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python - 使用 sparse_categorical_crossentropy 时如何修复形状不匹配错误

问题描述

我想知道为什么在输入两个具有相同形状和相同内容的张量时，我在调用 sparse_categorical_crossentropy 损失函数时遇到了错误。我有以下代码：

labels = np.array([0,1,2])
logits = np.array([0,1,2])
tf.keras.losses.sparse_categorical_crossentropy(labels, logits, from_logits=True)

我收到以下错误：

/tensorflow-2.1.0/python3.6/tensorflow_core/python/keras/losses.py in sparse_categorical_crossentropy(y_true, y_pred, from_logits, axis)
    976 def sparse_categorical_crossentropy(y_true, y_pred, from_logits=False, axis=-1):
    977   return K.sparse_categorical_crossentropy(
--> 978       y_true, y_pred, from_logits=from_logits, axis=axis)
    979 
    980 

/tensorflow-2.1.0/python3.6/tensorflow_core/python/keras/backend.py in sparse_categorical_crossentropy(target, output, from_logits, axis)
   4574   else:
   4575     res = nn.sparse_softmax_cross_entropy_with_logits_v2(
-> 4576         labels=target, logits=output)
   4577 
   4578   if update_shape and output_rank >= 3:

/tensorflow-2.1.0/python3.6/tensorflow_core/python/ops/nn_ops.py in sparse_softmax_cross_entropy_with_logits_v2(labels, logits, name)
   3535   """
   3536   return sparse_softmax_cross_entropy_with_logits(
-> 3537       labels=labels, logits=logits, name=name)
   3538 
   3539 

/tensorflow-2.1.0/python3.6/tensorflow_core/python/ops/nn_ops.py in sparse_softmax_cross_entropy_with_logits(_sentinel, labels, logits, name)
   3451                        "should equal the shape of logits except for the last "
   3452                        "dimension (received %s)." % (labels_static_shape,
-> 3453                                                      logits.get_shape()))
   3454     # Check if no reshapes are required.
   3455     if logits.get_shape().ndims == 2:

ValueError: Shape mismatch: The shape of labels (received (3,)) should equal the shape of logits except for the last dimension (received (1, 3)).

标签： pythontensorflowkeras

解决方案

labels = tf.convert_to_tensor(np.asarray([0,1,2]))
logits = tf.convert_to_tensor(np.asarray([[1,0,0],[0,1,0],[0,0,1]]), dtype=tf.float32)
tf.keras.losses.sparse_categorical_crossentropy(labels, logits)
<tf.Tensor: shape=(3,), dtype=float32, numpy=array([2.3841855e-07, 2.3841855e-07, 2.3841855e-07], dtype=float32)>

这sparse意味着您提供的整数labels实际上代表类标签，这意味着sparse_categorical_crossentropy_loss会将它们转换为一种热编码（如 logits）。不知道from_logits代表什么，对不起。

注意：您必须这样做tf.convert_to_tensor是为了避免tensorflow.

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