<li><strong>input</strong> (<em>Variable|list</em>) – a 2-D tensor with shape [N x D], where N is the
batch size and D is the number of classes. This input is a probability
computed by the previous operator, which is almost always the result
of a softmax operator.</li>
<li><strong>label</strong> (<em>Variable|list</em>) – the ground truth which is a 2-D tensor. When
<cite>soft_label</cite> is set to <cite>False</cite>, <cite>label</cite> is a tensor<int64> with shape
[N x 1]. When <cite>soft_label</cite> is set to <cite>True</cite>, <cite>label</cite> is a
tensor<float/double> with shape [N x D].</li>
<li><strong>soft_label</strong> (bool, via <cite>**kwargs</cite>) – a flag indicating whether to interpretate
the given labels as soft labels, default <cite>False</cite>.</li>
</ul>
</td>
</tr>
<trclass="field-even field"><thclass="field-name">Returns:</th><tdclass="field-body"><pclass="first">A 2-D tensor with shape [N x 1], the cross entropy loss.</p>
</td>
</tr>
<trclass="field-odd field"><thclass="field-name">Raises:</th><tdclass="field-body"><pclass="first last"><cite>ValueError</cite>– 1) the 1st dimension of <cite>input</cite> and <cite>label</cite> are not equal; 2) when <cite>soft_label == True</cite>, and the 2nd dimension of <cite>input</cite> and <cite>label</cite> are not equal; 3) when <cite>soft_label == False</cite>, and the 2nd dimension of <cite>label</cite> is not 1.</p>
"comment":"(Tensor, default Tensor<float>), a 2-D tensor with shape N x D, where N is the batch size and D is the number of classes. This input is a probability computed by the previous operator, which is almost always the result of a softmax operator.",
"comment":"(Tensor, default Tensor<float>), a 2-D tensor with shape [N x D], where N is the batch size and D is the number of classes. This input is a probability computed by the previous operator, which is almost always the result of a softmax operator.",
"duplicable":0,
"duplicable":0,
"intermediate":0
"intermediate":0
},{
},{
"name":"Label",
"name":"Label",
"comment":"(Tensor), the ground truth which is a 2-D tensor. When soft_label is set to false, Label is a Tensor<int64> with shape [N x 1]. When soft_label is set to true, Label is a Tensor<float/double> with shape [N x K].",
"comment":"(Tensor), the ground truth which is a 2-D tensor. When soft_label is set to false, Label is a Tensor<int64> with shape [N x 1]. When soft_label is set to true, Label is a Tensor<float/double> with shape [N x D].",
<li><strong>input</strong> (<em>Variable|list</em>) – a 2-D tensor with shape [N x D], where N is the
batch size and D is the number of classes. This input is a probability
computed by the previous operator, which is almost always the result
of a softmax operator.</li>
<li><strong>label</strong> (<em>Variable|list</em>) – the ground truth which is a 2-D tensor. When
<cite>soft_label</cite> is set to <cite>False</cite>, <cite>label</cite> is a tensor<int64> with shape
[N x 1]. When <cite>soft_label</cite> is set to <cite>True</cite>, <cite>label</cite> is a
tensor<float/double> with shape [N x D].</li>
<li><strong>soft_label</strong> (bool, via <cite>**kwargs</cite>) – a flag indicating whether to interpretate
the given labels as soft labels, default <cite>False</cite>.</li>
</ul>
</td>
</tr>
<trclass="field-even field"><thclass="field-name">返回:</th><tdclass="field-body"><pclass="first">A 2-D tensor with shape [N x 1], the cross entropy loss.</p>
</td>
</tr>
<trclass="field-odd field"><thclass="field-name">Raises:</th><tdclass="field-body"><pclass="first last"><cite>ValueError</cite>– 1) the 1st dimension of <cite>input</cite> and <cite>label</cite> are not equal; 2) when <cite>soft_label == True</cite>, and the 2nd dimension of <cite>input</cite> and <cite>label</cite> are not equal; 3) when <cite>soft_label == False</cite>, and the 2nd dimension of <cite>label</cite> is not 1.</p>
