mindspore.nn.DenseBnAct

class mindspore.nn.DenseBnAct(in_channels, out_channels, weight_init='normal', bias_init='zeros', has_bias=True, has_bn=False, momentum=0.9, eps=1e-05, activation=None, alpha=0.2, after_fake=True)[source]

A combination of Dense, Batchnorm, and the activation layer.

This part is a more detailed overview of Dense op.

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 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 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.

  • has_bn (bool) – Specifies to use batchnorm or not. Default: False.

  • momentum (float) – Momentum for moving average for batchnorm, must be [0, 1]. Default:0.9

  • eps (float) – Term added to the denominator to improve numerical stability for batchnorm, should be greater than 0. Default: 1e-5.

  • activation (Union[str, Cell, Primitive]) – Specifies activation type. The optional values are as following: ‘softmax’, ‘logsoftmax’, ‘relu’, ‘relu6’, ‘tanh’, ‘gelu’, ‘sigmoid’, ‘prelu’, ‘leakyrelu’, ‘hswish’, ‘hsigmoid’. Default: None.

  • alpha (float) – Slope of the activation function at x < 0 for LeakyReLU. Default: 0.2.

  • after_fake (bool) – Determine whether there must be a fake quantization operation after DenseBnAct. Default: True.

Inputs:

  • x (Tensor) - Tensor of shape \((N, in\_channels)\). The data type is float32.

Outputs:

Tensor of shape \((N, out\_channels)\). The data type is float32.

Raises

  • TypeError – If in_channels or out_channels is not an int.

  • TypeError – If has_bias, has_bn or after_fake is not a bool.

  • TypeError – If momentum or eps is not a float.

  • ValueError – If momentum is not in range [0, 1.0].

Supported Platforms:

Ascend GPU

Examples

>>> net = nn.DenseBnAct(3, 4)
>>> x = Tensor(np.random.randint(0, 255, [2, 3]), mindspore.float32)
>>> result = net(x)
>>> output = result.shape
>>> print(output)
(2, 4)