给我一份使用NNLM的词向量训练代码
时间: 2023-05-26 15:06:41 浏览: 166
下面是一个使用NNLM模型训练词向量的基本代码:
```
import tensorflow as tf
# 训练文本数据
corpus = [['I', 'love', 'chocolate', '.'], ['I', 'hate', 'spinach', '.'], ['I', 'like', 'ice', 'cream', '.']]
# 获取所有单词,并用整数编码
vocab = set(word for sentence in corpus for word in sentence)
word2idx = {word: idx for idx, word in enumerate(vocab)}
idx2word = {idx: word for idx, word in enumerate(vocab)}
# 构建NNLM模型
VOCAB_SIZE = len(vocab)
EMBED_SIZE = 50
HIDDEN_SIZE = 100
WINDOW_SIZE = 2
BATCH_SIZE = 32
NUM_SAMPLED = 64
LEARNING_RATE = 0.01
inputs = tf.keras.layers.Input(shape=(WINDOW_SIZE * 2,))
embeddings = tf.keras.layers.Embedding(VOCAB_SIZE, EMBED_SIZE)(inputs)
reshape = tf.keras.layers.Reshape((EMBED_SIZE * WINDOW_SIZE * 2,))(embeddings)
hidden = tf.keras.layers.Dense(HIDDEN_SIZE, activation='relu')(reshape)
output = tf.keras.layers.Dense(VOCAB_SIZE)(hidden)
model = tf.keras.models.Model(inputs=inputs, outputs=output)
# 编译模型
model.compile(loss=tf.keras.losses.CategoricalCrossentropy(from_logits=True),
optimizer=tf.keras.optimizers.Adam(LEARNING_RATE))
# 准备训练数据
train_data = []
labels = []
for sentence in corpus:
for i, word in enumerate(sentence):
for j in range(i - WINDOW_SIZE, i + WINDOW_SIZE + 1):
if j < 0 or j >= len(sentence) or i == j:
continue
train_data.append([word2idx[word], word2idx[sentence[j]]])
labels.append(word2idx[sentence[i]])
train_data = tf.data.Dataset.from_tensor_slices((train_data, labels)).batch(BATCH_SIZE).repeat()
# 训练模型
model.fit(train_data, epochs=1000, steps_per_epoch=len(train_data))
# 获取词向量
embeddings = model.get_layer('embedding').get_weights()[0]
```
在此代码中,我们使用了tensorflow.keras框架来构建和训练NNLM模型。我们首先获取所有单词并用整数编码,然后定义模型的超参数(例如嵌入大小、隐藏层大小、窗口大小等)和优化器,以及将文本数据转换为模型输入格式的准备。我们然后使用训练数据来拟合模型,并最终获得单词的嵌入向量。请注意,该代码只是一个基本框架,可以根据具体需求进行修改。
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