accelbrainbase._mxnet package

Submodules

accelbrainbase._mxnet.global_avg_pool_2d module

class accelbrainbase._mxnet.global_avg_pool_2d.GlobalAvgPool2D(pool_size=(1, 1), layout='NCHW', **kwargs)

Bases: mxnet.gluon.nn.conv_layers._Pooling

Global average pooling operation for spatial data.

Parameters:
  • pool_size (tuple, default (1, 1)) –
  • layout (str, default 'NCHW') – Dimension ordering of data and out (‘NCHW’ or ‘NHWC’). ‘N’, ‘C’, ‘H’, ‘W’ stands for batch, channel, height, and width dimensions respectively.
Inputs:
  • data: 4D input tensor with shape
    (batch_size, in_channels, height, width) when layout is NCHW. For other layouts shape is permuted accordingly.
Outputs:
  • out: 4D output tensor with shape
    (batch_size, channels, 1, 1) when layout is NCHW.

accelbrainbase._mxnet.initializable_params module

class accelbrainbase._mxnet.initializable_params.InitializableParams(**kwargs)

Bases: mxnet.initializer.Initializer

The interface to Initializes weights.

accelbrainbase._mxnet.relu_n module

class accelbrainbase._mxnet.relu_n.ReLuN(min_n=0, max_n=6, **kwargs)

Bases: mxnet.gluon.block.HybridBlock

ReLu N(=6) layer.

forward_propagation(F, x)

Hybrid forward with Gluon API.

Parameters:
  • F – mxnet.ndarray or mxnet.symbol.
  • x – mxnet.ndarray of observed data points.
Returns:

mxnet.ndarray or mxnet.symbol of inferenced feature points.
hybrid_forward(F, x)

Hybrid forward with Gluon API.

Parameters:
  • F – mxnet.ndarray or mxnet.symbol.
  • x – mxnet.ndarray of observed data points.
Returns:

mxnet.ndarray or mxnet.symbol of inferenced feature points.
inference(observed_arr)

Inference the labels.

Parameters:observed_arr – rank-2 Array like or sparse matrix as the observed data points. The shape is: (batch size, feature points)
Returns:mxnet.ndarray of inferenced feature points.

Module contents