer
from tensorflow.keras.preprocessing.sequence import pad_sequences
from tensorflow.keras.layers import Embedding, Conv1D, MaxPooling1D, Flatten, Dense
from sklearn.datasets import fetch_20newsgroups

# 加载20个新闻组数据集
newsgroups_train = fetch_20newsgroups(subset='train', shuffle=True)
newsgroups_test = fetch_20newsgroups(subset='test', shuffle=True)

# 将文本向量化
tokenizer = Tokenizer(num_words=5000)
tokenizer.fit_on_texts(newsgroups_train.data)
X_train = tokenizer.texts_to_sequences(newsgroups_train.data)
X_test = tokenizer.texts_to_sequences(newsgroups_test.data)

# 填充序列，使所有文本长度相同
maxlen = 100
X_train = pad_sequences(X_train, padding='post', maxlen=maxlen)
X_test = pad_sequences(X_test, padding='post', maxlen=maxlen)
y_train = newsgroups_train.target
y_test = newsgroups_test.target

2. 定义模型架构：

model = tf.keras.Sequential([
    Embedding(input_dim=5000, output_dim=32, input_length=maxlen),
    Conv1D(filters=32, kernel_size=3, padding='same', activation='relu'),
    MaxPooling1D(pool_size=2),
    Flatten(),
    Dense(units=20, activation='softmax')
])

3. 编译模型：

model.compile(loss='sparse_categorical_crossentropy', optimizer='adam', metrics=['accuracy'])

4. 训练模型：

model.fit(X_train, y_train, epochs=10, batch_size=32, validation_data=(X_test, y_test))

5. 评估模型：

loss, accuracy = model.evaluate(X_test, y_test)
print('Test accuracy:', accuracy)

以上就是使用textcnn构建分类模型的完整代码。