CNN-LSTM-Attention算法代码

以下是CNN-LSTM-Attention算法的代码示例：

import tensorflow as tf

# 定义输入层
inputs = tf.keras.layers.Input(shape=(None, 128, 128, 3))

# 定义卷积层
conv1 = tf.keras.layers.Conv3D(filters=32, kernel_size=(3, 3, 3), activation='relu')(inputs)
conv2 = tf.keras.layers.Conv3D(filters=64, kernel_size=(3, 3, 3), activation='relu')(conv1)

# 定义LSTM层
lstm1 = tf.keras.layers.TimeDistributed(tf.keras.layers.LSTM(128))(conv2)
stm2 = tf.keras.layers.LSTM(128)(lstm1)

# 定义Attention层
attention = tf.keras.layers.Dense(128, activation='tanh')(lstm2)
attention = tf.keras.layers.Dense(1, activation='softmax')(attention)
attention = tf.keras.layers.Flatten()(attention)
attention = tf.keras.layers.RepeatVector(128)(attention)
attention = tf.keras.layers.Permute([2, 1])(attention)

# 将LSTM层和Attention层相乘
multiply_layer = tf.keras.layers.Multiply()([lstm1, attention])

# 定义输出层
output_layer = tf.keras.layers.Dense(10, activation='softmax')(multiply_layer)

# 定义模型
model = tf.keras.models.Model(inputs=inputs, outputs=output_layer)