下面是一个使用TensorFlow深度学习框架构建的带有自注意力机制的卷积神经网络(Self-Attention Convolutional Neural Network)的示例代码。此示例用于训练和预测一维数据集的标签。
python
import tensorflow as tf
def scaled_dot_product_attention(Q, K, V):
dk = tf.cast(tf.shape(K)[-1], tf.float32)
dot_product = tf.matmul(Q, K, transpose_b=True)
scaled_dot_product = dot_product / tf.math.sqrt(dk)
attention_weights = tf.nn.softmax(scaled_dot_product, axis=-1)
output = tf.matmul(attention_weights, V)
return output
class SelfAttention(tf.keras.layers.Layer):
def __init__(self, num_heads, dim):
super(SelfAttention, self).__init__()
self.num_heads = num_heads
self.dim = dim
def build(self, input_shape):
self.query_weights = self.add_weight(
shape=(self.dim, self.num_heads, self.dim // self.num_heads),
initializer='random_normal',
trainable=True)
self.key_weights = self.add_weight(
shape=(self.dim, self.num_heads, self.dim // self.num_heads),
initializer='random_normal',
trainable=True)
self.value_weights = self.add_weight(
shape=(self.dim, self.num_heads, self.dim // self.num_heads),
initializer='random_normal',
trainable=True)
def call(self, inputs):
Q = tf.matmul(inputs, self.query_weights)
K = tf.matmul(inputs, self.key_weights)
V = tf.matmul(inputs, self.value_weights)
Q_split = tf.split(Q, self.num_heads, axis=2)
K_split = tf.split(K, self.num_heads, axis=2)
V_split = tf.split(V, self.num_heads, axis=2)
attention_outputs = []
for i in range(self.num_heads):
attention_outputs.append(
scaled_dot_product_attention(Q_split[i], K_split[i], V_split[i]))
attention_outputs = tf.concat(attention_outputs, axis=2)
attention_outputs = tf.reduce_sum(attention_outputs, axis=-1)
return attention_outputs
def create_self_attention_cnn(num_classes):
model = tf.keras.Sequential([
tf.keras.layers.Conv1D(32, kernel_size=3, activation='relu', padding='same', input_shape=(None, 1)),
tf.keras.layers.MaxPooling1D(pool_size=2),
tf.keras.layers.Conv1D(64, kernel_size=3, activation='relu', padding='same'),
tf.keras.layers.MaxPooling1D(pool_size=2),
tf.keras.layers.Conv1D(128, kernel_size=3, activation='relu', padding='same'),
tf.keras.layers.MaxPooling1D(pool_size=2),
SelfAttention(num_heads=8, dim=128),
tf.keras.layers.GlobalAveragePooling1D(),
tf.keras.layers.Dense(num_classes, activation='softmax')
])
return model
# 训练集和测试集的数据形状为 (样本数, 时间步长, 特征维度)
train_data = ...
train_labels = ...
test_data = ...
# 数据预处理和标准化
train_data = tf.keras.utils.normalize(train_data, axis=-1)
test_data = tf.keras.utils.normalize(test_data, axis=-1)
# 创建模型
num_classes = len(set(train_labels))
model = create_self_attention_cnn(num_classes)
# 编译模型
model.compile(optimizer='adam',
loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
metrics=['accuracy'])
# 训练模型
model.fit(train_data, train_labels, epochs=10)
# 预测测试集数据
predictions = model.predict(test_data)
需要注意的是,此示例代码中的自注意力机制是通过SelfAttention
类实现的。通过使用SelfAttention
类,可以将自注意力机制应用于卷积神经网络中。
在训练和测试模型时,需要根据具体情况对数据进行预处理和标准化,以适应模型的输入要求。另外,根据数据集的标签类型,将num_classes
设置为适当的值。
最后,可以使用训练好的模型对测试集数据的标签进行预测,并将预测结果保存在predictions
变量中。