labels=target, logits=output ValueError: `logits` and `labels` must have the same shape, received ((64, 1) vs (64, 137)).
时间: 2023-10-09 13:16:32 浏览: 80
这个错误表明,在模型训练的过程中,你传递的标签(labels)和模型输出的logits形状不匹配。这个错误通常出现在分类任务中,其中标签的形状应该为(batch_size, num_classes),而模型输出的形状应该为(batch_size, num_classes)或(batch_size,)。
你可以检查一下代码中的标签和模型输出是否一致。可能的原因是你的标签是多分类标签,但是你的模型最后一层的输出单元数没有设置为与标签类别数相同的值。或者你的标签是二分类标签,但是你的模型最后一层的激活函数没有设置为sigmoid函数。你可以根据具体情况来调整模型的结构或者损失函数来解决这个问题。
相关问题
ValueError: `logits` and `labels` must have the same shape, received ((64, 1) vs (64, 137)).
这个错误是由于logits和labels的形状不匹配导致的。logits是模型的输出结果,通常是一个未经softmax处理的概率分布。而labels是真实的标签,通常是一个one-hot编码的向量。在这个错误中,logits的形状是(64, 1),表示64个样本的模型输出,每个输出只有1个元素。而labels的形状是(64, 137),表示64个样本的真实标签,每个标签有137个元素。这意味着模型输出的维度和真实标签的维度不一致,导致了这个错误。
要解决这个问题,需要检查模型的输出维度和标签的维度是否一致。如果不一致,可以尝试调整模型的结构或者调整标签的格式,使它们的形状匹配。另外,还可以尝试调整损失函数的参数,使其能够处理不同形状的logits和labels。比如,可以使用tf.keras.losses.CategoricalCrossentropy()函数来处理多分类问题,该函数可以处理形状不匹配的情况,并且支持多种损失权重的设置。
ValueError: `logits` and `labels` must have the same shape, received ((None, 3) vs (None, 1)).
This error occurs when the shapes of the `logits` and `labels` tensors do not match. In this case, the `logits` tensor has shape `(None, 3)` while the `labels` tensor has shape `(None, 1)`.
To fix this error, you need to ensure that the shapes of the `logits` and `labels` tensors match. This may involve adjusting the architecture of your neural network, reshaping the input data, or adjusting the loss function.
If you are using a multi-class classification task with `n` classes, you need to ensure that the output layer of your neural network has `n` units and that the `labels` tensor is one-hot encoded with shape `(None, n)`.
If you are using a binary classification task, you need to ensure that the output layer of your neural network has a single unit and that the `labels` tensor is not one-hot encoded and has shape `(None, 1)`.
In either case, you may need to adjust the loss function to match the problem you are solving. For example, if you are solving a binary classification problem, you may want to use the binary cross-entropy loss function, while for a multi-class classification problem, you may want to use the categorical cross-entropy loss function.
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