mindflow.cell.FCSequential

class mindflow.cell.FCSequential(in_channels, out_channels, layers, neurons, residual=True, act='sin', weight_init='normal', has_bias=True, bias_init='default', weight_norm=False)[source]

A sequential container of the dense layers, dense layers are added to the container sequentially.

Parameters

in_channels (int) – The number of channels in the input space.
out_channels (int) – The number of channels in the output space.
layers (int) – The total number of layers, include input/hidden/output layers.
neurons (int) – The number of neurons of hidden layers.
residual (bool) – full-connected of residual block for the hidden layers. Default: True.
act (Union[str, Cell, Primitive, None]) – activate function applied to the output of the fully connected layer, eg. 'ReLU'.Default: "sin".
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'.
has_bias (bool) – Specifies whether the layer uses a bias vector. Default: True.
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: 'default'.
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

TypeError – If layers is not an int.
TypeError – If neurons is not an int.
TypeError – If residual is not a bool.
ValueError – If layers is less than 3.

Supported Platforms:: Ascend GPU

Examples

>>> import numpy as np
>>> from mindflow.cell import FCSequential
>>> from mindspore import Tensor
>>> inputs = np.ones((16, 3))
>>> inputs = Tensor(inputs.astype(np.float32))
>>> net = FCSequential(3, 3, 5, 32, weight_init="ones", bias_init="zeros")
>>> output = net(inputs).asnumpy()
>>> print(output.shape)
(16, 3)