# Initialize padded_sequences with the pad_valuepadded_sequences = torch.full((batch_size, max_len, feature_size), fill_value=pad_value, dtype=sequences.dtype, device=sequences.device)# Pad each sequence to the max_lenpadded_sequences[:...
0] = self.W[i, j]num_edges += 1edge_index = edge_index[:, :num_edges]edge_attr = edge_attr[:num_edges]return edge_index, edge_attrdef _create_sequences(self, data, n_node, n_window, edge_index, edge_attr):sequences...
The intermediate representation is the container for the operations that were recorded during symbolic tracing. It consists of a list of Nodes that represent function inputs, callsites (to functions, methods, or torch.nn.Module instances), and return values. More information about the IR can be ...
#模型体系print(model)def count_parameters(model): return sum(p.numel() for p in model.parameters() if p.requires_grad) print(f'The model has {count_parameters(model):,} trainable parameters')#初始化预训练embeddingpretrained_embeddings = TEXT.vocab.vectorsmodel.embedding.weight.data.copy...
其中 是当前卷积层的输出, 是上一个卷积层的输出,作为当前卷积层的输入, 是 节点相邻节点的信息, 是其连接边的信息(建议背下来这个公式,你会发现无论空域图卷积的论文怎么折腾,还是没跑出这个框架,只不过是 两个函数换了)。 对于以上计算过程,PyG利用MessagePassing进行实现。接下来以两篇经典图神经网络论文为例...
add(SimpleRNN(16, input_shape=(num_words, maxlen), return_sequences=False, activation="relu")) # SimpleRNN层有16个神经元,input_shape指定输入数据的形状,return_sequences=False表示只返回输出序列的最后一个输出 # activation="relu"指定激活函数为ReLU # 添加一个全连接层 rnn.add(Dense(1)) # ...
MAX_LENGTH = 10 eng_prefixes = ( "i am ", "i m ", "he is", "he s ", "she is", "she s", "you are", "you re ", "we are", "we re ", "they are", "they re " ) def filterPair(p): return len(p[0].split(' ')) < MAX_LENGTH and \ len(p[1].split(' ')...
returnpadded_sequences 训练过程还是传统的pytorch过程: deftrain(model, tokenizer, data_loader, optimizer, criterion, device,max_grad_norm=1.0,DEBUGGING_IS_ON=False): model.train()total_loss=0 forbatchindata_loader: optimizer.zero_grad()input_data=batch['input_ids'].clone().to(device)attention...
returnpadded_sequences 训练过程还是传统的pytorch过程: deftrain(model, tokenizer, data_loader, optimizer, criterion, device, max_grad_norm=1.0, DEBUGGING_IS_ON=False): model.train() total_loss=0 forbatchindata_loader: optimizer.zero_grad() ...
device=X.device)[None, :] < valid_len[:,None]# 优雅,比较 arange 生成张量(即列号序列)的列和 valid_len 的行X[~mask] = value# 按位反转 仅有效位赋值returnX X = torch.tensor([[1,2,3], [4,5,6]]) sequence_mask(X, torch.tensor([1,2])) ...