mindflow.cell.ResBlock

class mindflow.cell.ResBlock(in_channels, out_channels, weight_init='normal', bias_init='zeros', has_bias=True, activation=None, weight_norm=False)

The ResBlock of dense layer.

Parameters

• in_channels (int) – The number of channels in the input space.

• out_channels (int) – The number of channels in the output space.

• weight_init (Union[Tensor, str, Initializer, numbers.Number]) – The trainable weight_init parameter. The dtype is same as input x. The values of str refer to the function initializer. Default: 'normal'.

• bias_init (Union[Tensor, str, Initializer, numbers.Number]) – The trainable bias_init parameter. The dtype is same as input x. The values of str refer to the function initializer. Default: 'zeros'.

• has_bias (bool) – Specifies whether the layer uses a bias vector. Default: True.

• activation (Union[str, Cell, Primitive, None]) – activate function applied to the output of the dense layer. Default: None.

• weight_norm (bool) – Whether to compute the sum of squares of weight. Default: False.

Inputs:

• input (Tensor) - Tensor of shape \((*, in\_channels)\).

Outputs:

Tensor of shape \((*, out\_channels)\).

Raises

• ValueError – If in_channels not equal out_channels.

• TypeError – If activation is not in str or Cell or Primitive.

Supported Platforms:

Ascend GPU

Examples

>>> import numpy as np
>>> from mindflow.cell import ResBlock
>>> from mindspore import Tensor
>>> input = Tensor(np.array([[180, 234, 154], [244, 48, 247]], np.float32))
>>> net = ResBlock(3, 3)
>>> output = net(input)
>>> print(output.shape)
(2, 3)