cbow模型代码实现

CBOW（Continuous Bag-of-Words）模型是一种用于自然语言处理的神经网络模型，它的输入是上下文中的词汇，输出是中心词汇。CBOW 模型的代码实现如下：

import numpy as np
from keras.preprocessing.text import Tokenizer
from keras.utils import to_categorical
from keras.models import Sequential
from keras.layers import Dense, Embedding, Lambda

# 设置超参数
vocab_size = 5000
embedding_dim = 100
window_size = 2

# 构建语料库
corpus = [
    'I like playing soccer with my friends',
    'He enjoys playing basketball on weekends',
    'The cat loves drinking milk every morning'
]

# 将语料库转化为词向量矩阵
tokenizer = Tokenizer(num_words=vocab_size)
tokenizer.fit_on_texts(corpus)
sequences = tokenizer.texts_to_sequences(corpus)

# 构建训练数据集
x_train = []
y_train = []
for sequence in sequences:
    for i in range(window_size, len(sequence) - window_size):
        context = sequence[i-window_size:i] + sequence[i+1:i+window_size+1]
        target = sequence[i]
        x_train.append(context)
        y_train.append(target)

x_train = np.array(x_train)
y_train = to_categorical(y_train, num_classes=vocab_size)

# 构建 CBOW 模型
model = Sequential()
model.add(Embedding(vocab_size, embedding_dim, input_length=window_size * 2))
model.add(Lambda(lambda x: np.mean(x, axis=1), output_shape=(embedding_dim,)))
model.add(Dense(vocab_size, activation='softmax'))
model.compile(loss='categorical_crossentropy', optimizer='adam')

# 训练 CBOW 模型
model.fit(x_train, y_train, epochs=50, verbose=1)

# 输出词向量矩阵
embeddings = model.get_weights()[0]
print(embeddings)

在上面的代码中，我们首先读取语料库，将其转化为词向量矩阵，并且根据窗口大小构建训练数据集。然后，我们构建 CBOW 模型，其中使用了 Embedding 层和 Lambda 层，最后将模型训练得到的词向量矩阵输出。