# Function Differences with torch.nn.init.uniform_

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## torch.nn.init.uniform_

```text
torch.nn.init.uniform_(
    tensor,
    a=0.0,
    b=1.0
) -> Tensor
```

For more information, see [torch.nn.init.uniform_](https://pytorch.org/docs/1.8.1/nn.init.html#torch.nn.init.uniform_).

## mindspore.ops.uniform

```text
mindspore.ops.uniform(shape, minval, maxval, seed=None, dtype=mstype.float32) -> Tensor
```

For more information, see [mindspore.ops.uniform](https://www.mindspore.cn/docs/en/r2.0/api_python/ops/mindspore.ops.uniform.html).

## Differences

PyTorch: The upper and lower bounds of uniform distribution are specified by parameters `a` and `b`, i.e. U(-a, b).

MindSpore: The upper and lower bounds of uniform distribution are specified by parameters `minval` and `maxval`, i.e. U(minval, maxval),`seed` is used for random number generator to generate pseudorandom numbers.

| Categories | Subcategories |PyTorch | MindSpore | Difference |
| --- | --- | --- | --- |---|
| Parameters | Parameter 1 | tensor | shape         | The function is similar, but the input format is not.`tensor` is an n-dimensional tensor on Pytorch, but `shape` is a shape of random tensor to be generated on MindSpore platform.|
|  | Parameter 2 | a       | minval          | They have different names and similar functions, which are used to specify the minimum value of random values generated. |
|  | Parameter 3 | b       | maxval         | They have different names and similar functions, which are used to specify the maximum value of random values generated. |
|  | Parameter 4 | -       | seed          | Seed is used as entropy source for the random number engines to generate pseudo-random numbers, must be non-negative. |
|  | Parameter 5 | -       | dtype         | Specify the type of input data, and determine whether the  data generated by uniform distribution is discrete or continuous according to the data type. |

## Code Example

```python
# PyTorch
import torch
from torch import nn

w = torch.empty(3, 2)
output = nn.init.uniform_(w, a=1, b=4)
print(tuple(output.shape))
# (3, 2)

# MindSpore
import numpy as np
import mindspore
from mindspore import ops
from mindspore import Tensor

shape = (3,2)
minval = Tensor(1, mindspore.float32)
maxval = Tensor(4, mindspore.float32)
output = ops.uniform(shape, minval, maxval, dtype=mindspore.float32)
print(output.shape)
# Out：
# (3, 2)
```