LSTM句子生成算法
时间: 2023-11-25 11:49:51 浏览: 37
以下是使用LSTM网络生成句子的Python代码示例:
```python
import numpy as np
from keras.models import Sequential
from keras.layers import Dense, LSTM, Dropout
# 定义训练数据
data = "The quick brown fox jumps over the lazy dog"
chars = sorted(list(set(data)))
char_to_int = dict((c, i) for i, c in enumerate(chars))
seq_length = 5
dataX = []
dataY = []
for i in range(0, len(data) - seq_length, 1):
seq_in = data[i:i + seq_length]
seq_out = data[i + seq_length]
dataX.append([char_to_int[char] for char in seq_in])
dataY.append(char_to_int[seq_out])
n_patterns = len(dataX)
X = np.reshape(dataX, (n_patterns, seq_length, 1))
X = X / float(len(chars))
y = np_utils.to_categorical(dataY)
# 定义LSTM模型
model = Sequential()
model.add(LSTM(256, input_shape=(X.shape[1], X.shape[2]), return_sequences=True))
model.add(Dropout(0.2))
model.add(LSTM(256))
model.add(Dropout(0.2))
model.add(Dense(y.shape[1], activation='softmax'))
model.compile(loss='categorical_crossentropy', optimizer='adam')
# 训练模型
model.fit(X, y, epochs=50, batch_size=64)
# 使用模型生成句子
start = np.random.randint(0, len(dataX)-1)
pattern = dataX[start]
print("Seed:")
print("\"", ''.join([chars[value] for value in pattern]), "\"")
for i in range(50):
x = np.reshape(pattern, (1, len(pattern), 1))
x = x / float(len(chars))
prediction = model.predict(x, verbose=0)
index = np.argmax(prediction)
result = chars[index]
seq_in = [chars[value] for value in pattern]
print(result, end='')
pattern.append(index)
pattern = pattern[1:len(pattern)]
print("\nDone.")
```
该代码使用LSTM网络生成一个由给定字符串开始的新句子。在这个例子中,我们使用了一个包含256个神经元的LSTM层,并在每个LSTM层之后添加了一个20%的Dropout层。模型使用categorical_crossentropy作为损失函数,并使用adam优化器进行训练。在训练完成后,我们使用模型生成一个新的句子。