Fixing 'logits and labels' dimension mismatch in Tensorflow

  1. Understanding the Issue
  2. Fixing the Issue
  3. Conclusion

Understanding the Issue

When working with Tensorflow, it is common to encounter the error message "logits and labels must have the same first dimension". This error occurs when the dimensions of the logits and labels tensors do not match. In other words, the number of predictions made by the model (the first dimension of logits) does not match the number of true labels (the first dimension of labels).

Fixing the Issue

There are several ways to fix the "logits and labels" dimension mismatch in Tensorflow. One common solution is to use the tf.reshape() function to reshape the tensors so that they have the same first dimension. For example, if the logits tensor has shape [batch_size, num_classes] and the labels tensor has shape [batch_size], we can reshape the labels tensor to have shape [batch_size, 1] using the following code:

labels = tf.reshape(labels, [-1, 1])

Another solution is to use the tf.nn.sparse_softmax_cross_entropy_with_logits() function, which allows us to pass in the labels tensor as a one-dimensional array of integers instead of a one-hot encoded matrix. This function automatically performs the necessary reshaping of the tensors to ensure that they have the same first dimension.


In summary, the "logits and labels" dimension mismatch error is a common issue in Tensorflow, but it can be easily fixed by reshaping the tensors or using the tf.nn.sparse_softmax_cross_entropy_with_logits() function. By understanding the issue and implementing one of these solutions, you can ensure that your Tensorflow models are working correctly and efficiently.

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