WARNING:tensorflow:Model was constructed with shape (128, 24, 2) for input KerasTensor(type_spec=TensorSpec(shape=(128, 24, 2), dtype=tf.float32, name='RealData'), name='RealData', description="created by layer 'RealData'"), but it was called on an input with incompatible shape (6, 24, 2). WARNING:tensorflow:Model was constructed with shape (128, 24, 2) for input KerasTensor(type_spec=TensorSpec(shape=(128, 24, 2), dtype=tf.float32, name='RealData'), name='RealData', description="created by layer 'RealData'"), but it was called on an input with incompatible shape (6, 24, 2).

_dim = hidden_dim // num_heads

self.query_linear = nn.Linear(hidden_dim, hidden_dim)
self.key_linear = nn.Linear(hidden_dim, hidden_dim)
self.value_linear = nn.Linear(hidden_dim, hidden_dim)
self.out_linear = nn.Linear(hidden_dim, hidden这些警告表明模型在使用时遇到了输入形状不匹配的问题。警告中提到的_dim)

def forward(self, query, key, value):
batch_size = query.size(0)

query = self.query_linear(query两个形状`(128, 24, 2)`和`(6, 24, 2)`分别表示模型定义)
key = self.key_linear(key)
value = self.value_linear(value)

query = query.view(batch_size, -1, self时期望的输入形状和实际传入的输入形状。

要解决这个问题，需要确保输入数据.num_heads, self.head_dim).transpose(1, 2)
key = key.view(batch_size, -1, self.num_heads,的形状与模型定义时期望的形状一致。在这个例子中，期望的形状是 self.head_dim).transpose(1, 2)
value = value.view(batch_size, -1, self.num_heads, self.head_dim`(128, 24, 2)`，但实际传入的形状是`(6, 24, 2)`，).transpose(1, 2)

scores = torch.matmul(query, key.transpose(-2, -1)) / torch.sqrt(torch.tensor(self.head_dim))
attention_weights = nn.functional.softmax(scores, dim=-1)

attended_values = torch.matmul(attention_weights, value)
所以它们不匹配。

可能的解决方案包括：
1. 检查数据预处理部分的代码， attended_values = attended_values.transpose(1, 2).contiguous().view(batch_size, -1, self.num_heads * self.head确保输入数据被正确地转换为期望的形状。
2. 检查数据加载部分的代码，确保_dim)

output = self.out_linear(attended_values)
return output

# 定义前馈神经网络模块
class Feed正确加载具有期望形状的数据。
3. 检查模型定义部分的代码，确保模型的输入Forward(nn.Module):
def __init__(self, hidden_dim):
super(FeedForward, self).__init__()
self.linear1 =层与期望的形状一致。

通过解决输入形状不匹配的问题，警告应该会消失 nn.Linear(hidden_dim, hidden_dim * 4)
self.linear2 = nn.Linear(hidden_dim * 4, hidden_dim)

def。如果问题仍然存在，请提供更多代码和详细信息，以便更好地帮助你解决问题。