# 报错:the dimension of logits must be equal to 2

**1. 报错描述**

1.1 系统环境

```
Hardware Environment(Ascend/GPU/CPU): Ascend Software Environment: -- MindSpore version (source or binary): 1.6.0 -- Python version (e.g., Python 3.7.5): 3.7.6 -- OS platform and distribution (e.g., Linux Ubuntu 16.04): Ubuntu 4.15.0-74-generic -- GCC/Compiler version (if compiled from source):
```

1.2 基本信息

1.2.1 脚本

训练脚本是通过构建SoftmaxCrossEntropyWithLogits的单算子网络，计算两个变量softmax 交叉熵的例子。脚本如下：

```
 01 class Net(nn.Cell):
 02   def __init__(self):
 03     super(Net, self).__init__()
 04     self.loss = nn.SoftmaxCrossEntropyWithLogits(sparse=False)
 05
 06   def construct(self, logits, labels):
 07     output = self.loss(logits, labels)
 08     return output
 09
 10 net = Net()
 11 logits = Tensor(np.array([[[2, 4, 1, 4, 5], [2, 1, 2, 4, 3]]]), mindspore.float32)
 12 labels = Tensor(np.array([[[0, 0, 0, 0, 1], [0, 0, 0, 1, 0]]]).astype(np.float32))
 13 out = net(logits, labels)
 14 print('out',out)
```

1.2.2 报错

这里报错信息如下：

```
Traceback (most recent call last):
 File 'demo.py', line 13, in &lt;module&gt;
​    out = net(logits, labels)
…
ValueError: mindspore/core/utils/check_convert_utils.cc:395 CheckInteger] For primitive[SoftmaxCrossEntropyWithLogits], the dimension of logits must be equal to 2, but got 3.
The function call stack (See file ' rank_0/om/analyze_fail.dat' for more details):
\# 0 In file demo.py(04)
​    output = self.loss(logits, labels)
```

原因分析

我们看报错信息，在ValueError中，写到For primitive\[SoftmaxCrossEntropyWithLogits\], the dimension of logits must be equal to 2, but got 3，意思是传的logits应该等于2维，但是你传进去的logits的shape却是3维，查看官网对logits的描述可知，支持的shape为\(N,C\)。

![](figures/7-56.png)

​对于3维数据，建议先reshape成2维\(N\*L, C\)，然后再调用nn.SoftmaxCrossEntropyWithLogits接口，执行完后再reshape回 \(N, 1\)。

**2. 解决方法**

基于上面已知的原因，很容易做出如下修改：

```
  1 class Net(nn.Cell):
  2     def __init__(self):
  3         super(Net, self).__init__()
  4         self.loss = nn.SoftmaxCrossEntropyWithLogits(sparse=False)
  5
  6     def construct(self, logits, labels):
  7         output = self.loss(logits, labels)
  8         return output
  9
 10 net = Net()
 11 logits = Tensor(np.array([[[2, 4, 1, 4, 5], [2, 1, 2, 4, 3]]]), mindspore.float32)
 12 labels = Tensor(np.array([[[0, 0, 0, 0, 1], [0, 0, 0, 1, 0]]]).astype(np.float32))
 13 L, N, C = logits.shape
 14 logits,labels = logits.reshape(L*N, C),labels.reshape(L*N, C)
 15 out = net(logits, labels)
 16 out = out.reshape(N,1)
 17 print('out',out)
```

此时执行成功，输出如下：

```
out [[0.5899297 ]
[0.52374405]]
```

**3. 总结**

定位报错问题的步骤：

1、找到报错的用户代码行：output = self.loss\(logits, labels\);

2、 根据日志报错信息中的关键字，缩小分析问题的范围the dimension of logits must be equal to 2, but got ;

3、需要重点关注变量定义、初始化的正确性。

**4. 参考文档**

4.1  [SoftmaxCrossEntropyWithLogits算子API接口](https://www.mindspore.cn/docs/api/zh-CN/r1.5/api_python/nn/mindspore.nn.SoftmaxCrossEntropyWithLogits.html?highlight=softmaxcrossentropywithlogits#mindspore.nn.SoftmaxCrossEntropyWithLogits)

