LeCun normal initializer. It draws samples from a truncated normal distribution centered on 0 with `stddev = sqrt(1 / fan_in)` where `fan_in` is the number of input units in the weight tensor. # Arguments t(Tensor):the tensor to be filled in. # References - [Se
(t)
| 146 | _random_fill(t, scale=2., mode='fan_in', distribution='normal') |
| 147 | |
| 148 | def lecun_normal(t): |
| 149 | """LeCun normal initializer. |
| 150 | |
| 151 | It draws samples from a truncated normal distribution centered on 0 |
| 152 | with `stddev = sqrt(1 / fan_in)` |
| 153 | where `fan_in` is the number of input units in the weight tensor. |
| 154 | |
| 155 | # Arguments |
| 156 | t(Tensor):the tensor to be filled in. |
| 157 | |
| 158 | # References |
| 159 | - [Self-Normalizing Neural Networks](https://arxiv.org/abs/1706.02515) |
| 160 | - [Efficient Backprop](http://yann.lecun.com/exdb/publis/pdf/lecun-98b.pdf) |
| 161 | """ |
| 162 | _random_fill(t, scale=1., mode='fan_in', distribution='normal') |
| 163 | |
| 164 | |
| 165 | def he_uniform(t): |
nothing calls this directly
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