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mindspore.mint.nn.BatchNorm1d

class mindspore.mint.nn.BatchNorm1d(num_features: int, eps=1e-5, momentum=0.1, affine=True, track_running_stats=True, dtype=None)

Applies Batch Normalization over a 2D or 3D input as described in the paper Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift .

$$y=\frac{x-mean}{\sqrt{variance+ϵ}}\ast \gamma +\beta$$

The mean and standard-deviation are calculated per-dimension over the mini-batches and $\gamma$ and $\beta$ are learnable parameter vectors of size C (where C is the number of features or channels of the input). By default, the elements of $\gamma$ are set to 1 and the elements of $\beta$ are set to 0.

Warning

This API does not support Dynamic Rank. This is an experimental API that is subject to change or deletion.

Parameters

• num_features (int) – C from an expected input of shape $(N,C,L)$.

• eps (float, optional) – a value added to the denominator for numerical stability. Default: 1e-5 .

• momentum (float, optional) – the value used for the running_mean and running_var computation. Can be set to None for cumulative moving average. Default: 0.1 .

• affine (bool, optional) – a boolean value that when set to True, this cell has learnable affine parameters. Default: True .

• track_running_stats (bool, optional) – a boolean value that when set to True, this cell tracks the running mean and variance, and when set to False, this cell does not track such statistics. And this cell always uses batch statistics in both training and eval modes. Default: True .

• dtype (mindspore.dtype, optional) – Dtype of Parameters. Default: None .

Inputs:

• input (Tensor) - The input with shape $(N,C)$ or $(N,C,L)$, where $N$ means batch, $C$ means the number of feature or the number of channel, and $L$ is the length of sequence.

Outputs:

Tensor, has the same type and shape as input.

Raises

• TypeError – If num_features is not an int number.

• TypeError – If eps is not a float.

• ValueError – If num_features is less than 1.

Supported Platforms:

Ascend

Examples

>>> import mindspore
>>> from mindspore import Tensor, mint
>>> input_x = mindspore.Tensor([[0.7, 0.5, 0.5, 0.6], [0.5, 0.4, 0.6, 0.9]])
>>> net = mint.nn.BatchNorm1d(4)
>>> output = net(input_x)
>>> print(output)
[[ 0.99950075 0.9980011 -0.9980068 -0.9997783]
 [-0.9995012 -0.99799967 0.9980068  0.9997778]]