content/zh/_sources/docs/initializer.md.txt

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# 初始化器(Initializer)

## Python API

普遍使用的参数初始化方法（tensor对象）。

示例用法：

```python
from singa import tensor
from singa import initializer

x = tensor.Tensor((3, 5))
initializer.uniform(x, 3, 5) # use both fan_in and fan_out
initializer.uniform(x, 3, 0)  # use only fan_in
```
---

#### singa.initializer.uniform(t, fan_in=0, fan_out=0)

按照指定均匀分布对输入tensor初始化。

**参数：**
- **fan_in (int)** – 对于卷积层权重tensor，fan_in = nb_channel * kh * kw;对于全连接层，fan_in = input_feature_length
- **fan_out (int)** – 对于卷积层权重tensor，fan_out = nb_filter * kh * kw;对于全连接层，fan_out = output_feature_length

**参考文献** [Bengio and Glorot 2010]： Understanding the difficulty of training deep feedforward neuralnetworks.

---

#### singa.initializer.gaussian(t, fan_in=0, fan_out=0)

按照指定高斯分布对输入tensor初始化。

**参数：**
- **fan_in (int)** – 对于卷积层权重tensor，fan_in = nb_channel * kh * kw;对于全连接层，fan_in = input_feature_length
- **fan_out (int)** – 对于卷积层权重tensor，fan_out = nb_filter * kh * kw;对于全连接层，fan_out = output_feature_length

**参考文献** Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun: Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification

---