程序源码
时间: 2023-07-24 08:01:13 浏览: 81
程序源代码
由于篇幅限制,无法提供完整的程序源码。以下是一个简单的示例代码,用于演示如何使用Python和Keras库实现文本分类任务:
```python
# 导入必要的库
import numpy as np
from keras.preprocessing.text import Tokenizer
from keras.preprocessing.sequence import pad_sequences
from keras.models import Sequential
from keras.layers import Embedding, Conv1D, MaxPooling1D, LSTM, Dense, Dropout
from keras.optimizers import Adam
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score
# 加载数据集
data = load_data()
# 分词和向量化
tokenizer = Tokenizer(num_words=MAX_WORDS)
tokenizer.fit_on_texts(data['text'])
sequences = tokenizer.texts_to_sequences(data['text'])
word_index = tokenizer.word_index
x = pad_sequences(sequences, maxlen=MAX_LEN)
# 标签转换为数字
y = data['label'].map({'positive': 1, 'negative': 0}).values
# 划分训练集和测试集
x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=TEST_SIZE, random_state=RANDOM_STATE)
# 构建模型
model = Sequential()
model.add(Embedding(input_dim=MAX_WORDS, output_dim=EMBEDDING_DIM, input_length=MAX_LEN))
model.add(Conv1D(filters=NUM_FILTERS, kernel_size=FILTER_SIZE, activation='relu'))
model.add(MaxPooling1D(pool_size=POOL_SIZE))
model.add(LSTM(units=NUM_UNITS))
model.add(Dense(units=NUM_CLASSES, activation='softmax'))
model.compile(optimizer=Adam(lr=LEARNING_RATE), loss='categorical_crossentropy', metrics=['accuracy'])
# 训练模型
model.fit(x_train, y_train, batch_size=BATCH_SIZE, epochs=NUM_EPOCHS, validation_data=(x_test, y_test))
# 预测结果
y_pred = model.predict(x_test)
y_pred = np.argmax(y_pred, axis=1)
accuracy = accuracy_score(y_test, y_pred)
precision = precision_score(y_test, y_pred)
recall = recall_score(y_test, y_pred)
f1 = f1_score(y_test, y_pred)
print('Accuracy: {:.2f}%'.format(accuracy * 100))
print('Precision: {:.2f}%'.format(precision * 100))
print('Recall: {:.2f}%'.format(recall * 100))
print('F1 Score: {:.2f}%'.format(f1 * 100))
```
该代码使用了一个基于卷积神经网络和长短时记忆网络的模型,包括Embedding层、Conv1D层、MaxPooling1D层、LSTM层和Dense层。模型的训练过程包括数据预处理、模型构建、模型编译和模型训练等步骤。最后,使用测试集对模型进行评估,并输出准确率、精确率、召回率和F1得分等指标。
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