WARNING:tensorflow:Model was constructed with shape (128, 24, 2) for input Tensor("RealData:0", shape=(128, 24, 2), dtype=float32), 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 Tensor("RealData:0", shape=(128, 24, 2), dtype=float32), 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 Tensor("RealData:0", shape=(128, 24, 2), dtype=float32), 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 Tensor("RealData:0", shape=(128, 24, 2), dtype=float32), but it was called on an input with incompatible shape (6, 24, 2). 上面告警什么意思

这些警告来自TensorFlow框架，它们提供了一些关于模型输入数据形状不匹配的提示信息。让我们逐个解释这些警告的含义：

1. "Model was constructed with shape (128, 24, 2) for input Tensor("RealData:0", shape=(128, 24, 2), dtype=float32), but it was called on an input with incompatible shape (6, 24, 2)."

这个警告指出模型在构建时使用了输入张量的形状（shape）为(128, 24, 2)，即(批量大小, 时间步数, 特征数)。然而，模型在调用阶段却接收到了一个形状不兼容的输入张量，其形状为(6, 24, 2)。

这意味着您的代码在使用此模型进行推断或训练时提供了一个形状不匹配的输入数据。模型期望的批量大小是128，但您提供的数据批量大小是6，因此形状不兼容。

解决此警告的方法是确保在使用模型进行推断或训练时，提供与模型定义一致的输入数据形状。

这些警告可能是由于模型定义和输入数据之间的不一致引起的。请检查您的代码，确保提供与模型期望的输入形状相匹配的数据。

如果您需要更具体的帮助，请提供更多关于您的模型和代码的信息。

相关问题

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。

WARNING:tensorflow:Model was constructed with shape (None, 100, 1) for input KerasTensor(type_spec=TensorSpec(shape=(None, 100, 1), dtype=tf.float32, name='input_1'), name='input_1', description="created by layer 'input_1'"), but it was called on an input with incompatible shape (None, 88, 1). WARNING:tensorflow:Model was constructed with shape (None, 100, 1) for input KerasTensor(type_spec=TensorSpec(shape=(None, 100, 1), dtype=tf.float32, name='input_2'), name='input_2', description="created by layer 'input_2'"), but it was called on an input with incompatible shape (None, 137, 1).

这是一个 TensorFlow 的警告信息，提示模型的输入与实际输入的维度不匹配。根据警告信息，模型的输入应该是一个形状为 (None, 100, 1) 的张量，但是实际输入的张量形状不一致，分别是 (None, 88, 1) 和 (None, 137, 1)。这可能是由于模型的输入设置不正确或者实际输入数据的维度不符合要求导致的。如果想要解决这个问题，需要检查模型的输入设置以及实际输入数据的维度是否匹配。