用keras lstm写一个带有注意机制的例子，要用Attention

好的，这里是一个使用Keras LSTM和注意力机制的例子，实现对IMDB数据集进行情感分类。

首先，我们需要导入所需的库：

import numpy as np
from keras.models import Model
from keras.layers import Input, LSTM, Dense, Dropout, Embedding

然后，我们需要加载IMDB数据集：

from keras.datasets import imdb

max_features = 20000
(x_train, y_train), (x_test, y_test) = imdb.load_data(num_words=max_features)

maxlen = 80
x_train = sequence.pad_sequences(x_train, maxlen=maxlen)
x_test = sequence.pad_sequences(x_test, maxlen=maxlen)

接下来，我们将定义模型。在这里，我们将使用一个LSTM层和一个带有softmax激活函数的全连接层进行分类。此外，我们将添加一个注意力机制来提高模型的性能。

embedding_dim = 128
lstm_dim = 64

inputs = Input(shape=(maxlen,))
embed = Embedding(max_features, embedding_dim)(inputs)
lstm = LSTM(lstm_dim, return_sequences=True)(embed)

# 计算注意力权重
attention = Dense(1, activation='tanh')(lstm)
attention = Dropout(0.5)(attention)
attention = Dense(maxlen, activation='softmax')(attention)
attention = Dropout(0.5)(attention)

# 应用注意力权重
context = np.dot(attention, lstm)
output = Dense(1, activation='sigmoid')(context)

model = Model(inputs=inputs, outputs=output)

最后，我们编译并训练模型：

model.compile(loss='binary_crossentropy',
              optimizer='adam',
              metrics=['accuracy'])
model.fit(x_train, y_train,
          batch_size=32,
          epochs=10,
          validation_data=(x_test, y_test))

完整的代码如下所示：

import numpy as np
from keras.datasets import imdb
from keras.models import Model
from keras.layers import Input, LSTM, Dense, Dropout, Embedding
from keras.preprocessing import sequence

max_features = 20000
(x_train, y_train), (x_test, y_test) = imdb.load_data(num_words=max_features)

maxlen = 80
x_train = sequence.pad_sequences(x_train, maxlen=maxlen)
x_test = sequence.pad_sequences(x_test, maxlen=maxlen)

embedding_dim = 128
lstm_dim = 64

inputs = Input(shape=(maxlen,))
embed = Embedding(max_features, embedding_dim)(inputs)
lstm = LSTM(lstm_dim, return_sequences=True)(embed)

# 计算注意力权重
attention = Dense(1, activation='tanh')(lstm)
attention = Dropout(0.5)(attention)
attention = Dense(maxlen, activation='softmax')(attention)
attention = Dropout(0.5)(attention)

# 应用注意力权重
context = np.dot(attention, lstm)
output = Dense(1, activation='sigmoid')(context)

model = Model(inputs=inputs, outputs=output)
model.compile(loss='binary_crossentropy',
              optimizer='adam',
              metrics=['accuracy'])
model.fit(x_train, y_train,
          batch_size=32,
          epochs=10,
          validation_data=(x_test, y_test))