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). 这些告警什么原因？

这些警告是由TensorFlow引擎生成的，它们表示在使用模型时遇到了输入形状不匹配的问题。具体地说，警告中提到的两个形状`(128, 24, 2)`和`(6, 24, 2)`分别表示模型输入的期望形状和实际传入的形状。

这个警告通常出现在模型的输入数据维度与模型定义时不一致时。在这种情况下，需要检查输入数据的形状是否与模型期望的形状匹配。

解决这个问题的方法可能包括：
1. 检查输入数据的维度和形状，确保它们与模型期望的一致。可以使用`shape`属性检查张量的形状，并根据需要进行调整。
2. 检查模型定义和数据预处理部分的代码，确保它们一致地处理数据维度。
3. 确保输入数据的批次大小（batch size）与模型定义时一致。在这个例子中，期望的批次大小是128，但实际传入的是6，因此需要相应调整。

通过解决输入形状不匹配的问题，这些警告应该会消失。

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。