以下是一个使用Python和PyTorch实现简单自注意力机制的完整代码示例： ```python import torch import torch.nn as nn class SelfAttention(nn.Module): def __init__(self, input_dim, hidden_dim): super(SelfAttention, self).__init__() self.W_q = nn.Linear(input_dim, hidden_dim) self.W_k = nn.Linear(input_dim, hidden_dim) self.W_v = nn.Linear(input_dim, hidden_dim) self.softmax = nn.Softmax(dim=-1) def forward(self, x): Q = self.W_q(x) K = self.W_k(x) V = self.W_v(x) attention_scores = torch.matmul(Q, K.transpose(-2, -1)) attention_probs = self.softmax(attention_scores) output = torch.matmul(attention_probs, V) return output # 示例使用 input_dim = 10 hidden_dim = 20 batch_size = 32 sequence_length = 15 x = torch.randn(batch_size, sequence_length, input_dim) attention_layer = SelfAttention(input_dim, hidden_dim) output = attention_layer(x) print(output.shape) ``` 另外，引用[3]中给出了一个结合CNN和注意力机制用于低分辨率表情识别的模型代码，以下是完整的代码： ```python from tensorflow.keras.models import Model from tensorflow.keras.layers import Input, Conv2D, BatchNormalization, ReLU from tensorflow.keras.layers import MaxPooling2D, GlobalAvgPool2D, Dense, Multiply def attention_block(input_tensor, filters): x = GlobalAvgPool2D()(input_tensor) x = Dense(filters//8, activation='relu')(x) x = Dense(filters, activation='sigmoid')(x) return Multiply()([input_tensor, x]) def build_model(input_shape=(48, 48, 1)): inputs = Input(input_shape) x = Conv2D(32, 3, padding='same')(inputs) x = BatchNormalization()(x) x = ReLU()(x) x = MaxPooling2D(2)(x) x = attention_block(x, 32) x = Conv2D(64, 3, padding='same')(x) x = BatchNormalization()(x) x = ReLU()(x) x = MaxPooling2D(2)(x) x = attention_block(x, 64) x = GlobalAvgPool2D()(x) outputs = Dense(7, activation='softmax')(x) return Model(inputs, outputs) model = build_model() model.summary() ```